WritingmateWritingmate
Model Comparison

Trinity Large Thinking vs Ember-1Which Is Better in 2026?

Arcee AI•
Fireworks

Trinity Large Thinking vs Ember-1: which should you choose in 2026?

Trinity Large Thinking (by Arcee AI) and Ember-1 (by Fireworks) 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 15.5× cheaper on a blended 3:1 input/output basis ($0.3875 vs $6.00 per 1M tokens).

Ember-1 has the larger context window (1,048,576 tokens vs 262,144 tokens).

Choose Trinity Large Thinking if…

  • • lower blended API cost matters for your workload

Choose Ember-1 if…

  • • 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.

Chat with both models
vs

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 Ember-1 benchmark results
BenchmarkTrinity Large ThinkingEmber-1
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

——
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

    Trinity Large Thinking and Ember-1 model facts
    FeatureTrinity Large ThinkingEmber-1
    Context & model facts
    DeveloperArcee AIFireworks
    API providerArcee AIFireworks
    Input context262,144 tokens1,048,576 tokens
    Maximum output80,000 tokens943,718 tokens
    Released——
    Added to WritingmateApr 1, 2026Sep 24, 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)$0.25$3.00
    Output (per 1M tokens)$0.80$15.00
    Blended 3:1 input/output$0.3875$6.00
    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 Sep 24, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Trinity Large Thinking from Arcee AI with Ember-1 from Fireworks? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Trinity Large Thinking and Ember-1
    PlanPriceTrinity Large ThinkingEmber-1AI 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

    Trinity Large Thinking vs Ember-1 FAQ

    Which is better, Trinity Large Thinking or Ember-1?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Trinity Large Thinking is about 15.5× cheaper on a blended 3:1 input/output basis ($0.3875 vs $6.00 per 1M tokens). Ember-1 has the larger context window (1,048,576 tokens vs 262,144 tokens).

    Which model is cheaper to use through an API?

    Trinity Large Thinking is about 15.5× cheaper on a blended 3:1 input/output basis ($0.3875 vs $6.00 per 1M tokens).

    Which model supports more context?

    Ember-1 has the larger context window (1,048,576 tokens vs 262,144 tokens).

    Can I switch between Trinity Large Thinking and Ember-1?

    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.