Model reference · open weights
Step-3.7-Flash is an open-weight language model from stepfun-ai, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
[ModelPage]: https://static.stepfun.com/blog/step-3.7-flash/ 1. Introduction Step 3.7 Flash is a 198B-parameter sparse Mixture-of-Experts (MoE) vision-language model that combines a 196B-parameter language backbone with a 1.8B-parameter vision encoder for native image understanding. Engineered for high-frequency production workloads, it activates approximately 11B parameters per token and delivers a throughput of up to 400 tokens per second. Step 3.7 Flash supports a 256k context window and offers three selectable reasoning levels (low, medium, and high) so developers can easily balance speed, cost, and cognitive depth. We built Step 3.7 Flash for developers who need to scale agentic workflows that combine perception, search, and reasoning. It is designed to handle intensive tasks such as parsing massive financial reports in one pass, running multi-step search loops with cross-source verification, or operating concurrent coding agents in high-throughput pipelines. 2. Capabilities & Performance Multimodal Perception and Verification The model delivers top-tier visual intelligence, securing first place on SimpleVQA (Search) with a 79.2 and achieving frontier parity on V (Python) at 95.3. These metrics reflect strong visual grounding and retrieval-augmented reasoning beyond basic image description. The model accurately processes dense visual interfaces, such as UI wireframes, application GUIs, and data charts, to map them into structured code. When it encounters an incomplete visual asset, it can independently identify missing data and execute lookups to verify context before returning a factually verified conclusion. Workflow Integrity and Tool Orchestration Execution reliability is critical for autonomous agents. Step 3.7 Flash leads the ClawEval-1.1 benchmark with a score of 67.1, which significantly outperforms the next closest competitor at 59.8. This performance demonstrates high resistance to adversarial traps and strict adherence to system policies during multi-turn orchestration. Backed by scores of 49.5 on Toolathlon and 48.1 on HLE w. Tool, this profile ensures high trajectory integrity. Step 3.7 Flash reliably interacts with external APIs and executes l
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | stepfun-ai |
|---|---|
| Type | Language models |
| Parameters (lead) | 201.4B |
| Variants | 4 |
| Runs with | transformers |
| Released | 2026-05-23 |
| Popularity | 40k downloads / month |
| Likes | 448 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
Using it via the API
Once AxForge deploys stepfun-ai-step-3-7-flash for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (stepfun-ai-step-3-7-flash below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"stepfun-ai-step-3-7-flash","messages":[{"role":"user","content":"Hello"}]}'
Details
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Tags
Licence
Open weights under apache-2.0 — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗