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Model Comparison

Mercury 2.5 vs Qwen3 235B A22B Instruct 2507Which Is Better in 2026?

Inception•Qwen: Qwen3 235B A22B Instruct 2507 logoQwen

Mercury 2.5 vs Qwen3 235B A22B Instruct 2507: which should you choose in 2026?

Mercury 2.5 (by Inception) and Qwen3 235B A22B Instruct 2507 (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.

Mercury 2.5 is about 2.3× cheaper on a blended 3:1 input/output basis ($0.0675 vs $0.153125 per 1M tokens).

Choose Mercury 2.5 if…

  • • lower blended API cost matters for your workload
  • • you need a model with explicit reasoning support

Choose Qwen3 235B A22B Instruct 2507 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.

Mercury 2.5 and Qwen3 235B A22B Instruct 2507 benchmark results
BenchmarkMercury 2.5Qwen3 235B A22B Instruct 2507
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

    Mercury 2.5 and Qwen3 235B A22B Instruct 2507 model facts
    FeatureMercury 2.5Qwen3 235B A22B Instruct 2507
    Context & model facts
    DeveloperInceptionQwen
    API providerInceptionQwen
    Input context260,000 tokens262,144 tokens
    Maximum output65,536 tokens235,929 tokens
    Released——
    Added to WritingmateSep 8, 2026Jul 21, 2025
    LicenseNot availableNot available
    Knowledge cutoff—2025-06-30
    Capabilities
    InputsTextText
    OutputsTextText
    Provider endpoint accepts tool parametersYesYes
    ReasoningYesNo
    VisionNoNo
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.04$0.0875
    Output (per 1M tokens)$0.15$0.35
    Blended 3:1 input/output$0.0675$0.153125
    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 8, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Mercury 2.5 from Inception with Qwen3 235B A22B Instruct 2507 from Qwen? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Mercury 2.5 and Qwen3 235B A22B Instruct 2507
    PlanPriceMercury 2.5Qwen3 235B A22B Instruct 2507AI 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

    Mercury 2.5 vs Qwen3 235B A22B Instruct 2507 FAQ

    Which is better, Mercury 2.5 or Qwen3 235B A22B Instruct 2507?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Mercury 2.5 is about 2.3× cheaper on a blended 3:1 input/output basis ($0.0675 vs $0.153125 per 1M tokens).

    Which model is cheaper to use through an API?

    Mercury 2.5 is about 2.3× cheaper on a blended 3:1 input/output basis ($0.0675 vs $0.153125 per 1M tokens).

    Which model supports more context?

    The published context windows are close enough that this page does not treat the difference as a material advantage.

    Can I switch between Mercury 2.5 and Qwen3 235B A22B Instruct 2507?

    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.