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Model Comparison

Gemini 3.7 Flash vs Ling-2.6-flashWhich Is Better in 2026?

Google: Gemini 3.7 Flash logoGoogle•
inclusionAI

Gemini 3.7 Flash vs Ling-2.6-flash: which should you choose in 2026?

Gemini 3.7 Flash (by Google) and Ling-2.6-flash (by inclusionAI) 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-2.6-flash is about 50.0× cheaper on a blended 3:1 input/output basis ($0.015 vs $0.75 per 1M tokens).

Gemini 3.7 Flash has the larger context window (1,048,576 tokens vs 262,144 tokens).

Choose Gemini 3.7 Flash if…

  • • you work with longer documents, transcripts, or codebases
  • • you need a model with explicit reasoning support
  • • you need image inputs

Choose Ling-2.6-flash if…

  • • lower blended API cost matters for your workload

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.

Gemini 3.7 Flash and Ling-2.6-flash benchmark results
BenchmarkGemini 3.7 FlashLing-2.6-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

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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

——
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

——
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

    Gemini 3.7 Flash and Ling-2.6-flash model facts
    FeatureGemini 3.7 FlashLing-2.6-flash
    Context & model facts
    DeveloperGoogleinclusionAI
    API providerGoogleinclusionAI
    Input context1,048,576 tokens262,144 tokens
    Maximum output65,536 tokens32,768 tokens
    Released——
    Added to WritingmateAug 13, 2026Apr 21, 2026
    LicenseNot availableNot available
    Knowledge cutoff——
    Capabilities
    InputsText, Image, Video, File, AudioText
    OutputsTextText
    Provider endpoint accepts tool parametersYesYes
    ReasoningYesNo
    VisionYesNo
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.375$0.01
    Output (per 1M tokens)$1.88$0.03
    Blended 3:1 input/output$0.75$0.015
    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 13, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Gemini 3.7 Flash from Google with Ling-2.6-flash from inclusionAI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Gemini 3.7 Flash and Ling-2.6-flash
    PlanPriceGemini 3.7 FlashLing-2.6-flashAI 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

    Gemini 3.7 Flash vs Ling-2.6-flash FAQ

    Which is better, Gemini 3.7 Flash or Ling-2.6-flash?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Ling-2.6-flash is about 50.0× cheaper on a blended 3:1 input/output basis ($0.015 vs $0.75 per 1M tokens). Gemini 3.7 Flash has the larger context window (1,048,576 tokens vs 262,144 tokens).

    Which model is cheaper to use through an API?

    Ling-2.6-flash is about 50.0× cheaper on a blended 3:1 input/output basis ($0.015 vs $0.75 per 1M tokens).

    Which model supports more context?

    Gemini 3.7 Flash has the larger context window (1,048,576 tokens vs 262,144 tokens).

    Can I switch between Gemini 3.7 Flash and Ling-2.6-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.