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Model Comparison

gpt-oss-safeguard-20b vs ExtractWhich Is Better in 2026?

OpenAI: gpt-oss-safeguard-20b logoOpenAI•
Scaledown

gpt-oss-safeguard-20b vs Extract: which should you choose in 2026?

gpt-oss-safeguard-20b (by OpenAI) and Extract (by Scaledown) 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 4.4× cheaper on a blended 3:1 input/output basis ($0.03 vs $0.13125 per 1M tokens).

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

Choose gpt-oss-safeguard-20b if…

  • • you need a model with explicit reasoning support

Choose Extract if…

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

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.

gpt-oss-safeguard-20b and Extract benchmark results
Benchmarkgpt-oss-safeguard-20bExtract
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

——
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

——
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

    gpt-oss-safeguard-20b and Extract model facts
    Featuregpt-oss-safeguard-20bExtract
    Context & model facts
    DeveloperOpenAIScaledown
    API providerOpenAIScaleDown
    Input context131,072 tokens1,000,000 tokens
    Maximum output65,536 tokens900,000 tokens
    Released——
    Added to WritingmateOct 29, 2025Oct 13, 2026
    LicenseNot availableNot available
    Knowledge cutoff——
    Capabilities
    InputsTextText, Image, File
    OutputsTextText
    Provider endpoint accepts tool parametersYesNo
    ReasoningYesNo
    VisionNoYes
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.075$0.04
    Output (per 1M tokens)$0.30$0.00
    Blended 3:1 input/output$0.13125$0.03
    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 gpt-oss-safeguard-20b from OpenAI with Extract from Scaledown? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for gpt-oss-safeguard-20b and Extract
    PlanPricegpt-oss-safeguard-20bExtractAI 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

    gpt-oss-safeguard-20b vs Extract FAQ

    Which is better, gpt-oss-safeguard-20b or Extract?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Extract is about 4.4× cheaper on a blended 3:1 input/output basis ($0.03 vs $0.13125 per 1M tokens). Extract has the larger context window (1,000,000 tokens vs 131,072 tokens).

    Which model is cheaper to use through an API?

    Extract is about 4.4× cheaper on a blended 3:1 input/output basis ($0.03 vs $0.13125 per 1M tokens).

    Which model supports more context?

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

    Can I switch between gpt-oss-safeguard-20b and Extract?

    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.