Muse Ember vs Claude Haiku 5.5: Complete Comparison and Report on Pricing, Long-Context Rates, Agentic Benchmarks, and Cost per Task
Muse Ember is the smallest and cheapest model in Meta's Muse family, introduced on November 9, 2026 and sold through the Meta Model API under the ID muse-ember-1.0. Claude Haiku 5.5 is Anthropic's small, fast model, released on October 7, 2026 with the ID claude-haiku-5-5, and it has replaced Haiku 4.5 in the Claude apps. Both are built for high-volume work such as routing, extraction, tool calls and coding subagents, both offer a context window of about one million tokens, and both carry the same headline list price of $0.10 per million input tokens and $0.50 per million output tokens. Artificial Analysis gives the two models the same score of 36 on its Intelligence Index at maximum reasoning effort, which makes this one of the closest pairings among current small models.
The differences sit in the price structure and in how many tokens each model spends. Muse Ember keeps its base rate up to 200,000 input tokens, while Haiku 5.5 switches to its higher tier above 100,000 tokens, where input costs $0.50 and output $2.50 per million. On a 150,000-token document, Ember therefore charges $0.016 per request against $0.08 for Haiku 5.5, five times less. Ember's cached input costs $0.004 against $0.01 for Haiku 5.5, and Artificial Analysis measures $0.14 per Intelligence Index task for Ember against $0.41 for Haiku 5.5, because Ember uses roughly a third as many output tokens. Ember also leads on Terminal-Bench 4.0 (40.9% against 32.8% in Artificial Analysis runs) and on the Artificial Analysis Coding Agent Index (46 against 37).
Haiku 5.5 answers with a 50% Batch API discount that Meta has not published for Ember, a higher score on FrontierCode 2.0 (34.9% against 27.2%), higher factual accuracy on AA-Omniscience (34.7% against 21.1%), a measured output speed of 254 tokens per second, a published system card and availability on Amazon Bedrock, Google Cloud, Microsoft Foundry and Claude Platform on AWS as well as Anthropic's own API. Teams with long prompts, multimodal inputs or agent loops that are billed per completed task get more from Muse Ember; teams that run overnight batch jobs, need a hyperscaler contract or depend on documented safety controls get more from Claude Haiku 5.5. Prices in this comparison were verified on the official Meta and Anthropic pages on November 10, 2026.
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MUSE EMBER AND CLAUDE HAIKU 5.5: RELEASE TIMELINE AND MODEL IDENTITY.
Claude Haiku 5.5 arrived first. Anthropic released it on October 7, 2026 as the successor to Claude Haiku 4.5, and the model overview lists its status as "Active (latest)" with retirement not sooner than October 7, 2027. Anthropic positions it for "high-volume, latency-sensitive work such as classification, routing, extraction, and subagent tasks." In the consumer apps, Anthropic announced on October 15 that Haiku 4.5 would leave the model selector, and since October 29 users who had Haiku 4.5 selected have been switched to Haiku 5.5. In Claude Code, the haiku alias resolves to Haiku 5.5 on the Anthropic API, while some cloud providers still map the alias to Haiku 4.5.
Muse Ember followed a month later. Meta describes it as its smallest and most efficient model and sells it in Standard mode only, with no Contributor-mode variant of the kind Meta offers for Muse Spark. It is an API model: the open-weight members of the Muse family are Muse Spark Open and Muse Glimmer, and neither company has published a parameter count for its small model. Meta's own material places Ember underneath Muse Wonder and Muse Spark, as the worker model in planner-and-worker setups where a larger model plans and Ember executes bounded subtasks. Our launch coverage of Muse Ember's API pricing gives the full Meta price list.
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Item | Muse Ember | Claude Haiku 5.5 |
Developer | Meta | Anthropic |
Release date | November 9, 2026 | October 7, 2026 |
API model ID | muse-ember-1.0 | claude-haiku-5-5 |
Position in family | Smallest Muse model, below Muse Spark and Muse Wonder | Smallest current Claude model, below Sonnet 5.5 and Opus 5.5 |
Predecessor | None in the Muse API line | Claude Haiku 4.5 |
Weights | API only | API only |
Parameter count | Not published | Not published |
Knowledge cutoff | Not published | June 2026 |
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MUSE EMBER VS CLAUDE HAIKU 5.5 API PRICING, CACHE AND BATCH RATES.
At the base tier the two price lists are identical for fresh input and output, at $0.10 and $0.50 per million tokens. The gap opens in three places. Ember's cached input costs $0.004 per million tokens, which Artificial Analysis describes as 60% cheaper than the $0.01 Haiku 5.5 charges for cache reads. Anthropic, on the other hand, documents a 50% Batch API discount for Haiku 5.5, bringing it to $0.05 input and $0.25 output, while Meta's pages list no batch price for Ember. Anthropic also charges for cache writes ($0.125 per million tokens for the 5-minute cache and $0.20 for the 1-hour cache), and Meta does not publish a cache-write rate for Ember.
Haiku 5.5 has one more pricing detail that buyers coming from Haiku 4.5 should account for. Anthropic states that the model uses the same newer tokenizer as Claude 4.7 and later models, so the same text counts as approximately 30 percent more tokens than on Haiku 4.5. Meta does not publish a tokenizer comparison for Ember, so token counts for the same prompt can differ between the two models, and the per-token prices below are only fully comparable when the token counts are measured on each model.
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Price per 1M tokens (USD) | Muse Ember | Claude Haiku 5.5 |
Input, base tier | $0.10 | $0.10 |
Output, base tier | $0.50 | $0.50 |
Cached input / cache read, base tier | $0.004 | $0.01 |
Cache write | Not published | $0.125 (5-minute), $0.20 (1-hour) |
Batch input / output | Not published | $0.05 / $0.25 |
Input, long-context tier | $0.30 | $0.50 |
Output, long-context tier | $1.50 | $2.50 |
Cached input, long-context tier | $0.012 | $0.05 |
Long-context threshold | Over 200K input tokens | Over 100K input tokens |
US-only inference | Not published | 1.1x multiplier on all token prices |
Web search tool | Not listed for Ember | $10 per 1,000 searches |
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LONG-CONTEXT THRESHOLDS: 200K TOKENS FOR MUSE EMBER, 100K FOR HAIKU 5.5.
Both companies price long prompts at a premium, and the position of the threshold decides most of the cost difference for document work. Anthropic's pricing page states that a Haiku 5.5 request whose prompt is over 100,000 tokens pays the higher prices, that cache reads and cache writes count toward the prompt length, and that a request over the threshold pays the higher rates even when part of the prompt is a cache hit. Haiku 5.5 is also excluded from the standard-price one-million-token window that other Claude 4.6 and later models receive. Meta applies its higher tier only to requests over 200,000 input tokens, and its multiplier is three times the base rate, against five times for Haiku 5.5.
The table below shows what a single request costs at three prompt sizes, using list prices with no caching and no batch discount. Between 100,000 and 200,000 tokens, Haiku 5.5 is in its premium tier while Ember is still at its base rate, and the gap reaches a factor of five. Above 200,000 tokens both models pay the premium, and Ember remains cheaper because its premium rates are lower.
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Single request (list prices) | Muse Ember | Claude Haiku 5.5 | Ratio |
50K input + 2K output | $0.006 | $0.006 | 1.0x |
150K input + 2K output | $0.016 | $0.080 | 5.0x |
300K input + 4K output | $0.096 | $0.160 | 1.7x |
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CONTEXT WINDOW, OUTPUT LIMITS, MODALITIES AND EFFORT CONTROLS.
Muse Ember accepts 1,048,576 tokens of context and returns up to 524,288 output tokens, four times Haiku 5.5's 128K output limit. Anthropic offers a 300K output ceiling for Haiku 5.5 only through the Batch API, as a beta enabled with the output-300k-2026-03-24 header. On inputs, Meta's documentation lists text, image, video, audio and PDF for Ember, while Anthropic lists text and images for Haiku 5.5. A pipeline that transcribes meeting audio or describes video sequences can therefore send that material to Ember directly, while the same work on Haiku 5.5 needs a separate transcription or frame-extraction step.
Both models expose reasoning effort, and they set different defaults. Ember accepts effort values from minimal to max and defaults to max, which means an Ember call with no effort setting runs at the most expensive level. Haiku 5.5 supports low, medium, high, xhigh and max, defaults to medium and always uses adaptive thinking. Anthropic's migration notes add several breaking changes for teams moving from Haiku 4.5: manual budget_tokens returns an error, temperature, top_p and top_k must be omitted, assistant prefill returns an error, and the older computer_20250124 tool must be replaced with computer_toolset_20260801. Ember works with OpenAI-compatible clients through the base URL api.meta.ai/v1 and supports the Responses, Chat Completions and Messages endpoints.
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Specification | Muse Ember | Claude Haiku 5.5 |
Context window | 1,048,576 tokens | 1M tokens |
Max output | 524,288 tokens | 128K tokens (300K via Batch API beta) |
Input modalities | Text, image, video, audio, PDF | Text, images |
Output | Text | Text |
Effort levels | minimal to max | low, medium, high, xhigh, max |
Default effort | max | medium |
Thinking mode | Effort-controlled reasoning | Adaptive thinking, always on in Claude Code |
Computer use | Supported | Supported (computer_toolset_20260801) |
Tools and structured output | Supported | Supported |
Minimum cacheable prompt | Not published | 512 tokens |
API endpoints | Responses, Chat Completions, Messages | Claude Messages API |
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WHERE MUSE EMBER AND CLAUDE HAIKU 5.5 ARE AVAILABLE: APIS, CLOUDS AND APPS.
Distribution is Haiku 5.5's clearest advantage. Anthropic's model overview lists six platforms: the Claude API, Amazon Bedrock (as anthropic.claude-haiku-5-5), Claude Models on Google Cloud in both its Google-managed and Anthropic-managed forms, Microsoft Foundry and Claude Platform on AWS. A company that already buys cloud capacity from AWS, Google or Microsoft can use Haiku 5.5 under its existing contract. In Claude Code, Anthropic notes that Haiku models are always available on Enterprise plans and cannot be disabled by organization model restrictions.
Muse Ember is sold through Meta's self-serve Model API, which issues an API key in one click and has no waitlist, and Meta's model page also lists it on Google Cloud Marketplace as muse-ember-1-0. Artificial Analysis lists Meta as Ember's only measured provider. Meta's Ember model page says the model is included in Muse Code subscription plans, although the Muse Code subscription help page lists only Muse Spark, Muse Wonder and Muse Image by plan. Neither company states a country list for its small model on the pages reviewed. For a broader view of Meta's developer offer, see our guide to Meta AI API access and Muse model prices.
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Access route | Muse Ember | Claude Haiku 5.5 |
First-party API | Meta Model API (self-serve) | Claude API |
Amazon Bedrock | Not listed | Yes |
Google Cloud | Google Cloud Marketplace listing | Claude Models on Google Cloud |
Microsoft Foundry | Not listed | Yes |
Claude Platform on AWS | Not applicable | Yes |
Coding agent | Muse Code (all plans, per model page) | Claude Code (haiku alias) |
Consumer app | Not stated | Replaced Haiku 4.5 in Claude apps |
OpenAI-compatible endpoint | Yes (api.meta.ai/v1) | No |
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HOW THE MUSE EMBER AND HAIKU 5.5 BENCHMARKS WERE RUN.
Most of the scores in circulation come from Meta's launch table, which compares Muse Ember, Claude Haiku 5.5 and GPT-6 Luna, all at max effort. Meta's evaluation methodology page explains that the figures mix three origins. Some are Artificial Analysis evaluations (the Intelligence Index v5.0, AutomationBench-AA, Coding Agent Index-AA, FrontierCode 2.0-AA and GDP.pdf); some are Meta's internal runs (Agents' Last Exam, the Personal Superintelligence Index, OSWorld 2.1 and PerceptionBench); and the Terminal-Bench 4.0 comparison uses Artificial Analysis for Ember with the mini-swe-agent scaffold, Anthropic's self-reported figure with Claude Code for Haiku 5.5, and the public leaderboard with Codex for GPT-6 Luna. Meta also states that competitor runs are best-effort and may not be tuned for proprietary models.
Anthropic's launch page reports a different set of benchmarks, mostly against Haiku 4.5, GPT-6 Luna and Sonnet 5.5, and the Haiku 5.5 system card states that it gives the effort setting for some evaluations and not for each one. The two vendors chose largely different tests, and where they report the same test the agent scaffold often differs. Terminal-Bench 4.0 shows the effect: Anthropic reports 39.2% for Haiku 5.5 with Claude Code, while Artificial Analysis measured 32.8% in its own runs. The independent figures in the next sections carry more weight for a like-for-like reading.
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DIRECTLY COMPARABLE SCORES FROM META'S MUSE EMBER TABLE.
The figures below are the rows of Meta's launch table where both models were scored at max effort. Ember leads on eight of the ten rows, ties on the Artificial Analysis Intelligence Index and trails on FrontierCode 2.0-AA, where Haiku 5.5 scores 34.9% against 27.2%. The largest gaps are on AutomationBench-AA and on Meta's own Personal Superintelligence Index, a composite of eight internal evaluations. The OSWorld 2.1 figures come from Meta's common framework rather than the official runner, with each model in its provider's computer-use configuration and Claude Sonnet 4.6 as the judge for model-based checks.
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Benchmark (max effort) | Muse Ember | Claude Haiku 5.5 | Source of run |
Artificial Analysis Intelligence Index | 36 | 36 | Artificial Analysis |
Coding Agent Index-AA | 46 | 37 | Artificial Analysis (Muse Code vs Claude Code) |
Terminal-Bench 4.0 | 40.9% | 39.2% | AA for Ember; Anthropic self-reported for Haiku |
FrontierCode 2.0-AA | 27.2% | 34.9% | Artificial Analysis |
AutomationBench-AA | 37.8 | 13.9 | Artificial Analysis, as reported by Meta |
GDP.pdf-AA | 22.8% | 20.8% | Artificial Analysis |
OSWorld 2.1 (partial / strict) | 69.5 / 33.8 | 68.9 / 27.8 | Meta internal |
Agents' Last Exam | 61.6 | 57.0 | Meta internal |
PerceptionBench (with tools) | 80.6 | 78.6 | Meta internal |
Personal Superintelligence Index | 46.8 | 36.1 | Meta internal |
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ARTIFICIAL ANALYSIS RESULTS FOR MUSE EMBER AND CLAUDE HAIKU 5.5.
Artificial Analysis published its Muse Ember review on November 9, 2026, and describes Ember at max effort as the cheapest model scoring 36 or higher on its Intelligence Index. The cost gap comes from token consumption: at the same list prices, Ember needs about $0.14 per Intelligence Index task and Haiku 5.5 about $0.41, because Ember uses roughly a third as many output tokens. The Haiku 5.5 model page lists 298 million output tokens to run the index. Haiku 5.5 keeps an advantage on knowledge and office-document work, with higher AA-Omniscience accuracy and a higher AA-Briefcase Elo.
The AA-Omniscience result also shows a different behavior. Ember declines to answer 64% of the questions and has a hallucination rate of 17.4%, which Artificial Analysis puts at about half of Haiku 5.5's. Ember therefore answers fewer questions and is wrong less often when it does answer. The AutomationBench-AA figures do not line up across sources: the Artificial Analysis article gives Ember 68.1%, level with Muse Wonder and GPT-6 Astra, while Meta's table shows 37.8, and Artificial Analysis wrote in October that Haiku 5.5's 35% on the same test was likely understated. That benchmark should be read as unsettled until the figures are reconciled.
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Artificial Analysis metric | Muse Ember (max) | Claude Haiku 5.5 (max) |
Intelligence Index | 36 | 36 |
Cost per Intelligence Index task | $0.14 | $0.41 |
Blended price (7:2:1 cache/input/output) | $0.0728 per 1M | $0.077 per 1M |
Output tokens used | About one third of Haiku 5.5 | 298M to run the index |
Terminal-Bench 4.0 | 40.9% | 32.8% |
Coding Agent Index | 46 (in Muse Code) | 37 (in Claude Code) |
FrontierCode 2.0 | 27.2% | 34.9% |
GDP.pdf | 22.8% | 20.8% |
AA-Briefcase | 1521 Elo | 1574 Elo |
AA-Omniscience accuracy | 21.1% | 34.7% |
AA-Omniscience hallucination rate | 17.4% | About twice Ember's |
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The Intelligence Index score needs one caution. Artificial Analysis reported a score of 43 for Haiku 5.5 at max effort in its October 7 launch review, while its model page now shows 36, the figure Meta cites from Intelligence Index v5.0. The two numbers come from different versions of the index and cannot be compared with each other. The current page puts both models at 36.
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BENCHMARKS ONLY ANTHROPIC OR ONLY META PUBLISHED.
Several results exist for only one of the two models. Anthropic's launch page reports GDPval-AA, OSWorld 2.1 on an offline subset, Humanity's Last Exam and Chartography for Haiku 5.5, mostly against Haiku 4.5 and GPT-6 Luna; Meta has not published Ember scores on these tests. Meta's internal evaluations of JobBench, OfficeQA Pro V2, GDP.xlsx, VBT and FocusBench appear in its methodology page as test descriptions, without scores on the pages reviewed. The two OSWorld 2.1 figures for Haiku 5.5 (72.4% from Anthropic, 68.9 partial credit from Meta) come from different subsets and setups and are not interchangeable.
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Benchmark | Published by | Claude Haiku 5.5 | Muse Ember |
GDPval-AA v2.1 | Anthropic | 1620 | Not published |
AA-Briefcase v1.1 | Anthropic | 1578 | 1521 (Artificial Analysis) |
OSWorld 2.1 (offline subset) | Anthropic | 72.4% | Not published |
Humanity's Last Exam, no tools | Anthropic | 45.9% | Not published |
Humanity's Last Exam, with tools | Anthropic | 57.4% | Not published |
FrontierCode 1.1 (Main) | Anthropic | 46.4% | Not published |
Chartography, no tools | Anthropic | 46.4% | Not published |
Personal Superintelligence Index | Meta | 36.1 (Meta run) | 46.8 |
Agents' Last Exam | Meta | 57.0 (Meta run) | 61.6 |
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OUTPUT TOKENS AND COST FOR FOUR TYPICAL WORKLOADS.
The four workloads below were computed in Python from the official list prices. The assumptions are stated for each row, cache-write charges for Haiku 5.5 are left out because they depend on traffic patterns, and the same token counts are applied to both models even though their tokenizers differ. The fourth row uses the Artificial Analysis cost per Intelligence Index task as a proxy for agentic subtasks at max effort, where output-token consumption decides the bill.
The results follow the price structure. For short chat requests with caching, the two models are within 4% of each other, with Ember slightly cheaper thanks to its cache rate. For long documents between 100,000 and 200,000 tokens, Ember costs a fifth of Haiku 5.5 at standard rates and less than half of Haiku 5.5 with the Batch discount. For short classification jobs that can wait for a batch, Haiku 5.5 is half the price of Ember, because Meta publishes no batch rate. For agentic tasks at max effort, Ember's lower token use makes it about 2.9 times cheaper per task in the Artificial Analysis measurements.
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Workload (monthly) | Assumptions | Muse Ember | Claude Haiku 5.5 |
Support chatbot | 3M requests; 1,000 fresh + 2,000 cached input, 400 output each | $924 | $960 |
Same chatbot without caching | 3M requests; 3,000 input, 400 output each | $1,500 | $1,500 |
Long-document extraction | 10,000 documents; 150K input, 2K output each | $160 | $800 standard, $400 Batch |
Bulk classification | 1M items; 800 input, 50 output each | $105 | $105 standard, $52.50 Batch |
Coding subagent tasks | 2,000 tasks at AA cost per task | $280 | $820 |
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SPEED, LATENCY AND RATE LIMITS FOR MUSE EMBER AND HAIKU 5.5.
Artificial Analysis measures Haiku 5.5 at max effort at 254 output tokens per second on Anthropic's API, with a time to first answer token of 181.5 seconds, a figure that includes the model's thinking time at max effort on index tasks. For Ember, the Artificial Analysis provider page showed no speed or latency data on November 10, a day after launch. Anthropic states that Haiku 5.5 is the fastest model it has released at standard speed, and that it is slower than the Opus models in Fast Mode; fast mode itself is not offered for Haiku 5.5.
Meta's speed evidence for Ember comes from launch partners. Cloudflare reports that Ember ran up to 30% faster at the tail than the comparable model it tested, and You.com reports a p95 latency of 2.2 seconds, 4 to 13 times faster than leading open-weight models in its tests, and 36% to 48% fewer output tokens. Neither figure is a measurement against Haiku 5.5. Meta's pricing page lists rate limits for Muse Wonder and Muse Spark (3,000 requests and 4 million tokens per minute for the Standard models) and lists no Ember row, and Anthropic's Haiku 5.5 model page does not state rate limits.
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SAFETY CLASSIFIERS, REFUSALS AND DATA HANDLING.
Anthropic's system card for Haiku 5.5 treats the model as meeting its CB-1 and Autonomy-1 thresholds under the Responsible Scaling Policy, without crossing CB-2 or Autonomy-2. The model runs blocking classifiers for specific harmful cyber activities and the same chemical and biological misuse classifiers used for Claude Opus 5 and Sonnet 5, and these classifiers have no fallback model. Developers have to handle a stop_reason of "refusal" in their client, because server-side fallback is not available. The system card reports that Haiku 5.5 refused 84.3% of malicious Claude Code requests, up from 66.6%, and that the success rate of indirect prompt-injection attacks in the Gray Swan test fell from 83.2% to 7.1%.
Meta's pages for Ember contain no comparable safety documentation. On data handling, Meta's Model API pricing table labels its Contributor-mode variants as used to improve Meta's products and its other models as not used for that purpose; Ember is offered only in Standard mode. Anthropic offers US-only inference for Haiku 5.5 through the inference_geo parameter at 1.1 times the standard price, and thinking blocks from Haiku 5.5 work only in the account that produced them or in an account linked to it.
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WHAT META AND ANTHROPIC HAVE NOT PUBLISHED.
Several figures that buyers would use are missing from the official pages. Meta has not published a batch price, a cache-write price, rate limits, a knowledge cutoff, a country list, a system card or any independent speed measurement for Ember, and Meta's research blog post for the model returned a server error when fetched on November 10. Anthropic has not published a parameter count for Haiku 5.5, does not state rate limits on its model page, and does not give the effort setting for every result in the system card.
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MUSE EMBER OR CLAUDE HAIKU 5.5: WHICH ONE TO CHOOSE BY WORKLOAD.
The choice follows the shape of the workload more than the headline benchmark score, since both models sit at 36 on the Artificial Analysis Intelligence Index and charge the same base price. Anthropic's earlier move from Haiku 4.5 is covered in our report on the Claude Haiku 5.5 launch, and the table below maps common jobs to the model that the published prices and measurements favor.
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Workload | Recommended model | Reason |
Documents of 100K to 200K tokens | Muse Ember | Base rate up to 200K; Haiku 5.5 is 5x more expensive in that range |
Overnight batch classification | Claude Haiku 5.5 | 50% Batch discount; no Ember batch price published |
Agentic coding subtasks at max effort | Muse Ember | $0.14 vs $0.41 per AA task; leads Terminal-Bench 4.0 and Coding Agent Index |
Code changes judged by maintainer-merge criteria | Claude Haiku 5.5 | 34.9% vs 27.2% on FrontierCode 2.0 |
Audio, video or PDF inputs | Muse Ember | Native audio, video and PDF input |
Factual question answering | Claude Haiku 5.5 | 34.7% vs 21.1% AA-Omniscience accuracy |
AWS, Google Cloud or Azure procurement | Claude Haiku 5.5 | Bedrock, Google Cloud, Foundry, Claude Platform on AWS |
Very long outputs | Muse Ember | 524,288-token output limit vs 128K |
Regulated or safety-reviewed deployments | Claude Haiku 5.5 | Published system card and refusal classifiers |
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For a cost-sensitive agent pipeline that feeds long documents or recordings to a small worker model, Muse Ember is the cheaper and, on agentic coding tests, the stronger choice at the same list price. For a company that buys through a hyperscaler, runs large batch jobs or needs the documentation a compliance review asks for, Claude Haiku 5.5 is the better fit, and its FrontierCode 2.0 and AA-Omniscience leads make it the safer pick for code that must merge cleanly and for answers that must be factually right.
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