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Model Comparison

Trinity Large Thinking vs Muse Glimmer 30BWhich Is Better in 2026?

Arcee AI•Meta: Muse Glimmer 30B logoMeta

Trinity Large Thinking vs Muse Glimmer 30B: which should you choose in 2026?

Trinity Large Thinking (by Arcee AI) and Muse Glimmer 30B (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.

Trinity Large Thinking is about 1.7× cheaper on a blended 3:1 input/output basis ($0.3775 vs $0.6375 per 1M tokens).

Trinity Large Thinking has the larger context window (262,144 tokens vs 131,072 tokens).

Choose Trinity Large Thinking if…

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

Choose Muse Glimmer 30B if…

  • • 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.

Trinity Large Thinking and Muse Glimmer 30B benchmark results
BenchmarkTrinity Large ThinkingMuse Glimmer 30B
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

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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

    Trinity Large Thinking and Muse Glimmer 30B model facts
    FeatureTrinity Large ThinkingMuse Glimmer 30B
    Context & model facts
    DeveloperArcee AIMeta
    API providerArcee AIMeta
    Input context262,144 tokens131,072 tokens
    Maximum output235,929 tokens117,964 tokens
    Released——
    Added to WritingmateApr 1, 2026Aug 9, 2026
    LicenseNot availableNot available
    Knowledge cutoff——
    Capabilities
    InputsTextText, Image
    OutputsTextText
    Provider endpoint accepts tool parametersYesYes
    ReasoningYesYes
    VisionNoYes
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.22$0.35
    Output (per 1M tokens)$0.85$1.50
    Blended 3:1 input/output$0.3775$0.6375
    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 9, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Trinity Large Thinking from Arcee AI with Muse Glimmer 30B from Meta? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Trinity Large Thinking and Muse Glimmer 30B
    PlanPriceTrinity Large ThinkingMuse Glimmer 30BAI 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

    Trinity Large Thinking vs Muse Glimmer 30B FAQ

    Which is better, Trinity Large Thinking or Muse Glimmer 30B?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Trinity Large Thinking is about 1.7× cheaper on a blended 3:1 input/output basis ($0.3775 vs $0.6375 per 1M tokens). Trinity Large Thinking has the larger context window (262,144 tokens vs 131,072 tokens).

    Which model is cheaper to use through an API?

    Trinity Large Thinking is about 1.7× cheaper on a blended 3:1 input/output basis ($0.3775 vs $0.6375 per 1M tokens).

    Which model supports more context?

    Trinity Large Thinking has the larger context window (262,144 tokens vs 131,072 tokens).

    Can I switch between Trinity Large Thinking and Muse Glimmer 30B?

    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.