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Model Comparison

Mistral Large 2407 vs Sakana NamazuWhich Is Better in 2026?

Mistral Large 2407 logoMistral AI•
Sakana AI

Mistral Large 2407 vs Sakana Namazu: which should you choose in 2026?

Mistral Large 2407 (by Mistral AI) 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 1.8× cheaper on a blended 3:1 input/output basis ($1.71 vs $3.00 per 1M tokens).

Sakana Namazu has the larger context window (262,144 tokens vs 131,072 tokens).

Choose Mistral Large 2407 if…

  • • your own prompt tests favor its output; shared comparable evidence does not identify a unique advantage

Choose Sakana Namazu if…

  • • lower blended API cost matters for your workload
  • • you work with longer documents, transcripts, or codebases
  • • you need a model with explicit reasoning support
  • • you need image inputs

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.

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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.

Mistral Large 2407 and Sakana Namazu benchmark results
BenchmarkMistral Large 2407Sakana Namazu
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

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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

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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

——
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

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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

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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

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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

——
Arena preference scores
Arena (Text)

Human preference score

1314 ±4
—

Benchmark sources

Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 23, 2026.

Pricing, capabilities, and model facts

Mistral Large 2407 and Sakana Namazu model facts
FeatureMistral Large 2407Sakana Namazu
Context & model facts
DeveloperMistral AISakana AI
API providerMistral Large 2407Sakana
Input context131,072 tokens262,144 tokens
Maximum output—65,536 tokens
Released——
Added to WritingmateNov 19, 2024Aug 11, 2026
LicenseNot availableNot available
Knowledge cutoff2024-03-31—
Capabilities
InputsText, FileText, Image, File
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningNoYes
VisionNoYes
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$2.00$0.95
Output (per 1M tokens)$6.00$4.00
Blended 3:1 input/output$3.00$1.71
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 Aug 11, 2026.

Why Pay for Multiple Subscriptions?

Comparing Mistral Large 2407 from Mistral AI with Sakana Namazu from Sakana AI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Mistral Large 2407 and Sakana Namazu
PlanPriceMistral Large 2407Sakana NamazuAI ImagesAI Video
Writingmate Pro
Most popular
$20/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreSora 2, VEO 3.1
Writingmate Ultimate
Power users
$60/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreSora 2, VEO 3.1
View Plans & Pricing

Mistral Large 2407 vs Sakana Namazu FAQ

Which is better, Mistral Large 2407 or Sakana Namazu?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Sakana Namazu is about 1.8× cheaper on a blended 3:1 input/output basis ($1.71 vs $3.00 per 1M tokens). Sakana Namazu has the larger context window (262,144 tokens vs 131,072 tokens).

Which model is cheaper to use through an API?

Sakana Namazu is about 1.8× cheaper on a blended 3:1 input/output basis ($1.71 vs $3.00 per 1M tokens).

Which model supports more context?

Sakana Namazu has the larger context window (262,144 tokens vs 131,072 tokens).

Can I switch between Mistral Large 2407 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.