WritingmateWritingmate
Model Comparison

Schematron V2 Small vs Nemotron 3 Nano 30B A3BWhich Is Better in 2026?

Schematron V2 Small vs Nemotron 3 Nano 30B A3B: which should you choose in 2026?

Schematron V2 Small (by Inference net) and Nemotron 3 Nano 30B A3B (by NVIDIA) 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.

Nemotron 3 Nano 30B A3B is about 1.1× cheaper on a blended 3:1 input/output basis ($0.0875 vs $0.095 per 1M tokens).

Nemotron 3 Nano 30B A3B has the larger context window (262,144 tokens vs 128,000 tokens).

Choose Schematron V2 Small if…

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

Choose Nemotron 3 Nano 30B A3B 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.

vs

Performance benchmarks

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

Schematron V2 Small and Nemotron 3 Nano 30B A3B benchmark results
BenchmarkSchematron V2 SmallNemotron 3 Nano 30B A3B
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

    Schematron V2 Small and Nemotron 3 Nano 30B A3B model facts
    FeatureSchematron V2 SmallNemotron 3 Nano 30B A3B
    Context & model facts
    DeveloperInference netNVIDIA
    API providerInference.netNVIDIA
    Input context128,000 tokens262,144 tokens
    Maximum output4,096 tokens235,929 tokens
    Released
    Added to WritingmateSep 12, 2026Dec 14, 2025
    LicenseNot availableNot available
    Knowledge cutoff
    Capabilities
    InputsTextText
    OutputsTextText
    Provider endpoint accepts tool parametersNoYes
    ReasoningNoYes
    VisionNoNo
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.05$0.05
    Output (per 1M tokens)$0.23$0.20
    Blended 3:1 input/output$0.095$0.0875
    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 12, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Schematron V2 Small from Inference net with Nemotron 3 Nano 30B A3B from NVIDIA? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Schematron V2 Small and Nemotron 3 Nano 30B A3B
    PlanPriceSchematron V2 SmallNemotron 3 Nano 30B A3BAI 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

    Schematron V2 Small vs Nemotron 3 Nano 30B A3B FAQ

    Which is better, Schematron V2 Small or Nemotron 3 Nano 30B A3B?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Nemotron 3 Nano 30B A3B is about 1.1× cheaper on a blended 3:1 input/output basis ($0.0875 vs $0.095 per 1M tokens). Nemotron 3 Nano 30B A3B has the larger context window (262,144 tokens vs 128,000 tokens).

    Which model is cheaper to use through an API?

    Nemotron 3 Nano 30B A3B is about 1.1× cheaper on a blended 3:1 input/output basis ($0.0875 vs $0.095 per 1M tokens).

    Which model supports more context?

    Nemotron 3 Nano 30B A3B has the larger context window (262,144 tokens vs 128,000 tokens).

    Can I switch between Schematron V2 Small and Nemotron 3 Nano 30B A3B?

    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.