Muse Spark 1.1 vs Sakana Namazu: which should you choose in 2026?
Muse Spark 1.1 (by Meta) and Sakana Namazu (by Sakana AI) 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.
Sakana Namazu is about 1.2× cheaper on a blended 3:1 input/output basis ($1.71 vs $2.00 per 1M tokens).
Muse Spark 1.1 has the larger context window (1,048,576 tokens vs 262,144 tokens).
Choose Muse Spark 1.1 if…
- • you work with longer documents, transcripts, or codebases
Choose Sakana Namazu 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.
Performance benchmarks
Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.
| Benchmark | Muse Spark 1.1 | Sakana Namazu |
|---|---|---|
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 | 42.8% Artificial Analysis | — |
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 | — | — |
Finance Agent v2 A Vals AI benchmark with 927 expert-reviewed questions modeling the work of entry-level financial analysts. Task score | 57.2% Vals AI | — |
JobBench A professional tool-use benchmark with 65 tasks spanning 35 white-collar occupations. Task score | 54.7% JobBench | — |
MCP Atlas A scaled tool-use benchmark with 1,000 multi-step tasks across 36 real MCP servers and 220 tools. Tasks completed | 88.1%±1.9 Scale AI | — |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward | 80.8% Meta report | — |
A long-horizon software-engineering benchmark with 113 original tasks graded by hand-written tests. Pass@1 | 53.0%±3.0 DataCurve | — |
A contamination-resistant software-engineering benchmark with long-horizon tasks across multiple programming languages. Public and private splits are distinct protocols. Resolved | 61.5%±3.1 Scale AI | — |
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 | — | — |
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 | 80.0% Meta report | — |
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 | — | — |
The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry. Accuracy | — | — |
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 | 62.1% Meta report | — |
A contamination-resistant benchmark refreshed on a fixed release cadence. Scores from different LiveBench releases must never be compared as the same protocol. Mean of category averages | 75.3% LiveBench | — |
BabyVision A 388-question visual-understanding benchmark covering fine-grained discrimination, spatial perception, tracking, and pattern recognition. Accuracy | 76.3% Meta report | — |
The chart-reasoning portion of CharXiv, evaluating visual and mathematical reasoning over scientific figures. Accuracy | 88.4% Meta report | — |
| Arena preference scores | ||
Human preference score | 1487 ±6Preliminary | — |
Human preference score for code and web development | 1536 ±10Preliminary | — |
Benchmark sources
- Arena AI leaderboard
- AutomationBench-AA: Agentic SaaS Workflow Benchmark
- Finance Agent v2 Leaderboard
- JobBench Leaderboard
- MCP Atlas Leaderboard
- Muse Spark 1.1 Evaluation Report
- DeepSWE 1.1 Leaderboard
- SWE-Bench Pro Public Leaderboard
- LiveBench 2026-06-25 Leaderboard
Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 9, 2026.
Pricing, capabilities, and model facts
| Feature | Muse Spark 1.1 | Sakana Namazu |
|---|---|---|
| Context & model facts | ||
| Developer | Meta | Sakana AI |
| API provider | Meta | Sakana |
| Input context | 1,048,576 tokens | 262,144 tokens |
| Maximum output | 131,072 tokens | 65,536 tokens |
| Released | Jul 9, 2026 | — |
| Added to Writingmate | Jul 16, 2026 | Aug 11, 2026 |
| License | Proprietary | Not available |
| Knowledge cutoff | Not disclosed | — |
| Capabilities | ||
| Inputs | Text, Image, Video, File, Audio | Text, Image, File |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | Yes | Yes |
| Reasoning | Yes | Yes |
| Vision | Yes | Yes |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $1.25 | $0.95 |
| Output (per 1M tokens) | $4.25 | $4.00 |
| Blended 3:1 input/output | $2.00 | $1.71 |
| API performance | ||
| p95 latency | Not measured | Not measured |
| Output throughput | Not measured | Not 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
- Muse Spark 1.1 Evaluation Report
- Meta Model API provider documentation
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Aug 11, 2026.
Why Pay for Multiple Subscriptions?
Comparing Muse Spark 1.1 from Meta with Sakana Namazu from Sakana AI? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | Muse Spark 1.1 | Sakana Namazu | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Muse Spark 1.1 vs Sakana Namazu FAQ
Which is better, Muse Spark 1.1 or Sakana Namazu?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Sakana Namazu is about 1.2× cheaper on a blended 3:1 input/output basis ($1.71 vs $2.00 per 1M tokens). Muse Spark 1.1 has the larger context window (1,048,576 tokens vs 262,144 tokens).
Which model is cheaper to use through an API?
Sakana Namazu is about 1.2× cheaper on a blended 3:1 input/output basis ($1.71 vs $2.00 per 1M tokens).
Which model supports more context?
Muse Spark 1.1 has the larger context window (1,048,576 tokens vs 262,144 tokens).
Can I switch between Muse Spark 1.1 and Sakana Namazu?
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.
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