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Model Comparison

Gemini 3.7 Flash vs Mistral Small 3.2 24BWhich Is Better in 2026?

Google: Gemini 3.7 Flash logoGoogle•Mistral: Mistral Small 3.2 24B logoMistral AI

Gemini 3.7 Flash vs Mistral Small 3.2 24B: which should you choose in 2026?

Gemini 3.7 Flash (by Google) and Mistral Small 3.2 24B (by Mistral 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.

Mistral Small 3.2 24B is about 7.1× cheaper on a blended 3:1 input/output basis ($0.10625 vs $0.75 per 1M tokens).

Gemini 3.7 Flash has the larger context window (1,048,576 tokens vs 131,072 tokens).

Choose Gemini 3.7 Flash if…

  • • you work with longer documents, transcripts, or codebases
  • • you need a model with explicit reasoning support

Choose Mistral Small 3.2 24B 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.

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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.

Gemini 3.7 Flash and Mistral Small 3.2 24B benchmark results
BenchmarkGemini 3.7 FlashMistral Small 3.2 24B
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

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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

    Gemini 3.7 Flash and Mistral Small 3.2 24B model facts
    FeatureGemini 3.7 FlashMistral Small 3.2 24B
    Context & model facts
    DeveloperGoogleMistral AI
    API providerGoogleMistral
    Input context1,048,576 tokens131,072 tokens
    Maximum output65,536 tokens16,384 tokens
    Released——
    Added to WritingmateAug 13, 2026Jun 20, 2025
    LicenseNot availableNot available
    Knowledge cutoff—2023-10-31
    Capabilities
    InputsText, Image, Video, File, AudioImage, Text
    OutputsTextText
    Provider endpoint accepts tool parametersYesYes
    ReasoningYesNo
    VisionYesYes
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.375$0.075
    Output (per 1M tokens)$1.88$0.20
    Blended 3:1 input/output$0.75$0.10625
    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 13, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Gemini 3.7 Flash from Google with Mistral Small 3.2 24B from Mistral AI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Gemini 3.7 Flash and Mistral Small 3.2 24B
    PlanPriceGemini 3.7 FlashMistral Small 3.2 24BAI 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

    Gemini 3.7 Flash vs Mistral Small 3.2 24B FAQ

    Which is better, Gemini 3.7 Flash or Mistral Small 3.2 24B?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Mistral Small 3.2 24B is about 7.1× cheaper on a blended 3:1 input/output basis ($0.10625 vs $0.75 per 1M tokens). Gemini 3.7 Flash has the larger context window (1,048,576 tokens vs 131,072 tokens).

    Which model is cheaper to use through an API?

    Mistral Small 3.2 24B is about 7.1× cheaper on a blended 3:1 input/output basis ($0.10625 vs $0.75 per 1M tokens).

    Which model supports more context?

    Gemini 3.7 Flash has the larger context window (1,048,576 tokens vs 131,072 tokens).

    Can I switch between Gemini 3.7 Flash and Mistral Small 3.2 24B?

    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.