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Model Comparison

Claude Opus 5.5 vs Command R7B (12-2024)Which Is Better in 2026?

Claude Opus 5.5 vs Command R7B (12-2024): which should you choose in 2026?

Claude Opus 5.5 (by Anthropic) and Command R7B (12-2024) (by Cohere) 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.

Command R7B (12-2024) is about 121.9× cheaper on a blended 3:1 input/output basis ($0.065625 vs $8.00 per 1M tokens).

Claude Opus 5.5 has the larger context window (1,000,000 tokens vs 128,000 tokens).

Choose Claude Opus 5.5 if…

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

Choose Command R7B (12-2024) if…

  • lower blended API cost matters for your workload

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.

vs

Performance benchmarks

Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.

Claude Opus 5.5 and Command R7B (12-2024) benchmark results
BenchmarkClaude Opus 5.5Command R7B (12-2024)
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

    Claude Opus 5.5 and Command R7B (12-2024) model facts
    FeatureClaude Opus 5.5Command R7B (12-2024)
    Context & model facts
    DeveloperAnthropicCohere
    API providerAnthropicCohere
    Input context1,000,000 tokens128,000 tokens
    Maximum output128,000 tokens4,000 tokens
    Released
    Added to WritingmateSep 22, 2026Dec 14, 2024
    LicenseNot availableNot available
    Knowledge cutoff2024-08-31
    Capabilities
    InputsText, Image, FileText
    OutputsTextText
    Provider endpoint accepts tool parametersYesNo
    ReasoningYesNo
    VisionYesNo
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$4.00$0.0375
    Output (per 1M tokens)$20.00$0.15
    Blended 3:1 input/output$8.00$0.065625
    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 22, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Claude Opus 5.5 from Anthropic with Command R7B (12-2024) from Cohere? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Claude Opus 5.5 and Command R7B (12-2024)
    PlanPriceClaude Opus 5.5Command R7B (12-2024)AI ImagesAI Video
    Writingmate Pro
    Most popular
    $20/moUltimate requiredIncludedNano 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

    Claude Opus 5.5 vs Command R7B (12-2024) FAQ

    Which is better, Claude Opus 5.5 or Command R7B (12-2024)?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Command R7B (12-2024) is about 121.9× cheaper on a blended 3:1 input/output basis ($0.065625 vs $8.00 per 1M tokens). Claude Opus 5.5 has the larger context window (1,000,000 tokens vs 128,000 tokens).

    Which model is cheaper to use through an API?

    Command R7B (12-2024) is about 121.9× cheaper on a blended 3:1 input/output basis ($0.065625 vs $8.00 per 1M tokens).

    Which model supports more context?

    Claude Opus 5.5 has the larger context window (1,000,000 tokens vs 128,000 tokens).

    Can I switch between Claude Opus 5.5 and Command R7B (12-2024)?

    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.