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Model Comparison

Uncensored vs Mercury 2.5Which Is Better in 2026?

Cognitive Computations•
Inception

Uncensored vs Mercury 2.5: which should you choose in 2026?

Uncensored (by Cognitive Computations) and Mercury 2.5 (by Inception) 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.

Mercury 2.5 is about 5.6× cheaper on a blended 3:1 input/output basis ($0.0675 vs $0.375 per 1M tokens).

Mercury 2.5 has the larger context window (260,000 tokens vs 128,000 tokens).

Choose Uncensored if…

  • • your own prompt tests favor its output; shared comparable evidence does not identify a unique advantage

Choose Mercury 2.5 if…

  • • lower blended API cost matters for your workload
  • • you work with longer documents, transcripts, or codebases
  • • you need a model with explicit reasoning support

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
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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.

Uncensored and Mercury 2.5 benchmark results
BenchmarkUncensoredMercury 2.5
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

    Uncensored and Mercury 2.5 model facts
    FeatureUncensoredMercury 2.5
    Context & model facts
    DeveloperCognitive ComputationsInception
    API providerVeniceInception
    Input context128,000 tokens260,000 tokens
    Maximum output8,192 tokens65,536 tokens
    Released——
    Added to WritingmateJul 9, 2025Sep 8, 2026
    LicenseNot availableNot available
    Knowledge cutoff2024-04-30—
    Capabilities
    InputsTextText
    OutputsTextText
    Provider endpoint accepts tool parametersNoYes
    ReasoningNoYes
    VisionNoNo
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.20$0.04
    Output (per 1M tokens)$0.90$0.15
    Blended 3:1 input/output$0.375$0.0675
    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 8, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Uncensored from Cognitive Computations with Mercury 2.5 from Inception? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Uncensored and Mercury 2.5
    PlanPriceUncensoredMercury 2.5AI 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

    Uncensored vs Mercury 2.5 FAQ

    Which is better, Uncensored or Mercury 2.5?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Mercury 2.5 is about 5.6× cheaper on a blended 3:1 input/output basis ($0.0675 vs $0.375 per 1M tokens). Mercury 2.5 has the larger context window (260,000 tokens vs 128,000 tokens).

    Which model is cheaper to use through an API?

    Mercury 2.5 is about 5.6× cheaper on a blended 3:1 input/output basis ($0.0675 vs $0.375 per 1M tokens).

    Which model supports more context?

    Mercury 2.5 has the larger context window (260,000 tokens vs 128,000 tokens).

    Can I switch between Uncensored and Mercury 2.5?

    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.