StepFun
Step 5 Preview
step-5-preview
Total Context
1M
Max Output
1M
Released
N/A
Input$1 / M tokens
Output$2.7 / M tokens
Step 3.7 Flash
step-3.7-flashStepFun Step 3.7 Flash is a high-efficiency multimodal Mixture-of-Experts (MoE) model from StepFun, built for production agent workflows and developer workloads. It supports native text and image understanding, a 256K context window, and flexible reasoning for coding, visual and document analysis, tool calling, search-augmented tasks, and multi-step agents. Its sparse 198B architecture activates about 11B parameters per token, balancing reasoning and multimodal performance with efficient inference. Use the StepFun API through TokenHub to review the current Step 3.7 Flash price, model ID, supported endpoints, and limits before integrating it into production.
Context Window
256K tokens
Maximum Output
256K tokens
Release Date
May 29, 2026
Modalities
Step 3.7 Flash Pricing
| Input Price | Output Price | Cache Read |
|---|---|---|
| $0.2/M | $1.15/M | $0.04/M |
Step 3.7 Flash API Capabilities
Reasoning
Supported
Tool calling
Supported
Temperature parameter
Supported
Attachments
Supported
Knowledge Base
—
Endpoint Protocols
Completions APIResponses APIMessages API
Step 3.7 Flash Model Highlights
Step 3.7 Flash combines native image understanding, selectable reasoning depth, and high-throughput agentic coding for frequent production workloads.
Native Visual Understanding
A dedicated vision encoder lets the model interpret screenshots, interface layouts, diagrams, charts, and other image inputs.
Selectable Reasoning Depth
Low, medium, and high reasoning levels let developers balance response speed with the depth required by each task.
High-Throughput Agentic Coding
The sparse architecture is designed for rapid production inference while supporting multi-file coding and long-running agent workflows.
Step 3.7 Flash Use Cases
Step 3.7 Flash is suited to screenshot-to-code work, repository debugging, financial document analysis, and multi-source research agents.
Screenshot to Code
Interpret a UI screenshot or wireframe and generate structured front-end code that reflects its layout and components.
Repository Debugging
Trace behavior across multiple files, isolate a defect from an issue report, and generate a patch that can be checked with tests.
Multi-Source Research
Run iterative searches, compare evidence across sources, and synthesize verified findings from text, images, and dense reports.
How to Use Step 3.7 Flash via the TokenHub API
import OpenAI from "openai"
const client = new OpenAI({
apiKey: process.env.TOKENHUB_API_KEY,
baseURL: "https://us-api.tokenhub.com/v1",
})
const result = await client.chat.completions.create({})
console.log(result.choices[0]?.message?.content)Step 3.7 Flash Benchmarks
Step 3.7 Flash
| Index score | ||
|---|---|---|
| Artificial Analysis Intelligence Index | Artificial Analysis broad capability aggregate | 30.9 |
| Artificial Analysis Coding Index | Artificial Analysis software task aggregate | 39.6 |
Metrics sourced from Artificial Analysis
Step 3.7 Flash API FAQs
Answers about the StepFun API for Step 3.7 Flash, including multimodal capabilities, agent workflows, pricing, context limits, and TokenHub integration.
What is Step 3.7 Flash?+
Step 3.7 Flash is a high-efficiency multimodal Mixture-of-Experts model from StepFun. It is designed for production agent and developer workloads, combining text and image understanding, a 256K context window, and flexible reasoning for coding, document analysis, tool calling, and multi-step tasks.
What is Step 3.7 Flash best used for?+
Choose Step 3.7 Flash for responsive coding assistants, visual or document analysis, search-augmented research, structured extraction, and tool-using agent workflows. It is a practical fit when one task needs both multimodal input and multi-step reasoning rather than a text-only response.
Does Step 3.7 Flash support multimodal input, reasoning, and tool calling?+
The model is designed for native text and image understanding, flexible reasoning, and tool-oriented agent tasks. Before implementation, confirm the current TokenHub model page for the exact input formats, endpoint support, request schema, and tool-calling behavior available to your account.
How does the 256K context window and sparse MoE architecture affect use?+
A 256K context window can make Step 3.7 Flash useful for large documents, visual materials, retrieved evidence, and multi-step task state. Its sparse architecture is intended to balance capability with inference efficiency, but actual latency and cost still depend on input size, output length, reasoning settings, and the current TokenHub pricing.
How do I call Step 3.7 Flash through the StepFun API on TokenHub?+
Create a TokenHub API key, select the current Step 3.7 Flash model ID shown in the catalog, and use the endpoint and request example published on its model page. Check the live documentation before sending production traffic, especially if your application includes image input, streaming, or tool calls.
How should I evaluate Step 3.7 Flash API pricing?+
Use the current TokenHub price table rather than a fixed number from an older article. Estimate cost from your typical input tokens, output tokens, cache use where available, request volume, and the amount of image or long-context content in each workflow. Test representative prompts before committing a production budget.
When should I choose Step 3.7 Flash over another model?+
Prioritize Step 3.7 Flash when your workload combines image or document understanding, coding or research, tool calling, and multi-step agent execution—and you value inference efficiency. For simple text-only requests, compare smaller and cheaper models; for a high-stakes task, evaluate Step 3.7 Flash and alternatives on the same prompts, latency target, and budget.
Ready to use Step 3.7 Flash?
Use one API key to access Step 3.7 Flash and more AI models through TokenHub.
Step 3.7 Flash Media and Demos
Selected public announcements, tests, and community discussions about step-3.7-flash.
X (Twitter)
StepFun ·
⚡️ Step 3.7 Flash is here: The new frontier is agent efficiency. #1 ClawEval-1.1 (67.1), #1 SimpleVQA Search (79.2), #2 SWE-PRO (56.3), 95.3 on V* Python. Open weights under Apache 2.0. Built for agentic, coding, search, and multimodal workflows — balancing speed, cost, and https://t.co/mzLi1HnkxU
https://pbs.twimg.com/media/HJcdzQdbcAAcQki.jpgView originalStepFun ·
Step 3.7 Flash, free for 30 days for Hermes Agent users. What could possibly go wrong? 🍿 Thanks @NousResearch for making it happen. Can’t wait to see what Hermes users build!
View originalStepFun ·
Want to try Step 3.7 Flash without touching a line of code? It now has a hosted demo — open it right in your browser, no install needed. Built on Gradio by @_akhaliq 🙏, now live in our Hugging Face org. Give it a try 👇
View originalReddit
StepFun Releases Step 3.7 Flash: A 198B MoE Vision-Language Model for Coding Agents and Search Workflows
techlatest_net ·
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StepFun today released Step 3.7 Flash (https://github.com/stepfun-ai/Step-3.7-Flash), a multimodal Mixture-of-Experts model targeting agentic use cases. It adds native vision input and improved tool-use reliability over Step 3.5 Flash. Step 3.7 Flash [ModelPage]: https://static.stepfun.com/blog/step-3.7-flash/ [HuggingFace]: BF16: https://huggingface.co/stepfun-ai/Step-3.7-Flash/ FP8: https://huggingface.co/stepfun-ai/Step-3.7-Flash-FP8 NVFP4: https://huggingface.co/stepfun-ai/Step-3.7-Flash-NVFP4 GGUF: https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF Code Engineering and Professional Baselines r/LocalLLM - StepFun Releases Step 3.7 Flash: A 198B MoE Vision-Language Model for Coding Agents and Search Workflows (https://preview.redd.it/stepfun-releases-step-3-7-flash-a-198b-moe-vision-language-v0-2fh1hu88e64h1.png?width=1280&format=png&auto=webp&s=d47b392ea2ef4dbb7acf87816187e563412210ae)
Step 3.7 Flash quants rolling out today
colinrgodsey ·
https://huggingface.co/unsloth/Step-3.7-Flash-GGUF/tree/main It was empty this morning and has been getting less empty throughout the day. I grabbed the UD-IQ4_NL a few hours ago and its been pretty nice so far. EDIT: not trying to steal any thunder, I'm just very excited for this model and the amazing unsloth quants!
https://huggingface.co/unsloth/Step-3.7-Flash-GGUF/tree/mainView originalStepFun 3.7 Flash MTP Bench
westsunset ·
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This is the StepFun Step-3.7-Flash UD-IQ4_XS main model with the official StepFun MTP Q8_0 draft model, served through a patched llama.cpp Vulkan/RADV build. Host System: AMD Ryzen AI Max+ 395 / Radeon 8060S (gfx1151) Memory: 128 GB unified LPDDR5X BIOS UMA / VRAM: 4 GB UMA dedicated VRAM GTT ceiling: 112 GiB IOMMU: enabled (amd_iommu=on) OS: Ubuntu 25.04 (Plucky) Kernel: 6.18.1-061801-generic Mesa / RADV: Mesa 25.2.8 / RADV ROCm: 7.1.1 baseline; some later rows also reference ROCm 7.2.x runtime libraries Model Main model: StepFun Step-3.7-Flash UD-IQ4_XS Main model size on disk: 95,336,010,208 bytes / 88.79 GiB Main model shards: 3 Draft model: Step-3.7-Flash-MTP-Q8_0.gguf Draft model size: about 3.5 GiB Architecture: step35 Model class: roughly 200B total parameters / about 11B active parameters per token Backend: llama.cpp Vulkan/RADV b9360 with Step-3.7 MTP patch Context used for this bench: 12,288 MTP settings: DRAFT_N=2, PMIN=0.60, UBATCH=512 Latest measured numbers Metric StepFun MTP Non-MTP baseline Change Load to listening ~31 s ~31 s no startup penalty observed Prefill / prompt processing 211.2 tok/s 212.0 tok/s basically flat Decode / token generation 26.0 tok/s 20.4 tok/s +27.5% Normalized wall time, 1150-in/2000-out 82.4 s 103.4 s 20.8% faster Two concurrent requests 19.7 / 19.6 tok/s 17.14 tok/s each +15% per slot Two-slot aggregate 35.7 tok/s ~34 tok/s +5% aggregate Socket power during decode ~73 W ~85 W ~14% lower The main result: MTP materially improves decode speed without hurting prefill. For a roughly 200B-total MoE model, 26 tok/s single-stream on a 128 GB Strix Halo APU is a useful local lane. Draft acceptance The standard decode probe showed: Drafted tokens: 491 Accepted draft tokens: 416 Accepted / drafted: 84.7% Important source note: the summarized bench.json currently has "mtp.acceptance_pct": null. The 84.7% acceptance number comes from the raw tg_probe.json timing counters, not from the aggregate bench.json field. Context against other local lanes These are not quality-equivalent rows, but they help place the speed tier: Model / lane Total / active Quant / path Prefill Decode Qwen 3.6 35B MTP 35B / A3B Q4_K_M, Vulkan MTP not listed here 81.2 tok/s gpt-oss-120b 117B / A5.1B MXFP4, Vulkan 787 tok/s 46.7 tok/s Qwen3-Coder-Next coder MoE UD-Q4_K_XL, Vulkan 723.2 tok/s 44.4 tok/s Qwen 3.5 122B MTP 122B / A10B MXFP4_MOE, Vulkan MTP 332.1 tok/s 26.7 tok/s StepFun 3.7 Flash MTP ~200B / A11B UD-IQ4_XS + Q8 MTP draft 211.2 tok/s 26.0 tok/s StepFun 3.7 Flash plain ~200B / A11B UD-IQ4_XS, no MTP 212.0 tok/s 20.4 tok/s The interesting part is that StepFun MTP lands in the same rough decode tier as Qwen 122B MTP while moving a much larger total-parameter model. Whether that is the best lane depends on whether StepFun's quality is worth spending the 26 tok/s tier on.
YouTube
Step 3.7 Flash First Look & LOCAL Test – A VERY Creative Model!
Bijan Bowen
View originalStep 3.7 Flash - 198B Open Source Model That Does Everything; Does it Really?
Fahd Mirza
View originalStep 3.7 Flash: How to build a Fully Autonomous AI Research Agent - Best AI Model for Deep Research
The Metaverse Guy
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