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Model Comparison

Granite 4.0 Micro vs Muse Spark 1.2 ContributorWhich Is Better in 2026?

IBM Granite•Meta: Muse Spark 1.2 Contributor logoMeta

Granite 4.0 Micro vs Muse Spark 1.2 Contributor: which should you choose in 2026?

Granite 4.0 Micro (by IBM Granite) and Muse Spark 1.2 Contributor (by Meta) 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.

Granite 4.0 Micro is about 3.1× cheaper on a blended 3:1 input/output basis ($0.04075 vs $0.125 per 1M tokens).

Muse Spark 1.2 Contributor has the larger context window (1,048,576 tokens vs 131,000 tokens).

Choose Granite 4.0 Micro if…

  • • lower blended API cost matters for your workload

Choose Muse Spark 1.2 Contributor if…

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

Granite 4.0 Micro and Muse Spark 1.2 Contributor benchmark results
BenchmarkGranite 4.0 MicroMuse Spark 1.2 Contributor
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

——
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

    Granite 4.0 Micro and Muse Spark 1.2 Contributor model facts
    FeatureGranite 4.0 MicroMuse Spark 1.2 Contributor
    Context & model facts
    DeveloperIBM GraniteMeta
    API providerIBMMeta
    Input context131,000 tokens1,048,576 tokens
    Maximum output131,000 tokens—
    Released——
    Added to WritingmateOct 20, 2025Aug 21, 2026
    LicenseNot availableNot available
    Knowledge cutoff——
    Capabilities
    InputsTextText, Image, Video, File, Audio
    OutputsTextText
    Provider endpoint accepts tool parametersNoYes
    ReasoningNoYes
    VisionNoYes
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.017$0.10
    Output (per 1M tokens)$0.112$0.20
    Blended 3:1 input/output$0.04075$0.125
    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 21, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Granite 4.0 Micro from IBM Granite with Muse Spark 1.2 Contributor from Meta? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Granite 4.0 Micro and Muse Spark 1.2 Contributor
    PlanPriceGranite 4.0 MicroMuse Spark 1.2 ContributorAI 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

    Granite 4.0 Micro vs Muse Spark 1.2 Contributor FAQ

    Which is better, Granite 4.0 Micro or Muse Spark 1.2 Contributor?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Granite 4.0 Micro is about 3.1× cheaper on a blended 3:1 input/output basis ($0.04075 vs $0.125 per 1M tokens). Muse Spark 1.2 Contributor has the larger context window (1,048,576 tokens vs 131,000 tokens).

    Which model is cheaper to use through an API?

    Granite 4.0 Micro is about 3.1× cheaper on a blended 3:1 input/output basis ($0.04075 vs $0.125 per 1M tokens).

    Which model supports more context?

    Muse Spark 1.2 Contributor has the larger context window (1,048,576 tokens vs 131,000 tokens).

    Can I switch between Granite 4.0 Micro and Muse Spark 1.2 Contributor?

    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.