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Model Comparison

Qwen3.7 Flash vs Qwen3.8 27BWhich Is Better in 2026?

Qwen: Qwen3.7 Flash logoQwen•Qwen: Qwen3.8 27B logoQwen

Qwen3.7 Flash vs Qwen3.8 27B: which should you choose in 2026?

Qwen3.7 Flash (by Qwen) and Qwen3.8 27B (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.

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

Qwen3.7 Flash has the larger context window (1,000,000 tokens vs 262,144 tokens).

Choose Qwen3.7 Flash if…

  • • you work with longer documents, transcripts, or codebases

Choose Qwen3.8 27B 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.

Qwen3.7 Flash and Qwen3.8 27B benchmark results
BenchmarkQwen3.7 FlashQwen3.8 27B
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

    Qwen3.7 Flash and Qwen3.8 27B model facts
    FeatureQwen3.7 FlashQwen3.8 27B
    Context & model facts
    DeveloperQwenQwen
    API providerQwenQwen
    Input context1,000,000 tokens262,144 tokens
    Maximum output65,536 tokens131,072 tokens
    Released——
    Added to WritingmateJul 27, 2026Aug 14, 2026
    LicenseNot availableNot available
    Knowledge cutoff——
    Capabilities
    InputsText, Image, VideoText, Image, Video
    OutputsTextText
    Provider endpoint accepts tool parametersYesYes
    ReasoningYesYes
    VisionYesYes
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Base input (per 1M tokens)$0.03$0.45
    Base output (per 1M tokens)$0.13$3.20
    Higher-context pricing tiers
    • ≥32,000 prompt tokens$0.10 input · $0.40 output per 1M
    • ≥256,000 prompt tokens$0.20 input · $0.80 output per 1M
    		Base rate only
    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 14, 2026.

    Qwen3.7 Flash vs Qwen3.8 27B FAQ

    Which is better, Qwen3.7 Flash or Qwen3.8 27B?

    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. Qwen3.7 Flash has the larger context window (1,000,000 tokens vs 262,144 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?

    Qwen3.7 Flash has the larger context window (1,000,000 tokens vs 262,144 tokens).

    Can I switch between Qwen3.7 Flash and Qwen3.8 27B?

    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.