Step-3.7-Flash

About

What Step-3.7-Flash is

KLD Benchmarks: Note MXFP4-MOE is known to have worse KLD vs disk space - use UD-Q4KXL instead. mmproj / vision works as well [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 HL

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

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

Call it like any OpenAI endpoint

Once AxForge deploys step-3-7-flash for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (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":"step-3-7-flash","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Licence

Open weights

Sources

Weights & code

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