Muse Ember vs Claude Haiku 5.5 vs GPT-6 Luna: Cost per Task

Three small models, one price sheet, and a threefold difference in what an agent task actually costs. Here is where the money goes, which model wins which job, and how to measure it on your own work.

On November 9, 2026, Meta released Muse Ember at $0.10 per million input tokens and $0.50 per million output tokens - Meta. That is exactly what Claude Haiku 5.5 and GPT-6 Luna charge for normal-length prompts. Three labs, three volume models, one identical rate card. And yet when Artificial Analysis ran all three through the same nine-benchmark suite at maximum reasoning effort, Haiku 5.5 cost $0.41 per task, Luna $0.15 and Ember $0.14 - Artificial Analysis.

That gap is the whole story of this guide. When the price per token is the same, the bill is decided by things the price sheet does not show: how many tokens a model burns thinking, how often an agent re-reads its own context, what the provider charges for a cache hit, where a long-prompt surcharge kicks in, and how often the task fails and has to be done again. Muse Ember wins most of those contests, but not all of them, and there is a fourth model at ten times the token price that beats two of the three on cost per task. If you run agents at volume, the difference between picking well and picking by price sheet is a multiple of your inference bill.

What follows is built on primary data: the labs' own pricing pages and documentation, Meta's evaluation report, and the raw per-task cost, token and score records that Artificial Analysis publishes for every model and effort setting. Where we computed something ourselves (cost per successful task, break-even points, cost estimates inside coding harnesses), we say so and show the arithmetic.

This guide reflects prices, documentation and benchmark results as of 12 November 2026. Prices in this market change monthly and benchmark indexes are re-versioned, so verify current figures before committing a budget.

Contents

1. The Short Answer: Same Price, Three Different Bills
2. Why Identical Token Prices Produce Different Costs per Task
3. Muse Ember: Meta's Volume Model, Explained
4. Claude Haiku 5.5: Anthropic's Effort-Controlled Small Model
5. GPT-6 Luna: OpenAI's Frugal Worker
6. Benchmark by Benchmark: Where Each Model Wins
7. Cost per Successful Task: The Number That Actually Matters
8. Effort Settings: The Hidden Multiplier on Every Bill
9. The Long-Context Cliff: 100K, 200K and 272K Tokens
10. Speed and Throughput: Minutes per Task
11. Coding Agents: What a Task Costs Inside Real Harnesses
12. The Fourth Option: When a Pricier Model Is Cheaper per Task
13. Scored Comparison: Which Model for Volume Agent Work
14. Routing: Pairing a Planner With a Cheap Executor
15. Hidden Costs and Failure Modes
16. How to Measure Cost per Task on Your Own Workload
17. Market Context and What Could Change the Verdict
18. Verdict: Which One to Pick

1. The Short Answer: Same Price, Three Different Bills

If you only read one section, read this one. The three models share a list price but differ on almost everything that decides the bill, and the table below puts the facts that matter side by side.

Prices from Meta, Anthropic and OpenAI. Scores, costs and times from Artificial Analysis model pages for Muse Ember, Claude Haiku 5.5 and GPT-6 Luna; workflow completion from AutomationBench-AA.

Read that table as three different trade-offs rather than one winner. Muse Ember is the cheapest and fastest way to buy Haiku-level general intelligence, and it is far ahead on multi-step business workflows. Claude Haiku 5.5 reaches the same overall score but spends roughly three times as much per task getting there, and it earns that premium only on a narrow set of jobs: polished knowledge-work deliverables, mergeable code fixes and factual recall. GPT-6 Luna is the most frugal with tokens on short, single-shot reasoning, which makes it the cheapest per correct answer on that kind of work, but it falls behind badly once a task turns into a long agent loop.

The rest of this guide explains why each of those statements is true, where each one stops being true, and what you can do about it in your own deployment.

2. Why Identical Token Prices Produce Different Costs per Task

Start with what you actually buy. Nobody running an agent wants tokens; they want a ticket classified, a workflow completed, a bug fixed, a contract reviewed. Tokens are an input, and the price per token is only one of the factors that turn inputs into a cost per outcome. Written out, the cost of one agent task looks like this:

Cost per task = (tokens re-read from cache × cache price) + (new input tokens × input price) + (tokens written to cache × cache-write price) + (reasoning tokens + answer tokens) × output price

Two of those terms are invisible on a headline rate card, and they turn out to be the largest. The reason is the shape of an agent loop. Every turn, an agent sends the model its entire working context again: the system prompt, the tool definitions, the conversation so far, every tool result it has collected. Prompt caching means most of that context is billed at the cache-hit rate rather than the full input rate, but it is still billed, on every single turn. A task that runs for a hundred turns over a context that grows to 150,000 tokens re-reads millions of tokens before it finishes.

Artificial Analysis publishes exactly this breakdown for every model it tests: each evaluation's cost is calculated from input, cache hit, cache write, reasoning and answer token prices, divided by the number of tasks and weighted by the evaluation's index weight - Artificial Analysis. Its methodology also notes that the cost combines each provider's reported token counts with live measurements of the model's typical cache hit rate - Artificial Analysis methodology. Pulling those records apart for our three models shows where the money goes.

For all three models, cache reads are the single largest line: 49% of Ember's cost per task, 57% of Haiku's and 59% of Luna's in the Artificial Analysis records. Output, the line everyone looks at when comparing price sheets, is only a quarter to a third of the bill. Artificial Analysis says the same thing in its launch analysis: Ember's cached input, "most of the cost in our agentic evaluations", is 60% cheaper than its rivals', and it uses roughly a third as many output tokens as Haiku 5.5 - Artificial Analysis.

2.1 Decomposing Ember's advantage over Haiku

Because the records give both the dollar amounts and the prices, the gap between Haiku 5.5 ($0.411) and Ember ($0.136) can be split into its causes. Dividing each model's cache-read cost by its cache price gives the cached tokens it reads per task: about 23.3 million for Haiku against 16.6 million for Ember. Haiku reads 40% more context per task and pays 2.5 times as much for each cached token.

Of the $0.275 gap per task, our arithmetic on those records attributes:

• 36% to cache price: if Ember's 16.6 million cached tokens were billed at Haiku's $0.01 instead of $0.004, its cost would rise by about $0.10.
• 24% to re-read volume: the extra 6.6 million cached tokens it consumes per task.
• 26% to output volume: about 226,000 output tokens per task (173,000 of them reasoning) against Ember's 84,000.
• 14% to cache writes, again driven by Haiku putting more new material into its context.

So roughly a third of Ember's advantage is a pricing decision Meta made, and two thirds is the model doing the same work with fewer tokens. That distinction matters for the future: a price can be matched by a competitor next week, token efficiency cannot. If Anthropic cut Haiku 5.5's cache-read price to Ember's $0.004 tomorrow, the same records put Haiku at about $0.27 per task, still twice Ember's cost.

2.2 Against Luna, the cache price is the whole difference

The comparison with Luna runs the other way. Luna reads only about 9 million cached tokens per task, roughly half of Ember's, and produces a similar amount of output (75,000 tokens against 84,000). On tokens alone, Luna is the more frugal model. Ember only comes out cheaper because Meta charges $0.004 for a cache hit rather than $0.01: re-priced at Luna's cache rate, Ember would cost about $0.24 per task, more than Luna's $0.152.

That does not make Luna the better buy, because the score attached to those tokens is very different (27.9 against 36.0 on the index). But it does tell you something practical about where each model's cost comes from. Luna is cheap because it thinks briefly and reads little; Ember is cheap because it is efficient and its provider prices the most-used meter aggressively. Our deeper guide on prompt caching covers how to raise your own cache hit rate, which moves this line more than any model swap.

3. Muse Ember: Meta's Volume Model, Explained

Muse Ember is the smallest model in Meta's Muse family, and Meta positions it explicitly as the execution layer beneath its bigger models: "Muse Wonder for the hardest problems, Muse Spark for coding and agentic workflows, and Muse Ember for repeatable coding and agentic tasks at scale" - Meta. It is also the first model Meta says it trained using Muse Wonder Full, the higher-capability variant of Muse Wonder, which suggests Ember is at least partly a large model's behavior compressed into a cheap one.

Fahd Mirza's launch-day test is worth watching for one reason the benchmarks cannot show: what the model does with messy real inputs. The section starting at 3:42 hands Ember a disorganized spreadsheet and asks for a clean Excel report, exactly the kind of bounded, repetitive document work Meta designed it for, and the later tests push it on diagrams and circuit reasoning, where a small model's limits show sooner.

3.1 Pricing, tiers and the default that changes everything

Ember is billed at $0.10 input, $0.004 cached input and $0.50 output per million tokens for requests up to 200,000 input tokens. A request over that line is billed at $0.30 / $0.012 / $1.50 in full, not only for the tokens past 200K, and cached tokens count toward the threshold - Meta Model API pricing. The same page states that Contributor mode, Meta's discounted tier that trains on your content, is not available for Ember, so every Ember request runs in Standard mode, where Meta says your content is not used for training.

The setting that most changes a real bill is the default effort. Ember's reasoning effort runs from "minimal" to "max", it cannot be switched off entirely ("none" returns an HTTP 400 error), and when you omit the parameter it reasons at "max" - Meta reasoning docs. Haiku 5.5 and Luna both default to medium. A team that swaps one model ID for another without setting effort explicitly is not comparing like with like: it is comparing Ember at full depth against its rivals at their middle setting, and section 8 shows how large that difference is.

Context is 1,048,576 tokens and maximum output is 524,288 tokens, far above the 128,000-token output ceiling of Haiku and Luna - Meta. Rate limits are 3,000 requests and 4 million tokens per minute, counted per team rather than per key, and web search grounding costs $2.50 per 1,000 queries on top of tokens - Meta Model API pricing.

3.2 Where you can run it

Ember is on the Meta Model API and in Muse Code, and Meta says Muse Ember, Spark and Wonder are also available on Google Cloud and Oracle Cloud - Meta. OpenRouter lists it too, served by Meta and by Google - OpenRouter. The API speaks three protocols, Responses, Chat Completions and Messages, and Meta's own model guide spells out a practical consequence: in Claude Code and the Claude Agent SDK, you can map the haiku alias and the sub-agent model to muse-ember-1.0 - Meta choose-a-model guide. In other words, Meta is inviting Claude Code users to keep Anthropic's harness and swap Anthropic's cheap model out from underneath it.

3.3 What Meta claims, and what the partners said

Meta's headline is that Ember "delivers more intelligence per dollar than Claude Haiku 5.5 or GPT-6 Luna at any reasoning setting: it matches Haiku's best score at about a third of the cost and beats every Luna setting" - Meta. The Artificial Analysis records confirm the core of that: Ember at max scores 36.0 for $0.136, Haiku's best (max) scores 36.5 for $0.411, and no Luna setting reaches either score - Artificial Analysis. One detail on Meta's own chart is inconsistent (its subtitle calls Ember's costs "estimates at proposed pricing" while its footnote says "list pricing"), but it does not matter in practice, because Artificial Analysis independently reports the same $0.14 at list price.

The partner quotes are useful as signals of where early testers pointed the model, not as measurements. Cloudflare's CTO said Ember "ran up to 30% faster at the tail than the comparable model we tested" on multi-repo investigation, and You.com's inference lead reported a p95 latency of 2.2 seconds on its coding quests with "36-48% fewer output tokens" than leading open-weight models - Meta. Those are vendor-selected testimonials published on the vendor's own launch page, so treat them as the best case rather than the typical one.

4. Claude Haiku 5.5: Anthropic's Effort-Controlled Small Model

Anthropic released Haiku 5.5 on October 7, 2026, and the release was mostly a pricing event: the company says it costs around 75% less to run than Haiku 4.5 on average, 90% less for requests up to 100,000 tokens and 50% less above that - Anthropic. It is also Anthropic's first Haiku-class model with an adjustable effort setting, which turns one model into five price points.

PostHog's test is the most practical public look at Haiku 5.5 in the role it was built for. Dennis Ivy gives Opus a crew of ten Haiku sub-agents, runs them on a real codebase against Opus working alone, and covers "the pricing catch that can have you paying 5× more": that catch is the 100,000-token threshold covered in section 9, and it is the single most important thing to understand before putting Haiku in a long agent loop.

4.1 Pricing, in full

For prompts up to 100,000 tokens, Haiku 5.5 costs $0.10 input and $0.50 output per million tokens, with cache hits at $0.01, five-minute cache writes at $0.125 and one-hour cache writes at $0.20 - Anthropic pricing. For prompts over 100,000 tokens, every one of those rates is multiplied by five: $0.50 input, $2.50 output, $0.05 cache hits. The Batch API halves both sets of prices, to $0.05 / $0.25 below the threshold. US-only inference adds a 1.1x multiplier that applies to the long-prompt prices as well.

One more cost factor hides in the documentation rather than the price table. Claude 4.7 and later models, including Haiku 5.5, use a newer tokenizer that "produces approximately 30% more tokens for the same text" than earlier Claude models, and Anthropic's own announcement notes Haiku 5.5 uses slightly more tokens per task than Haiku 4.5. Equal prices per token are therefore not equal prices per word: the same document can cost more to process on Haiku than on a model with a more compact tokenizer, before any difference in reasoning.

4.2 Effort, defaults and Anthropic's own guidance

Haiku 5.5 supports low, medium, high, xhigh and max, and medium is the default - Anthropic effort docs. Anthropic's guidance is unusually candid about the trade-offs: start with medium for most work including agentic coding, use low for chat and short tool tasks, and note that "in long agent prompts, the model is more likely to skip a search, stop early, or skip a check at low." It recommends xhigh or max only where evals show a gain, and suggests comparing them with Sonnet 5.5 on performance, cost and speed. That last line is a quiet admission that at its top settings, Haiku 5.5 starts to cost like a bigger model.

4.3 Anthropic's benchmarks and customer results

Anthropic's launch table compares Haiku 5.5 directly with GPT-6 Luna: 1,620 on GDPval-AA against Luna's 1,437, 72.4% on the OSWorld 2.1 offline subset against 48.9%, and 39.2% on Terminal-Bench 4.0 against 16.4% - Anthropic. Customer results on the same page include Asana reporting over 30% lower latency on task completions, HubSpot scoring 92.8% on its CRM suite averaged over three runs, and Box measuring 11 points above Haiku 4.5 at about half the latency.

Note that Box appears in both Anthropic's and Meta's launch materials, praising each company's small model for work on enterprise content. That is not a contradiction; it is a reminder that customer quotes measure a model against its own predecessor or against an unnamed baseline, never against the direct rival you are trying to choose between.

5. GPT-6 Luna: OpenAI's Frugal Worker

OpenAI launched GPT-6 Sol and GPT-6 Luna on September 22, 2026, with API prices cut by half against the GPT-5.6 promotional pricing they replaced - OpenAI Developer Community. For Luna that meant input falling from $0.20 to $0.10 and output from $1.20 to $0.50 per million tokens - The Decoder. OpenAI describes it as its "most efficient model for focused, high-volume tasks", and our earlier comparison of GPT-6 Sol, Astra and Luna covers where it sits in OpenAI's own lineup.

5.1 Pricing and limits

Luna costs $0.10 input, $0.01 cached input and $0.50 output per million tokens, with cache writes at $0.125 - OpenAI. Its long-prompt surcharge is the gentlest of the three: prompts over 272,000 input tokens pay 2x the input and cache rates and 1.5x the output rate for the full request, which works out to $0.20 / $0.02 / $0.75. Batch and Flex processing are priced at 50% of standard, Fast mode at 2x, and regional processing adds 10%.

The context window is 1,050,000 tokens, but the maximum input is 922,000 and the maximum output 128,000, with a knowledge cutoff of May 18, 2026. Reasoning effort runs from none to max with medium as the default, so Luna can run with reasoning switched off entirely, as Haiku 5.5 can at high effort or below; Ember cannot. It is also available on Amazon Bedrock as openai.gpt-6-luna, with Google Cloud in private preview.

5.2 OpenAI's claims and the index that moved

At launch, OpenAI's headline efficiency claim was on DeepSWE v1.1, where The Decoder reports Luna at maximum effort scoring about 66.6%, matching Sol at xhigh, for $0.22 per task - The Decoder. The same report notes that Luna held at 37 points on the Artificial Analysis Intelligence Index at the time. On today's index it scores 27.9 at max - Artificial Analysis.

Luna did not get worse. The index changed: Artificial Analysis now runs Intelligence Index v5.0.1, a suite of nine evaluations weighted equally across knowledge work, coding, science and general knowledge, much of it agentic - Artificial Analysis methodology. Luna's slide from 37 to 28 is what happens when an index shifts toward long agent tasks, the kind Luna is weakest at. The lesson generalizes: never compare index scores across index versions, and never compare a launch-week article's number with today's leaderboard.

6. Benchmark by Benchmark: Where Each Model Wins

A single index number hides as much as it shows. The table below lays out every component of the Artificial Analysis index for all three models at maximum effort, plus the knowledge-reliability breakdown, so you can see which jobs each model is actually good at.

Source: Artificial Analysis records for Muse Ember, Claude Haiku 5.5 and GPT-6 Luna; strict AutomationBench from the AutomationBench-AA page.

6.1 Agentic workflows: Ember by a wide margin

AutomationBench is the evaluation closest to what most companies actually buy agents for. It is Artificial Analysis's run of Zapier's private set of 657 business-workflow tasks across simulated apps such as Gmail, Salesforce, Slack, Zendesk, Jira and HubSpot, where the agent has to discover the right API endpoints, follow layered business rules and write correct data into each system - Artificial Analysis. Ember completed 37.7% of those workflows in full, level with Grok 4.7 and slightly ahead of GPT-6.1 Sol (37.0%), a model with twenty times its output price. Haiku 5.5 completed 13.9%, Luna 20.2%.

The run data hints at why Haiku struggles here. Across the 657 tasks, Haiku 5.5 made 7,885 model turns and 31,351 tool calls, the fewest of any of the 29 models on the page; Ember made 13,236 turns and Luna 13,084 - Artificial Analysis. A model that takes fewer turns on a task requiring API discovery is usually stopping before the work is done. Anthropic describes that failure mode itself, though for Haiku at low effort: "in long agent prompts, the model is more likely to skip a search, stop early, or skip a check" - Anthropic. Haiku also triggered 0.84 guardrail violations per task against Ember's 0.50.

6.2 Coding: mixed, and harness-dependent

Coding splits cleanly by task type. On Terminal-Bench 4.0, where the agent works in a shell to reach a verified end state, Ember leads at 40.9% against Haiku's 32.8% and Luna's 12.6%. On FrontierCode 2.0, Cognition's evaluation of real engineering tasks graded on whether a maintainer would merge the change, Haiku leads at 34.9% against Ember's 27.2% - Artificial Analysis. The pattern is intuitive: Ember is better at persistent, step-by-step execution; Haiku writes code a reviewer is more likely to accept.

There is a subtlety in Meta's own comparison table worth knowing about. Meta lists Haiku at 39.2% and Luna at 16.4% on Terminal-Bench 4.0, but its evaluation report explains that those figures come from different harnesses: Ember's from Artificial Analysis's mini-swe-agent run, Haiku's from Anthropic's self-reported Claude Code result, Luna's from the Codex leaderboard entry - Meta evaluation report. On a common harness, Artificial Analysis measures Haiku at 32.8%, so Meta's mixed-source table actually understates Ember's lead. Several of Meta's other headline numbers (Agents' Last Exam, the Personal Superintelligence Index, OSWorld 2.1 and PerceptionBench) come from Meta's internal evaluation suite rather than independent runs, and the report itself cautions that competitor runs "may not be optimally tuned for proprietary models". Weigh the independent numbers above the internal ones. Our guide to why AI coding benchmarks mislead goes deeper on harness effects.

6.3 Knowledge and honesty: three very different personalities

The Omniscience rows describe how each model behaves when it does not know something, and the three could hardly be more different. Luna answers the most questions correctly (39.6%) but, when it is not right, it gives a wrong answer 79.3% of the time rather than declining. Ember answers the fewest correctly (21.1%) because it declines to answer 64% of questions, which keeps its hallucination rate at 17.4%, half of Haiku's - Artificial Analysis. Haiku sits between them and posts the best net knowledge score of the three.

For an agent, this matters more than raw accuracy. A model that abstains when unsure fails loudly: the agent knows it needs to search, retrieve or escalate. A model that confidently invents an answer fails silently, and silent failures are the expensive kind (section 7 puts a number on that). Meta's own documentation agrees with the data, steering "knowledge-heavy questions answered without retrieval" to Muse Spark or Muse Wonder rather than Ember - Meta. In practice, Ember needs retrieval for factual work; Luna needs verification.

7. Cost per Successful Task: The Number That Actually Matters

Cost per task, as the index reports it, is the cost of an attempt. What you pay for in production is a result. If one model completes a workflow 38% of the time and another 14% of the time, the second one's cheap attempts are not cheap at all. Dividing each model's per-attempt cost on an evaluation by its success rate gives a first approximation of the cost of getting one task done.

On agentic work the result is lopsided. A fully completed AutomationBench workflow costs about $0.05 with Ember, $0.18 with Luna and $0.30 with Haiku: Haiku's attempt costs 2.4 times Ember's, and it succeeds a little over a third as often, so the gap compounds to more than sixfold. On Terminal-Bench 4.0, a solved task costs $0.65 with Ember against roughly $1.90 with either rival. Even on FrontierCode, the one coding evaluation Haiku wins on success rate, Haiku's attempts are so much more expensive ($0.46 against Ember's $0.10) that Ember is still cheaper per mergeable fix.

The picture flips on short, non-agentic reasoning. On HLE-Diamond, the per-correct-answer cost is about $0.067 for Luna, $0.097 for Ember and $0.158 for Haiku; on hard CritPt physics problems it is $0.076, $0.138 and $0.233 respectively (our arithmetic on the same records). These are single-shot questions with no long context to re-read, so the cache-price advantage that carries Ember on agent loops disappears, and Luna's habit of thinking briefly makes it the cheapest per correct answer. If your workload is classification, extraction or one-turn reasoning rather than multi-step agents, Luna deserves a test before you assume Ember wins.

7.1 The caveat: retries are not independent

Dividing cost by success rate assumes you can detect a failure and retry, and that each retry has the same chance of succeeding. Neither is quite true. Some tasks are simply beyond a given model, so retrying them burns money without ever succeeding; the real cost per success for the weaker model is higher than the simple division suggests. And retrying assumes you know the attempt failed. For a terminal task with a test suite, you do. For a contract summary or a CRM update, you often do not.

7.2 When a failure costs more than the tokens

That second point is where Haiku's case lives. Suppose a failed coding fix is not retried automatically but costs you something real: an engineer's review time, a broken build, a rollback. Call that cost F. The expected cost per FrontierCode task is then the token cost plus the failure rate times F. For Ember that is $0.096 + 0.728 × F; for Haiku, $0.461 + 0.651 × F. The lines cross where F is about $4.70.

Below about $4.70 per failure, Ember's cheaper attempts win; above it, Haiku's higher acceptance rate pays for its token bill. Five dollars is a few minutes of an engineer's time. So for production code that a human reviews and merges, a reasonable reading of the data is that Haiku 5.5 is the better value despite costing nearly five times as much per attempt, while for exploratory, test-verified or throwaway subtasks, Ember is. The same logic applies to any domain: the more a silent failure costs you, the more you should pay for the model that fails less in the way that hurts.

8. Effort Settings: The Hidden Multiplier on Every Bill

All three models are reasoning models with an effort dial, and the dial moves cost per task by an order of magnitude. Artificial Analysis benchmarks each model at several settings, which lets us lay out the full ladder.

Intelligence Index v5.0.1 score, cost per task and minutes per task from Artificial Analysis pages for each setting, for example Haiku 5.5 at xhigh, Luna at medium and Ember at xhigh.

Three conclusions fall out of this table.

First, compare at matched capability, not matched labels. "Max" means a different amount of thinking on each model. The fair comparison is at a target score: to reach about 34 on the index, Ember at xhigh costs $0.116 and Haiku at xhigh costs $0.248, so Ember is less than half the price for the same capability. To reach about 24, Haiku at its default medium ($0.092) and Luna at xhigh ($0.104) cost nearly the same. Luna never reaches 30 at any setting.

Second, the defaults are a trap in both directions. Because Ember defaults to max and its rivals to medium, an out-of-the-box swap puts Ember at $0.136 per task against Haiku at $0.092 and Luna at $0.042. A team that benchmarks by changing only the model ID will conclude Ember is the most expensive of the three, and they will be right about the bill and wrong about the value, since Ember is delivering a score of 36 against 25 and 19. Set effort explicitly in every comparison you run.

Third, the top of each ladder is expensive. Haiku's last step, from xhigh to max, buys 1.9 index points for a 66% increase in cost and 54% more time. Anthropic's own advice is to use xhigh or max only "where your evals show a quality gain" - Anthropic effort docs. Ember's last step is cheap by comparison (1.9 points for 17% more cost), which is consistent with Meta choosing max as the default. For Luna, the low setting is worth knowing about for trivial work: at half a cent per task and well under a minute, it is the cheapest configuration of any model here, at the price of a 9.7 score that rules it out for anything requiring judgment.

9. The Long-Context Cliff: 100K, 200K and 272K Tokens

All three models advertise windows of about a million tokens, and all three charge more once a prompt gets long. The thresholds and multipliers differ, and in agent loops, where context grows every turn, they decide the bill more than the base price does.

Sources: Anthropic, Meta, OpenAI.

The "whole request" row is the trap. Anthropic's documentation is explicit that a request's prompt length counts all of its input tokens, including cache reads and writes, and that a request over the threshold pays the higher prices even when part of its prompt is a cache hit - Anthropic pricing. Meta's rule is the same for Ember, and it adds that a request continuing a stored conversation with previous_response_id counts that whole conversation toward the threshold - Meta Model API pricing.

9.1 What the cliff does to a coding agent

The clearest evidence comes from coding harnesses, where context routinely passes 100,000 tokens. Artificial Analysis measures Claude Code running Haiku 5.5 at max effort at $2.67 per task, with an average of 15.3 million input tokens, a 91.8% cache hit rate and 738,000 output tokens per task - Artificial Analysis coding agents. Re-pricing those token counts ourselves at Haiku's standard rates gives roughly $0.65 per task; at the over-100K rates for every request, roughly $3.20. The measured $2.67 sits about 80% of the way toward the long-prompt figure, which is consistent with most of the agent's requests crossing the 100,000-token line. In a long coding session, Haiku 5.5 is mostly a $0.50 / $2.50 model.

Luna, by contrast, stays under its far higher threshold: Codex running Luna at max costs $0.16 per task on 8.6 million input tokens, almost exactly what standard rates predict. Ember's cost inside Meta's own harness is not published, but its token counts are: 18.6 million input tokens at a 96.2% cache hit rate and 117,000 output tokens per task. At Meta's list prices that works out to about $0.20 if every request stayed under 200,000 tokens and about $0.60 if every request went over, before any cache-write charges. Either way it is a fraction of Haiku's measured $2.67.

9.2 How to stay under the line

The practical defenses are architectural rather than contractual. Keep sub-agents on small, isolated contexts: a sub-agent that receives a focused brief and returns a summary rarely approaches 100,000 tokens, while one that inherits the parent's full history crosses it in a few turns. Compact or summarize history before a long loop reaches the threshold rather than after. Trim tool results to what the next step needs instead of pasting whole files and pages back into context. And measure: Anthropic's token-counting endpoint and Meta's input-token counting route let you check a request's size before you send it. For Haiku specifically, a context budget of 100,000 tokens is not a tuning choice, it is the difference between a $0.10 model and a $0.50 one.

10. Speed and Throughput: Minutes per Task

Speed shows up on the bill indirectly. An agent that takes fifteen minutes per task ties up a worker slot, a sandbox, a user's patience and often a rate-limit allowance for fifteen minutes. At volume, wall-clock time per task decides how many machines you run and how many tasks you can promise per hour.

Ember finishes an average index task in 3.1 minutes, against 11.6 for Luna and 14.8 for Haiku, and it generates output at 448 tokens per second with about 5 seconds to its first answer token. Haiku at max generates 255 tokens per second but takes about 185 seconds to its first answer token, because it thinks at length before responding, and Luna is the slowest generator at 109 tokens per second, with about 76 seconds to its first answer token - Artificial Analysis.

The per-evaluation times sharpen the point. On Terminal-Bench 4.0, Ember spends about 5.5 minutes per task against roughly 22 minutes for both rivals; on FrontierCode, 2.0 minutes against 16.6 for Haiku. Some of that gap is raw generation speed and some is simply fewer tokens to generate. Either way, Ember completes nearly five times as many agent tasks per worker-hour as Haiku at max, which is a cost saving that never appears in a cost-per-task chart.

Rate limits frame the throughput ceiling. Meta allows 3,000 requests and 4 million tokens per minute per team for Ember on its own API. OpenAI's limits for Luna scale with usage tier, from 5,000 requests and 2 million tokens per minute on the Build tier to 30,000 requests and 180 million tokens per minute on the Grow tier - OpenAI. For very high volumes today, Luna has the most published headroom on a single first-party API, and Haiku has the widest distribution across clouds, which also spreads capacity.

11. Coding Agents: What a Task Costs Inside Real Harnesses

Index evaluations run every model in a common harness. In real life you run a model inside a product, and the product changes both the score and the cost. Artificial Analysis's coding-agent leaderboard measures exactly that: each model inside its lab's own agent, on Terminal-Bench 4.0, SWE-Atlas-QnA and FrontierCode v1.1.

Source: Artificial Analysis coding agents leaderboard. Index shown on a 0 to 100 scale.

Among our three, the ranking inside real harnesses matches the index: Ember in Muse Code scores 45.9, Haiku in Claude Code 37.3, Luna in Codex 31.2. Artificial Analysis attributes Ember's lead to SWE-Atlas-QnA and Terminal-Bench, with Haiku still ahead on FrontierCode - Artificial Analysis. The cost column is where the harness effects show. Haiku's 738,000 output tokens per task, more than six times Ember's 117,000, combine with the 100K cliff from section 9 to make it by far the most expensive small-model configuration in the table, at $2.67 against Luna's $0.16.

Two rows outside our trio deserve attention for anyone choosing a cheap coding agent. DeepSeek V4.1 Flash 1017 in its own harness scores 46.3 for $0.30, and GPT-6.1 Terra at high effort scores the same 46.3 for $0.54 in about twelve minutes. Both match Ember on score with a published cost, and both beat Haiku in Claude Code on score, cost and time. If coding is your main workload, the shortlist is wider than the three $0.10 models, which is the subject of the next section. Our November ranking of models for agents covers the full field.

The top three rows tell a different story, about routing, that section 14 returns to: Devin's pairing of Opus 5.5 with Luna scores within 0.2 points of Opus alone at 30% lower cost.

12. The Fourth Option: When a Pricier Model Is Cheaper per Task

Meta claims Ember "sits on the efficiency frontier of the full Artificial Analysis leaderboard: no model scores higher for less per task" - Meta. That is true. But pressure-testing it against the full leaderboard turns up something the three-way framing hides: the frontier around Ember is crowded by a model that costs ten times as much per token.

GPT-6.1 Terra lists at $1 input and $5 output per million tokens, with cache hits at $0.05 - OpenAI. On a price sheet it looks like a different weight class. On cost per task, it competes directly.

At low effort, Terra scores 29.4 for $0.071 per task, higher than Luna's best (27.9) at less than half Luna's cost - Artificial Analysis.

At medium effort, its default, Terra scores 38.2 for $0.169, higher than Haiku 5.5 at max (36.5) at 41% of Haiku's cost, and higher than Ember at max for about 24% more money - Artificial Analysis. On the index, both Luna at max and Haiku 5.5 at max are dominated: another configuration scores higher for less. Ember is not, which is exactly Meta's claim.

The mechanism is the one from section 2, pushed to its extreme. Terra at medium produced about 13.6 million output tokens across the whole index, against Ember's 115 million and Haiku's 298 million. A model that charges ten times as much per token but needs a tenth of the tokens costs the same. The lesson is general: the token price is a weak predictor of the task price, and any shortlist built by filtering on price per token will miss configurations like this one.

Terra is not a drop-in replacement for every job, and the per-evaluation records show where it falls short. Its AA-Briefcase knowledge-work score at medium is 1,190, far below Ember's 1,521 and Haiku's 1,573, and its AutomationBench partial score of 57.8% trails Ember's 68.1%. Its output speed is about 66 tokens per second, though it needs so few tokens that it still finishes an index task in 2.7 minutes. Where it is strong is factual and scientific reasoning: 47.4% Omniscience accuracy and 56.5% on hard CritPt. The honest summary is that Terra at low or medium effort is the strongest alternative to all three of our models for coding and reasoning-heavy work, and Ember remains the better choice for multi-step business workflows. For the open-weight end of the market, MiMo-V2.6-Pro (29.9 for $0.257, MIT-licensed) and the self-hosted route in our Muse Glimmer guide are the options to compare against.

13. Scored Comparison: Which Model for Volume Agent Work

To turn all of this into one view, the table below scores the three models, plus GPT-6.1 Terra at its default medium setting as the strongest outside alternative, for the job they are all marketed for: executing high volumes of agent tasks. Every cell gives its score out of 10 and the evidence behind it.

Cost: Artificial Analysis cost per Intelligence Index task. Agentic: AutomationBench-AA workflows fully completed (Terra: share of objectives met, as its strict score is not published at medium). Coding: Artificial Analysis Coding Agent Index in each lab's own harness. Knowledge: AA-Briefcase Elo, adjusted for AA-Omniscience reliability. Speed: minutes per index task. Evidence from the Artificial Analysis pages cited in sections 1 to 12. Final = weighted average.

The short cells hide some of the evidence behind each score, so here it is in full. Ember's cost score reflects both its $0.136 per task and its $0.004 cache reads, and its agentic score its low 0.50 guardrail violations per task. Terra's cost score is lower than Ember's despite a similar figure because it buys that figure at ten times the token price, which leaves less room when a workload's token mix differs from the index; its coding score rests on 34.5% on FrontierCode and a published $0.39 per coding task. Luna's cost score credits its $0.042 per task at its default medium effort, and its knowledge score is held down by its 79.3% hallucination rate. Haiku's coding score credits the best FrontierCode result of the three (34.9%) against a $2.67 per task bill inside Claude Code, its knowledge score the best AA-Briefcase Elo and best net Omniscience score of the three, and its low agentic score the fewest turns of any of the 29 models on AutomationBench. Ember's knowledge score of 6 balances the lowest factual recall of the three (21.1%) against the lowest hallucination rate (17.4%), and Terra's balances the best recall here (47.4%) against the weakest Briefcase score.

The weights reflect the job. Cost per task carries the most weight (30%) because cost is the reason anyone picks a model from this tier. Agentic workflows and coding carry 20% each because they are the two workloads these models are sold for. Knowledge work and accuracy carries 15% because a volume executor usually works from supplied context rather than its own memory, and speed 15% because it converts directly into throughput per worker.

Those weights are a choice, and changing them changes the order in a predictable way. If your volume work is producing polished documents and reports rather than executing workflows, raise the knowledge-work weight and Haiku 5.5 climbs; on AA-Briefcase it is the best of the three. If your work is single-shot classification or extraction, the agentic columns matter less and Luna's low-effort economics become more attractive. Our table of the cheapest LLM APIs for agents is a useful companion if you want to extend this comparison to more models.

14. Routing: Pairing a Planner With a Cheap Executor

None of the three labs expects you to run a whole agent on its volume model. Meta's documentation says it directly: put Muse Wonder on the main loop, "where planning and multi-step reasoning decide the outcome", and put Ember on sub-agents and background tasks: "the larger model plans, and Muse Ember handles the volume" - Meta. Anthropic's model overview describes Haiku 5.5 as built for "high-volume, latency-sensitive tasks such as classification, extraction, and routing" - Anthropic models overview. The economics of the three small models only make full sense inside that architecture.

The evidence that this works at the frontier comes from the coding-agent leaderboard. Devin's Fusion CLI, pairing Opus 5.5 at max with Luna at high, scores 61.5 for $8.00 per task against 61.7 for $11.42 with Opus alone in Claude Code, essentially the same capability for 30% less - Artificial Analysis. GitHub took the same idea mainstream at Universe on October 28 with Copilot Fusion, which "decides which AI model, or mix of models, should handle each step of a coding task", and GitHub says Fusion Max comes close to Opus 5.5 Max on the Artificial Analysis Coding Agent Index at half the cost - The Next Web.

14.1 The escalation arithmetic

The simplest routing pattern is escalation: try the cheap model first and send failures to a stronger one. If the cheap model succeeds with probability p at cost c, and the strong model costs C, the expected cost per task is c + (1 − p) × C, provided you can detect failures. With Ember on Terminal-Bench-style work (p of about 0.41, c of about $0.27) and a strong model at a few dollars per attempt, the strong model only runs on the 59% of tasks Ember could not finish. Whether that beats running the strong model on everything depends on C and on whether failures are detectable, which is why routing pays off most on work with built-in verification: tests, schemas, deterministic checks on the final state.

The same logic explains why the cheap model's failure mode matters as much as its success rate. Ember's tendency to abstain rather than guess (section 6.3) makes it a good first-try model, because its failures are visible and can be escalated. Luna's 79% hallucination rate on unknown facts makes it a risky first try for anything factual, because its failures look like successes. Our full guide to AI model routing covers the main router designs.

Not every team wants to build and maintain that machinery. Platforms such as O-mega, which builds and runs a company's website, app, billing, content and admin through one AI conversation, are the other route: you describe the outcome to one AI instead of wiring planner and executor models together yourself. The trade-off is control: you give up hand-tuning the executor for each step in exchange for not having to maintain the routing.

15. Hidden Costs and Failure Modes

Beyond the long-context cliff and the effort defaults, several smaller factors move the real cost of these models in ways a comparison table rarely shows.

15.1 Tokenizers make equal prices unequal

A price per million tokens is only comparable if a token means the same thing. It does not. Anthropic states that its tokenizer for Claude 4.7 and later models produces about 30% more tokens for the same text than its earlier models, with the exact increase depending on content - Anthropic pricing. Meta and OpenAI publish no equivalent comparison for their tokenizers. The practical consequence is that the only reliable comparison is an empirical one: send the same documents through each provider's token-counting endpoint and compare actual counts, rather than assuming $0.10 buys the same amount of text everywhere.

15.2 Caching works differently on each platform

Meta caches automatically: the Model API compares the start of every prompt with recently cached state, with no cache key, flag or breakpoint to manage, and reports the cached share in cached_tokens - Meta prompt caching docs. Anthropic supports both automatic caching through a single top-level cache_control field and explicit breakpoints, with five-minute writes at 1.25x and one-hour writes at 2x the base input price - Anthropic pricing. OpenAI bills Luna's cache writes at 1.25x and reads at 10% of input. In all three cases, the rule that decides your hit rate is the same: put stable content first (system prompt, tool definitions, examples) and variable content last. A timestamp or a user ID near the top of a prompt can quietly cut the hit rate, and with it multiply the largest line on the bill.

15.3 Tool fees sit on top of tokens

Web search is billed separately. Meta charges $2.50 per 1,000 searches for Ember - Meta, while Anthropic charges $10 per 1,000 searches plus the tokens the results add to context. For a research agent that searches several times per task, that difference can exceed the token cost of a small model entirely. Anthropic's tool definitions also add fixed overhead to every request: 286 tokens of tool-use system prompt for Haiku 5.5, about 4,500 tokens for the computer-use toolset and about 6,600 for the browser toolset - Anthropic pricing. At $0.01 per million cached tokens that overhead is negligible; at the over-100K rate, or uncached, it is not.

15.4 Vendor risk is a cost too

Model access can disappear for reasons that have nothing to do with price or quality. OpenAI told SpaceX it would wind down Cursor's access to its models, with access ending on November 12, 2026, saying it could not be confident SpaceX would use its technology within its terms; Cursor said OpenAI models were about 5% of its traffic - Channel Insider. The structural point is that any architecture welded to one lab's small model inherits that lab's decisions. Distribution helps here: Haiku 5.5 runs on Anthropic's API, AWS, Google Cloud and Microsoft Azure; Luna on OpenAI's API and Amazon Bedrock; Ember on Meta's API, Google Cloud, Oracle Cloud and OpenRouter. Because Ember accepts both OpenAI-style and Anthropic-style requests, and most agent frameworks already speak all three APIs, keeping a second model warm costs little and buys a lot of resilience.

15.5 Benchmarks are run by interested parties

Every launch table in this guide was published by the company selling the model. Meta's evaluation report is commendably explicit about its methods, including that competitor runs "remain best-effort" - Meta evaluation report. Even independent indexes have a weighting that may not match your work: Artificial Analysis weights knowledge work, coding, science and general knowledge at 25% each, and few production agents spend a quarter of their time on research physics. Use the independent records to build a shortlist, then decide on your own traffic, as the next section describes.

16. How to Measure Cost per Task on Your Own Workload

Every number in this guide is somebody else's workload. Yours will differ in prompt length, tool count, cache hit rate and failure cost, and those are exactly the factors that decide which model is cheapest. Measuring it properly takes a few days, not a research project.

Step 1: log every field. For every model call, store input tokens, cached input tokens, cache-write tokens, output tokens and, where reported, reasoning tokens, together with a task ID so calls can be grouped into tasks. All three APIs return these in their usage objects; Meta reports cached input under input_tokens_details.cached_tokens on its Responses API - Meta prompt caching docs.

Step 2: price every call with the full rules, including the long-prompt tiers. The function below does that for the three models at list prices as of this writing; it treats each request on its own, as all three providers do.

# List prices per million tokens, 12 Nov 2026. Verify before use.
# Each rate tuple is (input, cached, cache_write, output).
PRICES = {
    "muse-ember-1.0": {
        "threshold": 200_000,
        "standard": (0.10, 0.004, 0.10, 0.50),
        "long": (0.30, 0.012, 0.30, 1.50),
    },
    "claude-haiku-5-5": {
        "threshold": 100_000,
        "standard": (0.10, 0.010, 0.125, 0.50),
        "long": (0.50, 0.050, 0.625, 2.50),
    },
    "gpt-6-luna": {
        "threshold": 272_000,
        "standard": (0.10, 0.010, 0.125, 0.50),
        "long": (0.20, 0.020, 0.250, 0.75),
    },
}

def call_cost(model, uncached_in, cached_in, cache_write, output):
    """USD cost of one request. Output includes reasoning tokens."""
    p = PRICES [model]
    # Cached tokens count toward the long-prompt threshold.
    prompt_tokens = uncached_in + cached_in + cache_write
    tier = "long" if prompt_tokens > p ["threshold"] else "standard"
    i, c, w, o = p [tier]
    total = uncached_in * i + cached_in * c + cache_write * w + output * o
    return total / 1_000_000

def task_cost(model, calls):
    """calls: list of (uncached_in, cached_in, cache_write, output)."""
    return sum(call_cost(model, *call) for call in calls)

Meta's pricing page lists no separate cache-write rate for Ember, so the sketch bills Ember's newly written tokens at the normal input rate; Anthropic's five-minute write rate is used for Haiku. Adjust both to match what your invoices show.

Step 3: record success, not just completion. Decide what success means for each task type (tests pass, schema validates, a reviewer accepts, a downstream system updates correctly) and log it alongside the cost. Then compute cost per successful task as in section 7, and estimate what a silent failure costs you, as in section 7.2.

Step 4: sweep effort. Run a representative sample of a few hundred tasks at two or three effort settings for each candidate, with effort set explicitly. Section 8 showed that defaults differ and that the top settings cost far more for little gain; your sweep tells you where that knee sits for your work.

Step 5: shadow test first. Send a slice of live traffic to the candidate model in parallel, without acting on its output, and compare cost and success on identical inputs. This catches the factors benchmarks miss: your prompt structure's cache behavior, your context lengths relative to each tier threshold, and your tasks' particular failure modes. Our guide to agent evals and benchmarks covers how to build the harness, and the true cost of LLM inference covers the infrastructure side of the bill.

17. Market Context and What Could Change the Verdict

The three-way tie at $0.10 / $0.50 did not happen by accident. OpenAI set the price with Luna in September, Anthropic matched it with Haiku 5.5 in October, and Meta matched it again with Ember in November, each time choosing to compete on something other than the headline rate. That makes the next round of competition predictable: cache prices, long-prompt thresholds and token efficiency are where the volume tier will be fought, because those are the terms that actually decide cost per task. Anthropic has already shown it is willing to move cache prices, halving Sonnet 5.5's cache reads to $0.10 alongside the Haiku launch - Anthropic.

The same week Ember launched, the top of the market moved too. Anthropic released Claude Fable 5.2 at $8 input and $40 output per million tokens, down from $10 / $50 for Fable 5.1 - Anthropic pricing. On the Artificial Analysis index, Fable 5.2 at max scores 64.6 for $26.54 per task, against Ember's 36.0 for $0.136 - Artificial Analysis. Roughly 1.8 times the score for roughly 195 times the cost is the spread that makes planner-plus-executor architectures economically inevitable, and our comparison of Fable 5.2 and Opus 5.5 on cost per task covers the top end of that spread in detail.

Demand is shifting in the direction that favors volume models. Always-on agents are becoming a product category: Perplexity's Teammates, launched for Max subscribers in early November, are designed to "take ownership of ongoing work rather than handle one-off prompts", and each runs on an isolated computer with its own desktop and browser session - IT Brief. On GitHub, more than 40% of pull requests started with a draft from an agent by September, up from 17% in March - The Next Web. Agents that work continuously generate enormous numbers of small, repetitive steps, which is precisely the work these three models were built for, and precisely the work where the cache line dominates the bill.

Several developments would change this guide's verdict, and they are worth watching for:

• Haiku 5.5 repricing. A $0.004 cache rate alone would bring Haiku to about $0.27 per index task by our arithmetic, and a higher long-prompt threshold would transform its economics in coding agents.
• Ember's measured coding cost. Artificial Analysis has not published Ember's cost inside Muse Code; our $0.20 to $0.60 estimate is a range, and the real figure decides how Ember compares with DeepSeek V4.1 Flash 1017 and Terra for coding.
• Ember at lower effort settings. Meta's chart and the independent records only cover xhigh and max. If Ember at medium holds a score in the high twenties for a few cents, it would challenge Luna's low-effort niche as well.
• The next Luna. Luna's token frugality is real; what holds it back is agentic reliability. A version that closes the AutomationBench gap at the same token budget would be the cheapest model of the three.
• Meta's next model. Meta says it has "even more efficient models in training" - Meta.

18. Verdict: Which One to Pick

The search that brings most readers here, Muse Ember versus Claude Haiku 5.5, has a clear answer for the work both are marketed for. For high-volume agent execution (multi-step workflows, tool loops, sub-agent tasks, terminal work), Muse Ember delivers Haiku 5.5's overall capability at about a third of the cost per task and about a fifth of the time, and it completes nearly three times as many business workflows in full. About two thirds of that advantage is genuine token efficiency rather than a price Meta could reverse. Set its effort explicitly, keep requests under 200,000 tokens where you can, and give it retrieval for anything factual, since it would rather decline than guess.

Claude Haiku 5.5 earns its higher bill in three places: deliverables a human will read (it has the best knowledge-work score of the three), code a reviewer must accept (it leads on FrontierCode, and the break-even analysis favors it once a failure costs more than about $5), and teams whose stack is already built on Anthropic's platforms and clouds. Use it at medium or high effort rather than max, and treat a 100,000-token context as a hard budget, because above it Haiku costs five times as much.

GPT-6 Luna is the cheapest per correct answer on short, single-shot reasoning and the cheapest configuration of all at low effort, which makes it a strong choice for classification, extraction and one-turn questions with a verification step. It is the wrong choice for long agent loops, where it completes a fifth of business workflows, and for unverified factual answers, where it invents an answer four times out of five when it does not know.

And before committing to any of them, test GPT-6.1 Terra at low or medium effort. At ten times the token price it beats both Luna and Haiku on cost per task across the independent index, which is the most useful single lesson in this comparison: the price per token tells you very little, the cost per successful task tells you almost everything, and the only cost per task that really counts is the one you measure on your own work.

What this article rests on

Every model figure above is read from its record, re-read from OpenRouter's model feed every 6 hours, so the figures on this page are current even where the prose around them is older. Prices are OpenRouter's listed price per million tokens: for a lab's closed models that is the lab's own list price, and for an open-weight model it is the price of the host OpenRouter routes to by default, which can differ from the lab's own API and from other hosts.

• Muse Ember (Meta): $0.10 in and $0.50 out per million tokens, cached input $0.004, a 1M token window, listed 9 November 2026.
• Claude Haiku 5.5 (Anthropic): $0.10 in and $0.50 out per million tokens, cached input $0.01, a 1M token window, listed 7 October 2026.
• GPT-6 Luna (OpenAI): $0.10 in and $0.50 out per million tokens, cached input $0.01, a 1.05M token window, listed 22 September 2026.
• GPT-6.1 Terra (OpenAI): $1 in and $5 out per million tokens, cached input $0.05, a 1.05M token window, listed 26 October 2026.
• Claude Fable 5.2 (Anthropic): $8 in and $40 out per million tokens, cached input $0.20, a 1M token window, listed 9 November 2026.

In this topic: AI costs

• Claude Fable 5.2 vs Opus 5.5: Real Cost per Task (2026)
• n8n vs Zapier vs Make: What AI Agents Really Cost (2026)
• Muse Code Pricing 2026: Plans, Real Limits, Wonder Full
• Codex Pricing 2026: Plans, Limits, Resets and Credits
• Prompt Caching: Cut AI Agent Bills 45% in 2026
• Grok Bot Pricing 2026: Real Costs, Limits, Alternatives
• GPT-6 Astra Pricing: Real Cost per Agent Task 2026
• Gemini 3.8 Flash: $0.58/Task for Agents 2026

What do AI models and agents cost, and how do you cut it? Every guide on it