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Model Comparison

Mixtral 8x22B Instruct vs Grok 4.6Which Is Better in 2026?

Mistral: Mixtral 8x22B Instruct logoMistral AI•SpaceXAI: Grok 4.6 logoxAI

Mixtral 8x22B Instruct vs Grok 4.6: which should you choose in 2026?

Mixtral 8x22B Instruct (by Mistral AI) and Grok 4.6 (by xAI) 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.

At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.

Grok 4.6 has the larger context window (500,000 tokens vs 65,536 tokens).

Choose Mixtral 8x22B Instruct if…

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

Choose Grok 4.6 if…

  • • 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.

Mixtral 8x22B Instruct and Grok 4.6 benchmark results
BenchmarkMixtral 8x22B InstructGrok 4.6
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

——
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

——
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

    Mixtral 8x22B Instruct and Grok 4.6 model facts
    FeatureMixtral 8x22B InstructGrok 4.6
    Context & model facts
    DeveloperMistral AIxAI
    API providerMistralSpaceXAI
    Input context65,536 tokens500,000 tokens
    Maximum output52,428 tokens450,000 tokens
    Released——
    Added to WritingmateApr 17, 2024Aug 12, 2026
    LicenseNot availableNot available
    Knowledge cutoff2024-01-31—
    Capabilities
    InputsText, FileText, Image, File
    OutputsTextText
    Provider endpoint accepts tool parametersYesYes
    ReasoningNoYes
    VisionNoYes
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Base input (per 1M tokens)$2.00$2.00
    Base output (per 1M tokens)$6.00$6.00
    Higher-context pricing tiersBase rate only
    • ≥200,000 prompt tokens$4.00 input · $12.00 output per 1M
    Price-comparison caveatAt least one model changes token rates above a prompt-token threshold. Base rates are shown, but an unconditional blended price ratio would not be like-for-like.
    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 12, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Mixtral 8x22B Instruct from Mistral AI with Grok 4.6 from xAI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Mixtral 8x22B Instruct and Grok 4.6
    PlanPriceMixtral 8x22B InstructGrok 4.6AI 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

    Mixtral 8x22B Instruct vs Grok 4.6 FAQ

    Which is better, Mixtral 8x22B Instruct or Grok 4.6?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison. Grok 4.6 has the larger context window (500,000 tokens vs 65,536 tokens).

    Which model is cheaper to use through an API?

    At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.

    Which model supports more context?

    Grok 4.6 has the larger context window (500,000 tokens vs 65,536 tokens).

    Can I switch between Mixtral 8x22B Instruct and Grok 4.6?

    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.