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Model Comparison

Ling 3.1 Flash vs MiniMax M2-herWhich Is Better in 2026?

inclusionAI•MiniMax: MiniMax M2-her logoMiniMax

Ling 3.1 Flash vs MiniMax M2-her: which should you choose in 2026?

Ling 3.1 Flash (by inclusionAI) and MiniMax M2-her (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.

Ling 3.1 Flash has no token charge in the catalog; the other model costs $0.525 per 1M tokens on a blended 3:1 input/output basis.

Ling 3.1 Flash has the larger context window (262,144 tokens vs 65,536 tokens).

Choose Ling 3.1 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

Choose MiniMax M2-her 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.

Ling 3.1 Flash and MiniMax M2-her benchmark results
BenchmarkLing 3.1 FlashMiniMax M2-her
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

——
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

    Ling 3.1 Flash and MiniMax M2-her model facts
    FeatureLing 3.1 FlashMiniMax M2-her
    Context & model facts
    DeveloperinclusionAIMiniMax
    API providerinclusionAIMiniMax
    Input context262,144 tokens65,536 tokens
    Maximum output32,768 tokens2,048 tokens
    Released——
    Added to WritingmateOct 2, 2026Jan 23, 2026
    LicenseNot availableNot available
    Knowledge cutoff——
    Capabilities
    InputsTextText
    OutputsTextText
    Provider endpoint accepts tool parametersYesNo
    ReasoningYesNo
    VisionNoNo
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.00$0.30
    Output (per 1M tokens)$0.00$1.20
    Blended 3:1 input/output$0.00$0.525
    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 2, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Ling 3.1 Flash from inclusionAI with MiniMax M2-her from MiniMax? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Ling 3.1 Flash and MiniMax M2-her
    PlanPriceLing 3.1 FlashMiniMax M2-herAI 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

    Ling 3.1 Flash vs MiniMax M2-her FAQ

    Which is better, Ling 3.1 Flash or MiniMax M2-her?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Ling 3.1 Flash has no token charge in the catalog; the other model costs $0.525 per 1M tokens on a blended 3:1 input/output basis. Ling 3.1 Flash has the larger context window (262,144 tokens vs 65,536 tokens).

    Which model is cheaper to use through an API?

    Ling 3.1 Flash has no token charge in the catalog; the other model costs $0.525 per 1M tokens on a blended 3:1 input/output basis.

    Which model supports more context?

    Ling 3.1 Flash has the larger context window (262,144 tokens vs 65,536 tokens).

    Can I switch between Ling 3.1 Flash and MiniMax M2-her?

    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.