Step 3.7 Flash

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Agentic Coding

Foundation models are shifting from answering questions to taking action, and in the digital world that action takes the form of code. Coding is the substrate of digital agency, the purest form of the plan–execute–observe–iterate loop, and the leading indicator of where a model's broader agentic capability is heading. We invested heavily in this surface for Step 3.7 Flash. Compared to Step 3.5 Flash, it gains +5% on SWE-Bench Pro and 6.1% on Terminal-Bench 2.1.

In production, coding agents rarely run on a single scaffold. They live inside a heterogeneous stack of harnesses, each with its own prompting conventions, tool schemas, and orchestration patterns — and a model has to perform reliably across all of them to be genuinely useful. Step 3.7 Flash is markedly more balanced across this stack than Step 3.5 Flash, with the per-harness gap narrowing substantially on our in-house Step-SWE-Bench.

To push quality further without giving up Flash-tier efficiency, Step 3.7 Flash supports Advisor Mode. Step 3.7 Flash drives the trajectory end-to-end — calling tools, reading results, and iterating — and consults a larger advisor model only at the few inflection points where its own judgment falls short, such as planning or recovering from repeated failures. This is Step's implementation of the advisor strategy described by Anthropic, where a small executor stays in control and escalates to a frontier advisor only when needed, keeping most of the run at executor cost. With Advisor Mode enabled, Step 3.7 Flash reaches 97% of Claude Opus 4.6's coding performance at roughly one-ninth the per-task cost ($0.19 v.s. $1.76 per task).

Sharpened for Enterprise Tasks

Enterprise work inherently depends on two critical pillars: autonomous task execution in dynamic environments and deep, domain-specific vertical knowledge. Step 3.7 Flash is purpose-built and rigorously optimized across both frontiers to independently drive assignments and ship production-grade deliverables.

The model combines strong agentic execution with precise intent understanding and rich multimodal perception, allowing it to seamlessly bridge the gap between comprehension and action. Users can hand Step 3.7 Flash a complete piece of knowledge work and trust it to independently map out the plan, search across live sources, extract key information, and fluidly orchestrate tools to deliver a ready-to-ship result without intervention. It reads and directly acts on mixed inputs—such as screenshots, complex documents, and dense spreadsheets—parsing visual context and digital assets simultaneously. This long-horizon task execution is validated across diverse environments, where Step 3.7 Flash achieves 49.5% on Toolathlon for multi-tool coordination and 67.1% on ClawEval-1.1 for daily autonomous task execution in realistic environments.

The path from general intelligence to true professional expertise starts with real expert practices. By partnering deeply with domain specialists, we have embedded native industry know-how into the model, validating its capabilities through our own benchmarks in finance, accounting, and data analysis. This expertise extends well beyond specialized domains: Step 3.7 Flash reaches 45.8% on GDPval across 44 occupations, and passes at over 98% across different reasoning difficulty tiers on Tau2-bench Telecom.

Search Wider and Deeper

For a model at the scale of Step 3.7 Flash, the goal is not to pack every piece of world knowledge into its weights, but to make the model better at calling upon that knowledge when needed. We therefore focus its capabilities on search planning, evidence filtering, and information synthesis, turning search from an external add-on into a native part of the reasoning process.

Step 3.7 Flash delivers strong results across search-heavy benchmarks. It scores 47.20% on HLE with Tools, up from 35.68% (text-only) for Step 3.5 Flash, and outperforms Flash models from DeepSeek V4 and Gemini 3.5. It reaches 75.82% on BrowseComp, approaching larger models such as Claude Opus 4.7 and GLM 5.1. On DeepSearchQA, it achieves 92.82% F1 score, comparable to Kimi K2.6, a 1T / 32B-active model. On ResearchRubrics, it scores 71.68%, ahead of GPT 5.5 at 61.50% and close to Claude Opus 4.7 at 73.92%. These results show that Step 3.7 Flash combines Flash-level efficiency with strong deep-retrieval and research capabilities.

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Law · conflict-of-interest Two partners at NC LLP — can the firm act against a former client under the LSO rules?

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The trajectories further highlight both the breadth and depth of its search behavior. In the Ontario lawyer conflict-of-interest case, it similarly expanded its search around domain-specific concepts, combined evidence from papers, course materials, official rules, and case analyses, and caught the key traps in the questions.

Meet StepFun

• Step 3.7 Flash is available via our API platform (中文/EN), and you can chat with it on the Web (中文/EN) or in our App (iOS/Android).
• Join our Discord community for updates, support, and early access.

Availability, Deployment, and Ecosystem