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Model Comparison

Uncensored vs MiniMax M3.1 FlashWhich Is Better in 2026?

Cognitive Computations•MiniMax: MiniMax M3.1 Flash logoMiniMax

Uncensored vs MiniMax M3.1 Flash: which should you choose in 2026?

Uncensored (by Cognitive Computations) and MiniMax M3.1 Flash (by MiniMax) 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.

MiniMax M3.1 Flash is about 1.4× cheaper on a blended 3:1 input/output basis ($0.2625 vs $0.375 per 1M tokens).

MiniMax M3.1 Flash has the larger context window (1,048,576 tokens vs 128,000 tokens).

Choose Uncensored if…

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

Choose MiniMax M3.1 Flash 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.

Uncensored and MiniMax M3.1 Flash benchmark results
BenchmarkUncensoredMiniMax M3.1 Flash
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

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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

——
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

    Uncensored and MiniMax M3.1 Flash model facts
    FeatureUncensoredMiniMax M3.1 Flash
    Context & model facts
    DeveloperCognitive ComputationsMiniMax
    API providerVeniceMiniMax
    Input context128,000 tokens1,048,576 tokens
    Maximum output8,192 tokens524,288 tokens
    Released——
    Added to WritingmateJul 9, 2025Oct 19, 2026
    LicenseNot availableNot available
    Knowledge cutoff2024-04-30—
    Capabilities
    InputsTextText, Image, Video
    OutputsTextText
    Provider endpoint accepts tool parametersNoYes
    ReasoningNoYes
    VisionNoYes
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.20$0.15
    Output (per 1M tokens)$0.90$0.60
    Blended 3:1 input/output$0.375$0.2625
    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 19, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Uncensored from Cognitive Computations with MiniMax M3.1 Flash from MiniMax? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Uncensored and MiniMax M3.1 Flash
    PlanPriceUncensoredMiniMax M3.1 FlashAI 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

    Uncensored vs MiniMax M3.1 Flash FAQ

    Which is better, Uncensored or MiniMax M3.1 Flash?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. MiniMax M3.1 Flash is about 1.4× cheaper on a blended 3:1 input/output basis ($0.2625 vs $0.375 per 1M tokens). MiniMax M3.1 Flash has the larger context window (1,048,576 tokens vs 128,000 tokens).

    Which model is cheaper to use through an API?

    MiniMax M3.1 Flash is about 1.4× cheaper on a blended 3:1 input/output basis ($0.2625 vs $0.375 per 1M tokens).

    Which model supports more context?

    MiniMax M3.1 Flash has the larger context window (1,048,576 tokens vs 128,000 tokens).

    Can I switch between Uncensored and MiniMax M3.1 Flash?

    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.