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Model Comparison

Ling 3.0 Flash VL vs Fugu MaxWhich Is Better in 2026?

inclusionAI•
Sakana AI

Ling 3.0 Flash VL vs Fugu Max: which should you choose in 2026?

Ling 3.0 Flash VL (by inclusionAI) and Fugu Max (by Sakana AI) 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 3.0 Flash VL is about 33.3× cheaper on a blended 3:1 input/output basis ($0.09 vs $3.00 per 1M tokens).

Fugu Max has the larger context window (1,000,000 tokens vs 131,072 tokens).

Choose Ling 3.0 Flash VL if…

  • • lower blended API cost matters for your workload

Choose Fugu Max if…

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

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 3.0 Flash VL and Fugu Max benchmark results
BenchmarkLing 3.0 Flash VLFugu Max
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

    Ling 3.0 Flash VL and Fugu Max model facts
    FeatureLing 3.0 Flash VLFugu Max
    Context & model facts
    DeveloperinclusionAISakana AI
    API providerinclusionAISakana
    Input context131,072 tokens1,000,000 tokens
    Maximum output32,768 tokens128,000 tokens
    Released——
    Added to WritingmateSep 10, 2026Sep 11, 2026
    LicenseNot availableNot available
    Knowledge cutoff——
    Capabilities
    InputsText, Image, VideoText, Image, File
    OutputsTextText
    Provider endpoint accepts tool parametersYesYes
    ReasoningYesYes
    VisionYesYes
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.06$2.00
    Output (per 1M tokens)$0.18$6.00
    Blended 3:1 input/output$0.09$3.00
    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 11, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Ling 3.0 Flash VL from inclusionAI with Fugu Max from Sakana AI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Ling 3.0 Flash VL and Fugu Max
    PlanPriceLing 3.0 Flash VLFugu MaxAI 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

    Ling 3.0 Flash VL vs Fugu Max FAQ

    Which is better, Ling 3.0 Flash VL or Fugu Max?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Ling 3.0 Flash VL is about 33.3× cheaper on a blended 3:1 input/output basis ($0.09 vs $3.00 per 1M tokens). Fugu Max has the larger context window (1,000,000 tokens vs 131,072 tokens).

    Which model is cheaper to use through an API?

    Ling 3.0 Flash VL is about 33.3× cheaper on a blended 3:1 input/output basis ($0.09 vs $3.00 per 1M tokens).

    Which model supports more context?

    Fugu Max has the larger context window (1,000,000 tokens vs 131,072 tokens).

    Can I switch between Ling 3.0 Flash VL and Fugu Max?

    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.