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Model Comparison

Schematron V2 Turbo vs Mistral Small 3Which Is Better in 2026?

Inference net•Mistral: Mistral Small 3 logoMistral AI

Schematron V2 Turbo vs Mistral Small 3: which should you choose in 2026?

Schematron V2 Turbo (by Inference net) and Mistral Small 3 (by Mistral AI) 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.

Their blended API prices are effectively the same.

Schematron V2 Turbo has the larger context window (128,000 tokens vs 32,768 tokens).

Choose Schematron V2 Turbo if…

  • • you work with longer documents, transcripts, or codebases

Choose Mistral Small 3 if…

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

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.

Schematron V2 Turbo and Mistral Small 3 benchmark results
BenchmarkSchematron V2 TurboMistral Small 3
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

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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

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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

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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

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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

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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

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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

——
Arena preference scores
Arena (Text)

Human preference score

—
1274 ±6

Benchmark sources

Reviewed evidence last updated 2026-08-09. Arena data last refreshed Sep 12, 2026.

Pricing, capabilities, and model facts

Schematron V2 Turbo and Mistral Small 3 model facts
FeatureSchematron V2 TurboMistral Small 3
Context & model facts
DeveloperInference netMistral AI
API providerInference.netMistral
Input context128,000 tokens32,768 tokens
Maximum output8,192 tokens16,384 tokens
Released——
Added to WritingmateSep 12, 2026Jan 30, 2025
LicenseNot availableNot available
Knowledge cutoff—2023-10-31
Capabilities
InputsTextText
OutputsTextText
Provider endpoint accepts tool parametersNoNo
ReasoningNoNo
VisionNoNo
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$0.03$0.05
Output (per 1M tokens)$0.15$0.08
Blended 3:1 input/output$0.06$0.0575
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 Turbo from Inference net with Mistral Small 3 from Mistral AI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Schematron V2 Turbo and Mistral Small 3
PlanPriceSchematron V2 TurboMistral Small 3AI 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

Schematron V2 Turbo vs Mistral Small 3 FAQ

Which is better, Schematron V2 Turbo or Mistral Small 3?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Their blended API prices are effectively the same. Schematron V2 Turbo has the larger context window (128,000 tokens vs 32,768 tokens).

Which model is cheaper to use through an API?

Their blended API prices are effectively the same.

Which model supports more context?

Schematron V2 Turbo has the larger context window (128,000 tokens vs 32,768 tokens).

Can I switch between Schematron V2 Turbo and Mistral Small 3?

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.