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Model details

Step 3.7 Flash

Step 3.7 Flash is positioned as a high-efficiency model aimed at real-world agent workflows, framed around a "See. Think. Act." loop with throughput claims of up to 400 tokens per second. It accepts text, image, and video inputs and produces text outputs, with native multimodal understanding that spans product UIs, documents, charts, and natural scenes before writing code or invoking tools. The model is offered as an open release, with code and weights available through GitHub, Hugging Face, and ModelScope, and it is distributed on Deep Infra's hosted inference platform.

The model is built for sustained agent runs rather than single-turn chat, emphasizing reliable tool use and orchestration across terminals, browsers, Office tools, and search interfaces while minimizing drift, broken tool calls, and failed runs on long trajectories. It plugs into mainstream agent harnesses including Claude Code, KiloCode, Hermes Agent, and OpenClaw, and supports a Skills ecosystem to reduce integration overhead. On the SWE-Bench Pro agentic coding evaluation it reaches a score of 56.3, a meaningful step up from the 51.3 posted by its 3.5 Flash predecessor and competitive with other Flash-class peers, making it a practical fit for teams building coding agents, visual search pipelines, and multi-step automation.

Deep Infrastepfun-ai/Step-3.7-Flashdeprecated

Quick Info

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Provider
Deep Infra
Model key
stepfun-ai/Step-3.7-Flash
Release date
May 29, 2026
Last updated
May 29, 2026
Knowledge cutoff
2026-03-01
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.20
Output token cost
$1.15

Limits

Output tokens
256,000 tokens
Context window
262,144 tokens

Latest news about Step 3.7 Flash

Nvidia

Coverage

StepFun's Step 3.7 Flash release at the end of May 2026 under an Apache 2.0 license is drawing attention because it combines open weights, multimodal capability, and low active compute in a package developers can actually try locally. The Shanghai-based lab, backed by investors including Tencent, released the model to According to StepFun's Hugging Face model card as cited in coverage, Step 3.7 Flash supports a 256K context window, three reasoning levels, and throughput of up to 400 tokens per second. The sparse MoE vision-language system has a 196B-parameter language backbone and a 1.8B-parameter vision encoder, activating about 11

Nvidia

CoverageRelease Notes

StepFun released Step 3.7 Flash on May 29, 2026, a 198-billion-parameter sparse Mixture-of-Experts vision-language model targeting agentic use cases, adding native vision input and improved tool-use reliability over Step 3.5 Flash. It pairs a 196B-parameter language backbone with a 1.8B-parameter vision encoder (ViT) f Key published specifications include a 256K-token context window, up to 400 tokens/sec throughput, and three configurable reasoning levels (low, medium, high). StepFun positions Step 3.7 Flash as infrastructure for agentic coding and search workflows rather than as a raw scale leader. The MoE architecture keeps inferen

Hugging Face

Coverage

Lambda's deployment documentation explicitly names Step 3.7 Flash, attributes it to StepFun AI, and confirms the Apache 2.0 release with a 198B-parameter vision-language architecture, 11B active parameters per token, 256K context, and low/medium/high reasoning modes. The language backbone carries over from Step 3.5 Fla Lambda's own vLLM-MTP benchmarks show aggregate throughput of 7,982 tok/s on 4× B200, 6,224 tok/s on 8× H100, and 2,370 tok/s on 8× A100 under an 8192-in/2048-out, 32-concurrent workload. Architecture specifics include 288 routed plus 1 shared expert with top-8 sigmoid routing, 45 layers, 4096 hidden size, and S3F1 hyb

Hugging Face

Coverage

Step 3.7 Flash is described as a high-efficiency large AI model aimed at production-level Agents, open-sourced by StepFun on May 29, 2026 under the Apache 2.0 license, with Step 3.5 Flash identified as its predecessor in the Flash series. It uses a sparse MoE architecture totaling 196B+1.8B (ViT) parameters and activat The model delivers native multimodal understanding and execution plus internet and visual search enhancement, along with tool invocation and orchestration, and reportedly scored 67.1% on ClawEval-1.1 and 49.5% on Toolathlon for multi-tool collaboration. Following release, domestic hardware vendors including Tianshu Zhi

Videos about Step 3.7 Flash