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Model Comparison

LFM2.5-24B-A2B vs SummarizeWhich Is Better in 2026?

Liquid AI•
Scaledown

LFM2.5-24B-A2B vs Summarize: which should you choose in 2026?

LFM2.5-24B-A2B (by Liquid AI) and Summarize (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.

Summarize is about 7.1× cheaper on a blended 3:1 input/output basis ($0.03 vs $0.2125 per 1M tokens).

Summarize has the larger context window (1,000,000 tokens vs 128,000 tokens).

Choose LFM2.5-24B-A2B if…

  • • you need a model with explicit reasoning support

Choose Summarize 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.

LFM2.5-24B-A2B and Summarize benchmark results
BenchmarkLFM2.5-24B-A2BSummarize
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

    LFM2.5-24B-A2B and Summarize model facts
    FeatureLFM2.5-24B-A2BSummarize
    Context & model facts
    DeveloperLiquid AIScaledown
    API providerLiquidAIScaleDown
    Input context128,000 tokens1,000,000 tokens
    Maximum output8,192 tokens4,000 tokens
    Released——
    Added to WritingmateOct 13, 2026Oct 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)$0.05$0.04
    Output (per 1M tokens)$0.70$0.00
    Blended 3:1 input/output$0.2125$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 LFM2.5-24B-A2B from Liquid AI with Summarize from Scaledown? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for LFM2.5-24B-A2B and Summarize
    PlanPriceLFM2.5-24B-A2BSummarizeAI 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

    LFM2.5-24B-A2B vs Summarize FAQ

    Which is better, LFM2.5-24B-A2B or Summarize?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Summarize is about 7.1× cheaper on a blended 3:1 input/output basis ($0.03 vs $0.2125 per 1M tokens). Summarize has the larger context window (1,000,000 tokens vs 128,000 tokens).

    Which model is cheaper to use through an API?

    Summarize is about 7.1× cheaper on a blended 3:1 input/output basis ($0.03 vs $0.2125 per 1M tokens).

    Which model supports more context?

    Summarize has the larger context window (1,000,000 tokens vs 128,000 tokens).

    Can I switch between LFM2.5-24B-A2B and Summarize?

    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.