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Model Comparison

Ming Image 0.1 Design Layer vs Qwen3.8 Max PrimeWhich Is Better in 2026?

inclusionAI•Qwen: Qwen3.8 Max Prime logoQwen

Ming Image 0.1 Design Layer vs Qwen3.8 Max Prime: which should you choose in 2026?

Ming Image 0.1 Design Layer (by inclusionAI) and Qwen3.8 Max Prime (by Qwen) 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.

Choose Ming Image 0.1 Design Layer if…

  • • you need image generation

Choose Qwen3.8 Max Prime if…

  • • you need a model with explicit reasoning support

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.

Ming Image 0.1 Design Layer and Qwen3.8 Max Prime benchmark results
BenchmarkMing Image 0.1 Design LayerQwen3.8 Max Prime
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

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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

    Ming Image 0.1 Design Layer and Qwen3.8 Max Prime model facts
    FeatureMing Image 0.1 Design LayerQwen3.8 Max Prime
    Context & model facts
    DeveloperinclusionAIQwen
    API providerinclusionAIQwen
    Input context—1,000,000 tokens
    Maximum output—131,072 tokens
    Released——
    Added to WritingmateSep 23, 2026Sep 23, 2026
    LicenseNot availableNot available
    Knowledge cutoff——
    Capabilities
    InputsText, ImageText, Image, Video
    OutputsImageText
    Provider endpoint accepts tool parametersNoYes
    ReasoningNoYes
    VisionYesYes
    Image GenerationYesNo
    Video GenerationNoNo
    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 Sep 23, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Ming Image 0.1 Design Layer from inclusionAI with Qwen3.8 Max Prime from Qwen? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Ming Image 0.1 Design Layer and Qwen3.8 Max Prime
    PlanPriceMing Image 0.1 Design LayerQwen3.8 Max PrimeAI ImagesAI Video
    Writingmate Pro
    Most popular
    		$20/moIncludedUltimate requiredNano 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

    Ming Image 0.1 Design Layer vs Qwen3.8 Max Prime FAQ

    Which is better, Ming Image 0.1 Design Layer or Qwen3.8 Max Prime?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader.

    Which model is cheaper to use through an API?

    A comparable blended token price is not available for both models. Check the API pricing rows for the values that are currently published.

    Which model supports more context?

    Comparable context-window data is not available for both models.

    Can I switch between Ming Image 0.1 Design Layer and Qwen3.8 Max Prime?

    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.