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Model Comparison

Schematron V2 Turbo vs Step 3.5 FlashWhich Is Better in 2026?

Inference net•
StepFun

Schematron V2 Turbo vs Step 3.5 Flash: which should you choose in 2026?

Schematron V2 Turbo (by Inference net) and Step 3.5 Flash (by StepFun) 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.

Schematron V2 Turbo is about 2.5× cheaper on a blended 3:1 input/output basis ($0.06 vs $0.15 per 1M tokens).

Step 3.5 Flash has the larger context window (262,144 tokens vs 128,000 tokens).

Choose Schematron V2 Turbo if…

  • • lower blended API cost matters for your workload

Choose Step 3.5 Flash if…

  • • 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.

Schematron V2 Turbo and Step 3.5 Flash benchmark results
BenchmarkSchematron V2 TurboStep 3.5 Flash
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

——
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

—
1394 ±4

Benchmark sources

Reviewed evidence last updated 2026-08-09. Arena data last refreshed Sep 14, 2026.

Pricing, capabilities, and model facts

Schematron V2 Turbo and Step 3.5 Flash model facts
FeatureSchematron V2 TurboStep 3.5 Flash
Context & model facts
DeveloperInference netStepFun
API providerInference.netStepFun
Input context128,000 tokens262,144 tokens
Maximum output8,192 tokens65,536 tokens
Released——
Added to WritingmateSep 12, 2026Jan 29, 2026
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)$0.03$0.10
Output (per 1M tokens)$0.15$0.30
Blended 3:1 input/output$0.06$0.15
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 Step 3.5 Flash from StepFun? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Schematron V2 Turbo and Step 3.5 Flash
PlanPriceSchematron V2 TurboStep 3.5 FlashAI 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 Step 3.5 Flash FAQ

Which is better, Schematron V2 Turbo or Step 3.5 Flash?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Schematron V2 Turbo is about 2.5× cheaper on a blended 3:1 input/output basis ($0.06 vs $0.15 per 1M tokens). Step 3.5 Flash has the larger context window (262,144 tokens vs 128,000 tokens).

Which model is cheaper to use through an API?

Schematron V2 Turbo is about 2.5× cheaper on a blended 3:1 input/output basis ($0.06 vs $0.15 per 1M tokens).

Which model supports more context?

Step 3.5 Flash has the larger context window (262,144 tokens vs 128,000 tokens).

Can I switch between Schematron V2 Turbo and Step 3.5 Flash?

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.