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Model Comparison

Mistral Nemo vs Moondream 3.1Which Is Better in 2026?

Mistral: Mistral Nemo logoMistral AI•
Moondream

Mistral Nemo vs Moondream 3.1: which should you choose in 2026?

Mistral Nemo (by Mistral AI) and Moondream 3.1 (by Moondream) 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.

Mistral Nemo is about 29.1× cheaper on a blended 3:1 input/output basis ($0.02925 vs $0.85 per 1M tokens).

Mistral Nemo has the larger context window (131,072 tokens vs 32,000 tokens).

Choose Mistral Nemo if…

  • • lower blended API cost matters for your workload
  • • you work with longer documents, transcripts, or codebases

Choose Moondream 3.1 if…

  • • 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 Nemo and Moondream 3.1 benchmark results
BenchmarkMistral NemoMoondream 3.1
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

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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

——

Benchmark sources

    Reviewed evidence last updated 2026-08-09.

    Pricing, capabilities, and model facts

    Mistral Nemo and Moondream 3.1 model facts
    FeatureMistral NemoMoondream 3.1
    Context & model facts
    DeveloperMistral AIMoondream
    API providerMistralMoondream
    Input context131,072 tokens32,000 tokens
    Maximum output16,384 tokens4,096 tokens
    Released——
    Added to WritingmateJul 19, 2024Oct 15, 2026
    LicenseNot availableNot available
    Knowledge cutoff2024-04-30—
    Capabilities
    InputsTextText, Image
    OutputsTextText
    Provider endpoint accepts tool parametersYesNo
    ReasoningNoYes
    VisionNoYes
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.029$0.30
    Output (per 1M tokens)$0.03$2.50
    Blended 3:1 input/output$0.02925$0.85
    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 15, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Mistral Nemo from Mistral AI with Moondream 3.1 from Moondream? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Mistral Nemo and Moondream 3.1
    PlanPriceMistral NemoMoondream 3.1AI ImagesAI Video
    Writingmate Pro
    Most popular
    		$20/moIncludedIncludedNano 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

    Mistral Nemo vs Moondream 3.1 FAQ

    Which is better, Mistral Nemo or Moondream 3.1?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Mistral Nemo is about 29.1× cheaper on a blended 3:1 input/output basis ($0.02925 vs $0.85 per 1M tokens). Mistral Nemo has the larger context window (131,072 tokens vs 32,000 tokens).

    Which model is cheaper to use through an API?

    Mistral Nemo is about 29.1× cheaper on a blended 3:1 input/output basis ($0.02925 vs $0.85 per 1M tokens).

    Which model supports more context?

    Mistral Nemo has the larger context window (131,072 tokens vs 32,000 tokens).

    Can I switch between Mistral Nemo and Moondream 3.1?

    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.