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Model Comparison

Extract vs Pareto 26.9Which Is Better in 2026?

Scaledown•
Unbiased

Extract vs Pareto 26.9: which should you choose in 2026?

Extract (by Scaledown) and Pareto 26.9 (by Unbiased) 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.

Extract is about 125.0× cheaper on a blended 3:1 input/output basis ($0.03 vs $3.75 per 1M tokens).

Extract has the larger context window (1,000,000 tokens vs 262,144 tokens).

Choose Extract if…

  • • lower blended API cost matters for your workload
  • • you work with longer documents, transcripts, or codebases

Choose Pareto 26.9 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.

Extract and Pareto 26.9 benchmark results
BenchmarkExtractPareto 26.9
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

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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

——
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

    Extract and Pareto 26.9 model facts
    FeatureExtractPareto 26.9
    Context & model facts
    DeveloperScaledownUnbiased
    API providerScaleDownPareto 26.9
    Input context1,000,000 tokens262,144 tokens
    Maximum output900,000 tokens131,072 tokens
    Released——
    Added to WritingmateOct 13, 2026Oct 13, 2026
    LicenseNot availableNot available
    Knowledge cutoff——
    Capabilities
    InputsText, Image, FileText, Image
    OutputsTextText
    Provider endpoint accepts tool parametersNoYes
    ReasoningNoNo
    VisionYesYes
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.04$2.50
    Output (per 1M tokens)$0.00$7.50
    Blended 3:1 input/output$0.03$3.75
    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 Oct 13, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Extract from Scaledown with Pareto 26.9 from Unbiased? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Extract and Pareto 26.9
    PlanPriceExtractPareto 26.9AI ImagesAI Video
    Writingmate Pro
    Most popular
    		$20/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreVEO 3.1, Kling 3.0
    Writingmate Ultimate
    Power users
    		$60/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreVEO 3.1, Kling 3.0
    View Plans & Pricing

    Extract vs Pareto 26.9 FAQ

    Which is better, Extract or Pareto 26.9?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Extract is about 125.0× cheaper on a blended 3:1 input/output basis ($0.03 vs $3.75 per 1M tokens). Extract has the larger context window (1,000,000 tokens vs 262,144 tokens).

    Which model is cheaper to use through an API?

    Extract is about 125.0× cheaper on a blended 3:1 input/output basis ($0.03 vs $3.75 per 1M tokens).

    Which model supports more context?

    Extract has the larger context window (1,000,000 tokens vs 262,144 tokens).

    Can I switch between Extract and Pareto 26.9?

    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.