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Model Comparison

Muse Wonder vs o3Which Is Better in 2026?

Meta: Muse Wonder logoMeta•OpenAI: o3 logoOpenAI

Muse Wonder vs o3: which should you choose in 2026?

Muse Wonder (by Meta) and o3 (by OpenAI) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.

There are not enough shared, protocol-compatible benchmark results to declare a performance leader.

o3 is about 2.3× cheaper on a blended 3:1 input/output basis ($3.50 vs $8.00 per 1M tokens).

Muse Wonder has the larger context window (1,048,576 tokens vs 200,000 tokens).

Choose Muse Wonder if…

  • • you work with longer documents, transcripts, or codebases

Choose o3 if…

  • • lower blended API cost matters for your workload

The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.

Chat with both models
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Performance benchmarks

Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.

Muse Wonder and o3 benchmark results
BenchmarkMuse Wondero3
AutomationBench-AA

A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework.

Guardrail-aware score

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BrowseComp

A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing.

Accuracy

——
OSWorld-Verified

A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state.

Mean task reward

—
23.0%
OSWorld
SWE-Bench Verified

A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests.

Resolved

—
58.4%
SWE-bench
Terminal-Bench 2.1

Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol.

Mean task success

——
AutoBench

A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench.

Weighted peer-assessment score

——
GPQA Diamond

The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry.

Accuracy

——
Humanity's Last Exam

A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged.

Accuracy

——
Artificial Analysis Intelligence Index

Comprehensive comparison of AI models across intelligence, price, speed, and latency

Intelligence Index

—
65
artificialanalysis.ai

Benchmark sources

Reviewed evidence last updated 2026-08-09.

Pricing, capabilities, and model facts

Muse Wonder and o3 model facts
FeatureMuse Wondero3
Context & model facts
DeveloperMetaOpenAI
API providerMetaOpenAI
Input context1,048,576 tokens200,000 tokens
Maximum output943,718 tokens100,000 tokens
Released——
Added to WritingmateOct 12, 2026Apr 16, 2025
LicenseNot availableNot available
Knowledge cutoff—2024-06-30
Capabilities
InputsText, Image, Video, File, AudioImage, Text, File
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningYesYes
VisionYesYes
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$4.00$2.00
Output (per 1M tokens)$20.00$8.00
Blended 3:1 input/output$8.00$3.50
API performance
p95 latencyNot measuredNot measured
Output throughputNot measuredNot measured
Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table.

Data sources

  • Writingmate model catalog (pricing, limits, and availability)

Catalog data last updated Oct 12, 2026.

Why Pay for Multiple Subscriptions?

Comparing Muse Wonder from Meta with o3 from OpenAI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Muse Wonder and o3
PlanPriceMuse Wondero3AI ImagesAI Video
Writingmate Pro
Most popular
$20/moUltimate requiredIncludedNano Banana Pro, FLUX.2, DALL-E & moreVEO 3.1, Kling 3.0
Writingmate Ultimate
Power users
$60/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreVEO 3.1, Kling 3.0
View Plans & Pricing

Muse Wonder vs o3 FAQ

Which is better, Muse Wonder or o3?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. o3 is about 2.3× cheaper on a blended 3:1 input/output basis ($3.50 vs $8.00 per 1M tokens). Muse Wonder has the larger context window (1,048,576 tokens vs 200,000 tokens).

Which model is cheaper to use through an API?

o3 is about 2.3× cheaper on a blended 3:1 input/output basis ($3.50 vs $8.00 per 1M tokens).

Which model supports more context?

Muse Wonder has the larger context window (1,048,576 tokens vs 200,000 tokens).

Can I switch between Muse Wonder and o3?

Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.