Meta Muse Ember Matches Claude Haiku 5.5 at One-Third the Cost for Agent Work

Meta Superintelligence Labs released Muse Ember on November 9, 2026, a compact AI model that matches Anthropic's Claude Haiku 5.5 on the Artificial Analysis Intelligence Index while costing roughly one-third as much per executed task. At $0.14 per task versus Haiku 5.5's $0.41, the gap is large enough to materially change the economics of agent workflows at scale — provided developers can accept Ember's weaker performance on complex real-world engineering work.

Ember is available through the Meta Model API's Responses API and within Muse Code, Meta's coding-focused interface. Pricing is $0.10 per million input tokens, $0.004 per million cached input tokens, and $0.50 per million output tokens. Its context window reaches one million tokens with a maximum output of 512,000 tokens per call — specifications that position it well above most flash-tier models in raw capacity.

Muse Ember Is the First Muse Model Trained on Wonder Full Outputs

Meta's announcement describes Ember as "trained using Muse Wonder Full outputs," making it the first model in the Muse family produced through distillation rather than trained independently from scratch.

In model distillation, a smaller "student" model learns by training on the output distribution of a larger, more capable "teacher" model, in this case Muse Wonder Full — the larger of the two Wonder models Meta released in October 2026. The student network does not necessarily replicate the teacher's internal representations, but it learns to reproduce its outputs with far fewer parameters and far lower inference cost. The result is a model that captures significant capability from its teacher without inheriting the teacher's computational demands.

Meta has not disclosed Ember's parameter count, model architecture family, or the specific details of the training procedure. Whether Ember uses a transformer, a mixture-of-experts design, or another architecture is not stated in publicly available material. The same applies to its inference infrastructure requirements — batch size characteristics, memory footprint, and hardware preferences are absent from Meta's announcement. This opacity is consistent with how Meta handled earlier Muse models but leaves developers unable to independently evaluate deployment costs beyond the per-token API price.

What is clear from the product strategy is the layered intent. Wonder Full plans and reasons; Ember executes at volume. Meta frames Ember as the right model for high-frequency agent subtasks — structured data extraction, document analysis, multi-step coding workflows, and business process automation — while Wonder handles the orchestration layer. This mirrors the hierarchical model design that has become standard across the industry, where a frontier reasoning model handles decomposition and a cheaper model handles the resulting sequence of subtasks. The November 6, 2026 rollout of Wonder-distilled models to consumer Muse and Meta AI users, three days before Ember's API launch, suggests the two releases were coordinated as part of a deliberate tiering strategy rather than separate engineering timelines.

Benchmark Scores Are Selectively Strong — and One Category Is Openly Weak

Ember's benchmark results are a mix of genuine competitive leads and an acknowledged shortcoming that matters for certain use cases.

On AutomationBench-AA, which tests strict pass@1 accuracy on structured business workflow automation tasks, Ember scores 37.8% against Claude Haiku 5.5's 13.9% and GPT-6 Luna's 20.2% — more than double the nearest competitor. On the Agents' Last Exam Linux subset, which tests multi-step shell and system administration tasks, Ember scores 61.6% versus 57.0% for Haiku 5.5 and 57.1% for Luna. On Terminal-Bench 4.0, Ember scores 40.9%, narrowly ahead of Haiku 5.5's 39.2% and significantly ahead of Luna's 16.4%. On the Coding Agent Index-AA, a harness-dependent composite, Ember scores 46 against Haiku 5.5's 37 and Luna's 31. On OSWorld 2.1, which measures visual computer use with partial credit, Ember scores 69.5% against Haiku 5.5's 68.9% and Luna's 50.5%. These are Meta's internal evaluation results and have not been independently reproduced by third parties.

The exception is FrontierCode 2.0-AA, which tests performance on real GitHub engineering tasks drawn from production repositories. Here Ember scores 27.2%, trailing Claude Haiku 5.5's 34.9% by 7.7 percentage points. Meta's announcement materials do not explain the gap. FrontierCode is widely regarded as a harder signal than synthetic coding benchmarks because the tasks involve genuine codebase navigation, bug reproduction, and patch generation against real test suites. For teams whose agent workflows depend heavily on autonomous code modification of complex existing repositories, this result is a meaningful consideration.

The Artificial Analysis Intelligence Index, the one benchmark in this dataset compiled independently rather than by Meta, places Ember at 36 — tied with Claude Haiku 5.5 at 36.2. This is the most credibly comparable figure across the two models, and it anchors the core cost-per-intelligence argument.

Ember's weakest published result comes from AA Omniscience, Artificial Analysis's factual recall evaluation: Ember scores 21.1%, declines to answer 64% of questions, and among the questions it does attempt, produces incorrect answers approximately 17.4% of the time. For agentic workflows that depend on factual recall without retrieval — where the model is expected to know something without looking it up — this performance makes Ember unreliable. Meta's recommended architecture, using Ember as an execution layer rather than a knowledge source, mitigates but does not eliminate this risk in practice.

The Two-Layer Agent Architecture Meta Is Selling

Three of Meta's named early testers provided accounts of Ember's performance in production-like settings at launch.

Dane Knecht, Cloudflare's CTO, reported tail latency improvements of up to 30% compared to similarly positioned models in internal testing, citing Ember's handling of multi-repository code investigations as a specific use case. Knecht said "what stood out in our testing was speed."

Chak Pothina, inference lead at You.com, reported a p95 latency of 2.2 seconds and 36 to 48 percent fewer output tokens compared to competing models on coding tasks, which translates to meaningful cost reduction in token-priced deployments. Pothina said Ember "completed every coding quest in our testing with the lowest latency of any model we tested."

Saoud Rizwan, founder of Cline, an agentic coding tool, said Ember "is purpose-built for, and excels at, subagent work like coding and document analysis."

These are company-reported testimonials collected at launch and have not been independently audited. They are consistent with Ember's strong AutomationBench and Terminal-Bench results but do not substitute for independent evaluation.

The economic argument becomes clearest at scale. An agent session that makes 100 model calls — plausible for a multi-step automation task — costs $14 at Ember's $0.14-per-task rate and $41 at Haiku 5.5's rate. At 10,000 sessions per month, the difference is $270,000. For companies running agent infrastructure at that volume, the choice of execution-tier model becomes a material operational cost decision.

DeepSeek Flash and Regional Alternatives Offer Different Trade-offs

The flash-tier competitive landscape now includes at least four credible options at different points on the intelligence-cost curve.

DeepSeek V4.1 Flash scores similarly to Ember on the Artificial Analysis Intelligence Index but costs approximately $0.56 per task — roughly four times Ember's price. GLM-5.3-Flash offers a similar cost-to-score profile but is not widely available on enterprise API platforms in North American markets. GPT-6 Luna scores 23 to 28 points on the AA Index depending on reasoning setting — meaningfully below Ember's 36 — at $0.15 per task, comparable to Ember's cost but with weaker performance on the agent-focused benchmarks.

Muse Wonder itself, Ember's teacher model, scores 44 on the AA Index — eight points above Ember — but at an estimated task cost more than 50 times higher. The Wonder/Ember combination is therefore the layered-cost bet: pay Wonder pricing for planning steps and Ember pricing for execution steps, with the gap between 44 and 36 representing the intelligence cost of distillation.

Muse Glimmer, the 30-billion-parameter open-weight model Meta released in August 2026, offers a different trade-off: developers who need to self-host or fine-tune can use Glimmer without paying API inference costs, but it lacks the agentic benchmark performance Ember demonstrates on AutomationBench-AA and the Coding Agent Index. Ember does not offer open weights.

Anthropic's Claude Haiku 5.5 remains the most direct like-for-like competitor: same AA Intelligence Index score, strong FrontierCode performance where Ember trails, but roughly three times the per-task cost. The choice between them is effectively a trade-off between cost efficiency (Ember), code-heavy agent tasks (Haiku 5.5), and whether the missing 7.7 percentage points on FrontierCode matter for a given workflow.

What Meta Has Not Said About Muse Ember

The absence of several standard pieces of technical information is notable for a model positioned for enterprise agent deployment.

Meta has not published Ember's parameter count, which makes it impossible to independently estimate hardware requirements for hypothetical on-premise deployments or to place Ember in the broader landscape of model sizes. The comparison to Haiku 5.5 is calibrated entirely by benchmark results and pricing, not by architectural similarity.

Meta has not published fine-tuning availability. The absence of a fine-tuning API means developers cannot adapt Ember to domain-specific tasks or adjust its behavior using their own data, a capability that has become standard for enterprise AI deployments.

Meta's safety evaluation states that Ember was assessed under the Superintelligence Scaling Framework and falls below high-risk thresholds across the evaluated categories. No methodology, red-teaming details, or category definitions are publicly available, making it impossible to independently evaluate the claim.

The factual recall weakness — a 64% question decline rate and 17.4% error rate on attempted answers in AA Omniscience — is not addressed in Meta's materials. For retrieval-augmented agent architectures where Ember only needs to process already-retrieved content, this limitation matters less. For workflows where the model's own parametric knowledge is part of the execution logic, it matters considerably more.

The Muse Ecosystem and What the Ember Launch Reveals

The Muse product stack now spans four model tiers: Muse Spark (April 2026) established the lab's presence; Muse Wonder and Wonder Full (October 2026) established the frontier reasoning tier; and Ember now fills the high-volume execution slot.

The November 6 distribution of Wonder-distilled models to consumer Muse and Meta AI users followed by the November 9 Ember API launch represents a coordinated rollout: distillation technology enters mass-market consumer products three days before it appears as an enterprise API product, compressing the product cycle in a way that lets Meta market consumer AI improvements and enterprise API pricing simultaneously.

The 10 million Muse personal agent setups Meta reported in October 2026 represent the deployment surface on which this layered architecture runs. If even a fraction of those sessions use Wonder for orchestration and Ember-class models for execution, the volume of Ember-equivalent inference is already enormous — which may explain why Meta's pricing positions Ember below its nearest benchmark competitor rather than at parity.

The next meaningful milestones for the Muse ecosystem are the Japan and Singapore consumer agent expansion, which will add regional data-handling constraints to the platform's operational profile, and any third-party independent replication of Ember's key benchmark claims — particularly AutomationBench-AA, where the gap over Haiku 5.5 is large enough to change deployment decisions if confirmed. The FrontierCode gap is the signal to watch for teams with complex codebase agent workflows: a future Muse model that closes that gap at Ember's price point would materially shift the competitive balance in AI coding infrastructure.