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Model Comparison

Perceptron Mk1.5 vs ReMM SLERP 13BWhich Is Better in 2026?

Perceptron•
Undi95

Perceptron Mk1.5 vs ReMM SLERP 13B: which should you choose in 2026?

Perceptron Mk1.5 (by Perceptron) and ReMM SLERP 13B (by Undi95) 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.

ReMM SLERP 13B is about 1.1× cheaper on a blended 3:1 input/output basis ($0.425 vs $0.4875 per 1M tokens).

Perceptron Mk1.5 has the larger context window (36,864 tokens vs 6,144 tokens).

Choose Perceptron Mk1.5 if…

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

Choose ReMM SLERP 13B 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.

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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.

Perceptron Mk1.5 and ReMM SLERP 13B benchmark results
BenchmarkPerceptron Mk1.5ReMM SLERP 13B
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

    Perceptron Mk1.5 and ReMM SLERP 13B model facts
    FeaturePerceptron Mk1.5ReMM SLERP 13B
    Context & model facts
    DeveloperPerceptronUndi95
    API providerPerceptronReMM SLERP 13B
    Input context36,864 tokens6,144 tokens
    Maximum output8,192 tokens5,529 tokens
    Released——
    Added to WritingmateSep 25, 2026Jul 22, 2023
    LicenseNot availableNot available
    Knowledge cutoff—2023-06-30
    Capabilities
    InputsText, Image, Video, AudioText
    OutputsTextText
    Provider endpoint accepts tool parametersYesNo
    ReasoningYesNo
    VisionYesNo
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.15$0.35
    Output (per 1M tokens)$1.50$0.65
    Blended 3:1 input/output$0.4875$0.425
    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 25, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Perceptron Mk1.5 from Perceptron with ReMM SLERP 13B from Undi95? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Perceptron Mk1.5 and ReMM SLERP 13B
    PlanPricePerceptron Mk1.5ReMM SLERP 13BAI 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

    Perceptron Mk1.5 vs ReMM SLERP 13B FAQ

    Which is better, Perceptron Mk1.5 or ReMM SLERP 13B?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. ReMM SLERP 13B is about 1.1× cheaper on a blended 3:1 input/output basis ($0.425 vs $0.4875 per 1M tokens). Perceptron Mk1.5 has the larger context window (36,864 tokens vs 6,144 tokens).

    Which model is cheaper to use through an API?

    ReMM SLERP 13B is about 1.1× cheaper on a blended 3:1 input/output basis ($0.425 vs $0.4875 per 1M tokens).

    Which model supports more context?

    Perceptron Mk1.5 has the larger context window (36,864 tokens vs 6,144 tokens).

    Can I switch between Perceptron Mk1.5 and ReMM SLERP 13B?

    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.