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Model Comparison

Granite 4.2 8B vs Mixtral 8x22B InstructWhich Is Better in 2026?

IBM Granite•Mistral: Mixtral 8x22B Instruct logoMistral AI

Granite 4.2 8B vs Mixtral 8x22B Instruct: which should you choose in 2026?

Granite 4.2 8B (by IBM Granite) and Mixtral 8x22B Instruct (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.

Granite 4.2 8B is about 26.7× cheaper on a blended 3:1 input/output basis ($0.1125 vs $3.00 per 1M tokens).

Granite 4.2 8B has the larger context window (131,072 tokens vs 65,536 tokens).

Choose Granite 4.2 8B 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

Choose Mixtral 8x22B Instruct if…

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

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.

Granite 4.2 8B and Mixtral 8x22B Instruct benchmark results
BenchmarkGranite 4.2 8BMixtral 8x22B Instruct
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

——
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

    Granite 4.2 8B and Mixtral 8x22B Instruct model facts
    FeatureGranite 4.2 8BMixtral 8x22B Instruct
    Context & model facts
    DeveloperIBM GraniteMistral AI
    API providerIBMMistral
    Input context131,072 tokens65,536 tokens
    Maximum output117,964 tokens52,428 tokens
    Released——
    Added to WritingmateAug 31, 2026Apr 17, 2024
    LicenseNot availableNot available
    Knowledge cutoff—2024-01-31
    Capabilities
    InputsTextText, File
    OutputsTextText
    Provider endpoint accepts tool parametersYesYes
    ReasoningYesNo
    VisionNoNo
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.10$2.00
    Output (per 1M tokens)$0.15$6.00
    Blended 3:1 input/output$0.1125$3.00
    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 31, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Granite 4.2 8B from IBM Granite with Mixtral 8x22B Instruct from Mistral AI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Granite 4.2 8B and Mixtral 8x22B Instruct
    PlanPriceGranite 4.2 8BMixtral 8x22B InstructAI 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

    Granite 4.2 8B vs Mixtral 8x22B Instruct FAQ

    Which is better, Granite 4.2 8B or Mixtral 8x22B Instruct?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Granite 4.2 8B is about 26.7× cheaper on a blended 3:1 input/output basis ($0.1125 vs $3.00 per 1M tokens). Granite 4.2 8B has the larger context window (131,072 tokens vs 65,536 tokens).

    Which model is cheaper to use through an API?

    Granite 4.2 8B is about 26.7× cheaper on a blended 3:1 input/output basis ($0.1125 vs $3.00 per 1M tokens).

    Which model supports more context?

    Granite 4.2 8B has the larger context window (131,072 tokens vs 65,536 tokens).

    Can I switch between Granite 4.2 8B and Mixtral 8x22B Instruct?

    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.