StepFun today released 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.
What is Step 3.7 Flash?
Step 3.7 Flash is a 198B-parameter sparse Mixture-of-Experts (MoE) vision-language model. It pairs a 196B-parameter language backbone with a 1.8B-parameter vision encoder (ViT) for native image understanding.
The model activates approximately 11B parameters per token during inference. In MoE architectures, only a subset of “expert” sub-networks fires per forward pass — not the full network. This keeps inference compute closer to an 11B dense model while maintaining a 198B total parameter budget.
Key specs:
SpecValue
Total parameters198B (196B language + 1.8B ViT)
Active parameters per token~11B
Context window256k tokens
ThroughputUp to 400 tokens/sec
Reasoning levelsLow, medium, high
LicenseApache 2.0
Architecture Notes
The vision encoder runs as a separate 1.8B ViT module. It injects image representations into the language backbone’s context. Step 3.5 Flash had no multimodal support; this is a new addition in 3.7.
Three selectable reasoning depths — low, medium, and high — let developers trade latency for reasoning depth. Low is faster and cheaper; high applies more computation per response.
Agentic Coding Performance
On SWE-Bench Pro, Step 3.7 Flash scores 56.26%, up from Step 3.5 Flash’s 51.3% — a gain of roughly 5 percentage points. On Terminal-Bench 2.1, it scores 59.55%, up from 53.37%.
On SWE-MTLG (a multi-task long-generation coding benchmark), it scores 72.42%.
Cross-harness consistency on StepFun’s internal Step-SWE-Bench:
StepFun today released 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.
What is Step 3.7 Flash?
Step 3.7 Flash is a 198B-parameter sparse Mixture-of-Experts (MoE) vision-language model. It pairs a 196B-parameter language backbone with a 1.8B-parameter vision encoder (ViT) for native image understanding.
The model activates approximately 11B parameters per token during inference. In MoE architectures, only a subset of “expert” sub-networks fires per forward pass — not the full network. This keeps inference compute closer to an 11B dense model while maintaining a 198B total parameter budget.
ScaffoldStep 3.7 FlashStep 3.5 Flash
Hermes Agent67.5%60.0%
OpenClaw67.0%47.0%
KiloCode67.5%59.0%
RooCode64.5%43.0%
Claude Code71.5%73.0%
OpenCode64.5%57.0%
Step 3.5 Flash ranged from 43% to 73% across harnesses. Step 3.7 Flash ranges from 64.5% to 71.5%. In production, coding agents often run inside heterogeneous scaffolds — each with its own prompting conventions and tool schemas. Narrower per-harness variance means more predictable behavior across different setups.
Advisor Mode
Step 3.7 Flash supports Advisor Mode, StepFun’s implementation of the advisor strategy described by Anthropic. The model runs the agentic loop end-to-end — calling tools, reading results, iterating — and escalates to a larger advisor model only at specific inflection points, such as planning or recovering from repeated failures. Most of the run stays at executor cost.
With Advisor Mode enabled on SWE-Bench Verified, StepFun reports Step 3.7 Flash reaches 97% of Claude Opus 4.6’s coding performance at roughly one-ninth the per-task cost ($0.19 vs. $1.76 per task). These are StepFun’s internal figures.
Multimodal Capabilities
Step 3.7 Flash supports two visual tool pathways:
Visual Search Tool — For recognition tasks where the model’s parametric knowledge is insufficient (long-tail entities, recently emerged concepts), it invokes a visual search tool to retrieve and verify. On SimpleVQA (with Search), it scores 79.16%, comparable to GPT 5.5 (79.11%) and above Kimi K2.6 (78.24%) and GLM 5V Turbo (78.20%).
Python Tool — For fine-grained visual tasks (high-resolution images, visual probing, bounding-box analysis), it uses a code interface to crop, zoom, and draw pixels or bounding boxes. On V (a self-tested score with Python), it scores 95.29%. On HR-Bench 4K and HR-Bench 8K, it scores 89.13% and 86.34% respectively.
StepFun notes an observed behavior during testing: the model combined visual tools with non-visual tools without being explicitly trained to do so. For example, after generating frontend code, it used the GUI to render and inspect the result before iterating. StepFun describes this as emergent compositional tool use.
On Android Daily (long-horizon phone UI task completion), Step 3.7 Flash scores 61.87%, ahead of Kimi K2.6 (53.36%) and GLM 5V Turbo (51.68%). Gemini 3 Flash (63.21%) leads this benchmark.
Search and Research Benchmarks
StepFun focused this model’s search design on planning, evidence filtering, and synthesis — integrating search as part of the reasoning loop rather than a separate add-on.
BenchmarkStep 3.7 FlashNotable comparison
HLE with Tools (acc)47.20%DeepSeek V4 Flash: 45.10%
BrowseComp (acc)75.82%Claude Opus 4.7: 79.30%
DeepSearchQA (F1)92.82%Kimi K2.6: 92.50%
ResearchRubrics (score)71.68%GPT 5.5: 61.50%
Note: The HLE with Tools score of 47.20% compares to Step 3.5 Flash’s text-only score of 35.68%. Step 3.5 Flash did not support tool-augmented evaluation on HLE.
General Agent Benchmarks
BenchmarkStep 3.7 FlashDescription
Toolathlon49.51%Multi-tool coordination
ClawEval-1.167.07%Daily autonomous task execution in realistic environments
GDPval (44 occupations)45.8%General professional task execution
Tau2-bench Telecom>98%Across different reasoning difficulty tiers
On ClawEval-1.1, Step 3.7 Flash (67.07%) leads DeepSeek V4 Flash (57.80%) and DeepSeek V4 Pro (59.80%) among the compared models.
Long-Context Performance
On AA-LCR (a long-context retrieval benchmark, avg@16/acc), Step 3.7 Flash scores 63.94%. This is comparable to DeepSeek V4 Flash (63.70%) and DeepSeek V4 Pro (66.30%).
Pricing
Token TypePrice
Input (cache miss)$0.20 / M tokens
Input (cache hit)$0.04 / M tokens
Output$1.15 / M tokens
Marktechpost’s Visual Explainer
Model Release
Step 3.7 Flash — A 198B MoE Vision-Language Model
StepFun · Released May 29, 2026 · Apache 2.0
Slide 1 of 8 — Overview
What Is Step 3.7 Flash?
Step 3.7 Flash is a sparse Mixture-of-Experts (MoE) vision-language model from StepFun. It combines a 196B-parameter language backbone with a 1.8B-parameter Vision Transformer (ViT) encoder for native image understanding.
In a MoE model, only a subset of “expert” sub-networks activates per token — not the full network. This keeps inference compute close to an 11B dense model while maintaining 198B total parameters.
Total Params
198B
Active / Token
~11B
Context Window
256k tokens
Throughput
400 tok/sec
Reasoning Levels
Low / Med / High
License
Apache 2.0
Slide 2 of 8 — Architecture
Architecture Notes
The 1.8B ViT encoder runs as a separate module and injects image representations into the language backbone’s context. Step 3.5 Flash was text-only; native multimodal support is new in 3.7.
Three selectable reasoning depths let developers balance speed and cost:
Low — Fastest, cheapest. Suitable for simple completions.
Medium — Balanced cost and reasoning depth.
High — More compute per response. Best for complex agent tasks.
MoE routing means you pay for ~11B active params at inference, not 198B. This is the core efficiency trade-off in Flash-tier models.
Slide 3 of 8 — Agentic Coding
Agentic Coding Performance
Step 3.7 Flash scores 56.26% on SWE-Bench Pro (up from 51.3% in 3.5 Flash) and 59.55% on Terminal-Bench 2.1 (up from 53.37%). On SWE-MTLG it scores 72.42%.
Per-harness scores on StepFun’s internal Step-SWE-Bench:
Scaffold3.7 Flash3.5 Flash
Hermes Agent67.5%60.0%
OpenClaw67.0%47.0%
KiloCode67.5%59.0%
RooCode64.5%43.0%
Claude Code71.5%73.0%
OpenCode64.5%57.0%
3.5 Flash ranged 43–73% across harnesses. 3.7 Flash narrows that to 64.5–71.5% — more predictable across heterogeneous scaffolds.
Slide 4 of 8 — Advisor Mode
Advisor Mode
Step 3.7 Flash supports Advisor Mode, StepFun’s implementation of the advisor strategy described by Anthropic. The model runs the full agentic loop — calling tools, reading results, iterating — and escalates to a larger advisor model only at specific inflection points.
Escalates during planning or recovery from repeated failures
Most of the run stays at executor (Flash) cost
Large advisor model is consulted sparingly
SWE-Bench Verified results with Advisor Mode (StepFun internal figures):
Step 3.7 Flash + Advisor
76.3% score
Per-task cost
$0.19
Claude Opus 4.6
78.7% score
Claude Opus 4.6 cost
$1.76
Slide 5 of 8 — Multimodal
Multimodal Capabilities
Step 3.7 Flash supports two visual tool pathways:
Visual Search Tool — Invoked for long-tail entity recognition or recently emerged concepts where parametric knowledge is insufficient. SimpleVQA (Search): 79.16%
Android Daily (long-horizon phone UI tasks): Step 3.7 Flash scores 61.87%, ahead of Kimi K2.6 (53.36%) and GLM 5V Turbo (51.68%). Gemini 3 Flash leads at 63.21%.
StepFun reports emergent compositional tool use during testing — the model combined visual and non-visual tools without explicit training to do so.
Slide 6 of 8 — Search & Research
Search and Research Benchmarks
Search is integrated into the model’s reasoning loop rather than treated as an external add-on. StepFun focused training on search planning, evidence filtering, and synthesis.
Benchmark3.7 FlashComparison
HLE w. Tools (acc)47.20%DeepSeek V4 Flash: 45.10%
BrowseComp (acc)75.82%Claude Opus 4.7: 79.30%
DeepSearchQA (F1)92.82%Kimi K2.6: 92.50%
ResearchRubrics71.68%GPT 5.5: 61.50%
HLE comparison: Step 3.5 Flash scored 35.68% text-only. Step 3.7 Flash scores 47.20% with tool access — these are not apples-to-apples.
Slide 7 of 8 — Deployment
Pricing, Deployment & Ecosystem
Token TypePrice
Input (cache miss)$0.20 / M tokens
Input (cache hit)$0.04 / M tokens
Output$1.15 / M tokens
Available on:
StepFun Platform OpenRouter NVIDIA NIM DeepInfra (soon) Fireworks AI (soon) Modal (soon)
Inference backends: vLLM, SGLang, Hugging Face Transformers (requires v5.0+), llama.cpp
Quantization formats: BF16, FP8, NVFP4, GGUF
Local minimum: 120 GB unified memory/VRAM
Slide 8 of 8 — Key Takeaways
Key Takeaways
198B sparse MoE model with ~11B active params per token and a 256k context window
Native multimodal support (images, GUIs, documents) — Step 3.5 Flash was text-only
Advisor Mode scores 76.3% on SWE-Bench Verified at $0.19/task vs. Claude Opus 4.6 at $1.76
Cross-harness coding variance narrowed from 43–73% (3.5) to 64.5–71.5% (3.7)
Released Apache 2.0 with BF16, FP8, NVFP4, and GGUF weights on Hugging Face
Compatible harnesses:
Claude Code KiloCode Hermes Agent OpenClaw
← Prev
1 / 8
Next →
Key Takeaways
Step 3.7 Flash is a 198B sparse MoE model with 11B active params and a 256k context window.
Native multimodal support (images, GUIs, documents) is new — Step 3.5 Flash was text-only.
Advisor Mode reaches 97% of Claude Opus 4.6's SWE-Bench Verified performance at $0.19 per task vs. $1.76.
Cross-harness coding variance narrowed from a 43–73% range (3.5 Flash) to 64.5–71.5% (3.7 Flash).
Released under Apache 2.0 with BF16, FP8, NVFP4, and GGUF weights on Hugging Face.
Where (Inferences) to Run Step 3.7 Flash
Where to Run It
Step 3.7 Flash — Inference Providers & Access
StepFun’s 198B MoE vision-language model across hosted APIs and open weights.
Hosted API · Live Now
StepFun Platform (Global) Live
Official API. Base URL: api.stepfun.ai/v1. Model ID: step-3.7-flash.
platform.stepfun.ai →
StepFun Platform (China) Live
China region API. Base URL: api.stepfun.com/v1. Requires +86 verification.
GPU-accelerated endpoints. Containerized microservice for on-prem, cloud, or hybrid.
build.nvidia.com →
Open Weights · Apache 2.0
Hugging Face Weights
Download BF16, FP8, NVFP4, and GGUF checkpoints for self-hosting.
huggingface.co/stepfun-ai/Step-3.7-Flash →
GitHub Repo
Model code and deployment guides for vLLM, SGLang, and llama.cpp.
github.com/stepfun-ai/Step-3.7-Flash →
Sources: StepFun model page, Hugging Face, GitHub, OpenRouter, NVIDIA Technical Blog. Accurate as of May 29, 2026.
Check out the Model Weights, Repo and Technical Details. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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The post StepFun Releases Step 3.7 Flash: A 198B MoE Vision-Language Model for Coding Agents and Search Workflows appeared first on MarkTechPost.
The vision encoder runs as a separate 1.8B ViT module. It injects image representations into the language backbone’s context. Step 3.5 Flash had no multimodal support; this is a new addition in 3.7.
Three selectable reasoning depths — low, medium, and high — let developers trade latency for reasoning depth. Low is faster and cheaper; high applies more computation per response.
Agentic Coding Performance
On SWE-Bench Pro, Step 3.7 Flash scores 56.26%, up from Step 3.5 Flash’s 51.3% — a gain of roughly 5 percentage points. On Terminal-Bench 2.1, it scores 59.55%, up from 53.37%.
On SWE-MTLG (a multi-task long-generation coding benchmark), it scores 72.42%.
Cross-harness consistency on StepFun’s internal Step-SWE-Bench:
ScaffoldStep 3.7 FlashStep 3.5 Flash
Hermes Agent67.5%60.0%
OpenClaw67.0%47.0%
KiloCode67.5%59.0%
RooCode64.5%43.0%
Claude Code71.5%73.0%
OpenCode64.5%57.0%
Step 3.5 Flash ranged from 43% to 73% across harnesses. Step 3.7 Flash ranges from 64.5% to 71.5%. In production, coding agents often run inside heterogeneous scaffolds — each with its own prompting conventions and tool schemas. Narrower per-harness variance means more predictable behavior across different setups.
Advisor Mode
Step 3.7 Flash supports Advisor Mode, StepFun’s implementation of the advisor strategy described by Anthropic. The model runs the agentic loop end-to-end — calling tools, reading results, iterating — and escalates to a larger advisor model only at specific inflection points, such as planning or recovering from repeated failures. Most of the run stays at executor cost.
With Advisor Mode enabled on SWE-Bench Verified, StepFun reports Step 3.7 Flash reaches 97% of Claude Opus 4.6’s coding performance at roughly one-ninth the per-task cost ($0.19 vs. $1.76 per task). These are StepFun’s internal figures.
Multimodal Capabilities
Step 3.7 Flash supports two visual tool pathways:
Visual Search Tool — For recognition tasks where the model’s parametric knowledge is insufficient (long-tail entities, recently emerged concepts), it invokes a visual search tool to retrieve and verify. On SimpleVQA (with Search), it scores 79.16%, comparable to GPT 5.5 (79.11%) and above Kimi K2.6 (78.24%) and GLM 5V Turbo (78.20%).
Python Tool — For fine-grained visual tasks (high-resolution images, visual probing, bounding-box analysis), it uses a code interface to crop, zoom, and draw pixels or bounding boxes. On V (a self-tested score with Python), it scores 95.29%. On HR-Bench 4K and HR-Bench 8K, it scores 89.13% and 86.34% respectively.
StepFun notes an observed behavior during testing: the model combined visual tools with non-visual tools without being explicitly trained to do so. For example, after generating frontend code, it used the GUI to render and inspect the result before iterating. StepFun describes this as emergent compositional tool use.
On Android Daily (long-horizon phone UI task completion), Step 3.7 Flash scores 61.87%, ahead of Kimi K2.6 (53.36%) and GLM 5V Turbo (51.68%). Gemini 3 Flash (63.21%) leads this benchmark.
Search and Research Benchmarks
StepFun focused this model’s search design on planning, evidence filtering, and synthesis — integrating search as part of the reasoning loop rather than a separate add-on.
BenchmarkStep 3.7 FlashNotable comparison
HLE with Tools (acc)47.20%DeepSeek V4 Flash: 45.10%
BrowseComp (acc)75.82%Claude Opus 4.7: 79.30%
DeepSearchQA (F1)92.82%Kimi K2.6: 92.50%
ResearchRubrics (score)71.68%GPT 5.5: 61.50%
Note: The HLE with Tools score of 47.20% compares to Step 3.5 Flash’s text-only score of 35.68%. Step 3.5 Flash did not support tool-augmented evaluation on HLE.
General Agent Benchmarks
BenchmarkStep 3.7 FlashDescription
Toolathlon49.51%Multi-tool coordination
ClawEval-1.167.07%Daily autonomous task execution in realistic environments
GDPval (44 occupations)45.8%General professional task execution
Tau2-bench Telecom>98%Across different reasoning difficulty tiers
On ClawEval-1.1, Step 3.7 Flash (67.07%) leads DeepSeek V4 Flash (57.80%) and DeepSeek V4 Pro (59.80%) among the compared models.
Long-Context Performance
On AA-LCR (a long-context retrieval benchmark, avg@16/acc), Step 3.7 Flash scores 63.94%. This is comparable to DeepSeek V4 Flash (63.70%) and DeepSeek V4 Pro (66.30%).
Pricing
Token TypePrice
Input (cache miss)$0.20 / M tokens
Input (cache hit)$0.04 / M tokens
Output$1.15 / M tokens
Marktechpost’s Visual Explainer
Model Release
Step 3.7 Flash — A 198B MoE Vision-Language Model
StepFun · Released May 29, 2026 · Apache 2.0
Slide 1 of 8 — Overview
What Is Step 3.7 Flash?
Step 3.7 Flash is a sparse Mixture-of-Experts (MoE) vision-language model from StepFun. It combines a 196B-parameter language backbone with a 1.8B-parameter Vision Transformer (ViT) encoder for native image understanding.
In a MoE model, only a subset of “expert” sub-networks activates per token — not the full network. This keeps inference compute close to an 11B dense model while maintaining 198B total parameters.
Total Params
198B
Active / Token
~11B
Context Window
256k tokens
Throughput
400 tok/sec
Reasoning Levels
Low / Med / High
License
Apache 2.0
Slide 2 of 8 — Architecture
Architecture Notes
The 1.8B ViT encoder runs as a separate module and injects image representations into the language backbone’s context. Step 3.5 Flash was text-only; native multimodal support is new in 3.7.
Three selectable reasoning depths let developers balance speed and cost:
Low — Fastest, cheapest. Suitable for simple completions.
Medium — Balanced cost and reasoning depth.
High — More compute per response. Best for complex agent tasks.
MoE routing means you pay for ~11B active params at inference, not 198B. This is the core efficiency trade-off in Flash-tier models.
Slide 3 of 8 — Agentic Coding
Agentic Coding Performance
Step 3.7 Flash scores 56.26% on SWE-Bench Pro (up from 51.3% in 3.5 Flash) and 59.55% on Terminal-Bench 2.1 (up from 53.37%). On SWE-MTLG it scores 72.42%.
Per-harness scores on StepFun’s internal Step-SWE-Bench:
Scaffold3.7 Flash3.5 Flash
Hermes Agent67.5%60.0%
OpenClaw67.0%47.0%
KiloCode67.5%59.0%
RooCode64.5%43.0%
Claude Code71.5%73.0%
OpenCode64.5%57.0%
3.5 Flash ranged 43–73% across harnesses. 3.7 Flash narrows that to 64.5–71.5% — more predictable across heterogeneous scaffolds.
Slide 4 of 8 — Advisor Mode
Advisor Mode
Step 3.7 Flash supports Advisor Mode, StepFun’s implementation of the advisor strategy described by Anthropic. The model runs the full agentic loop — calling tools, reading results, iterating — and escalates to a larger advisor model only at specific inflection points.
Escalates during planning or recovery from repeated failures
Most of the run stays at executor (Flash) cost
Large advisor model is consulted sparingly
SWE-Bench Verified results with Advisor Mode (StepFun internal figures):
Step 3.7 Flash + Advisor
76.3% score
Per-task cost
$0.19
Claude Opus 4.6
78.7% score
Claude Opus 4.6 cost
$1.76
Slide 5 of 8 — Multimodal
Multimodal Capabilities
Step 3.7 Flash supports two visual tool pathways:
Visual Search Tool — Invoked for long-tail entity recognition or recently emerged concepts where parametric knowledge is insufficient. SimpleVQA (Search): 79.16%
Android Daily (long-horizon phone UI tasks): Step 3.7 Flash scores 61.87%, ahead of Kimi K2.6 (53.36%) and GLM 5V Turbo (51.68%). Gemini 3 Flash leads at 63.21%.
StepFun reports emergent compositional tool use during testing — the model combined visual and non-visual tools without explicit training to do so.
Slide 6 of 8 — Search & Research
Search and Research Benchmarks
Search is integrated into the model’s reasoning loop rather than treated as an external add-on. StepFun focused training on search planning, evidence filtering, and synthesis.
Benchmark3.7 FlashComparison
HLE w. Tools (acc)47.20%DeepSeek V4 Flash: 45.10%
BrowseComp (acc)75.82%Claude Opus 4.7: 79.30%
DeepSearchQA (F1)92.82%Kimi K2.6: 92.50%
ResearchRubrics71.68%GPT 5.5: 61.50%
HLE comparison: Step 3.5 Flash scored 35.68% text-only. Step 3.7 Flash scores 47.20% with tool access — these are not apples-to-apples.
Slide 7 of 8 — Deployment
Pricing, Deployment & Ecosystem
Token TypePrice
Input (cache miss)$0.20 / M tokens
Input (cache hit)$0.04 / M tokens
Output$1.15 / M tokens
Available on:
StepFun Platform OpenRouter NVIDIA NIM DeepInfra (soon) Fireworks AI (soon) Modal (soon)
Inference backends: vLLM, SGLang, Hugging Face Transformers (requires v5.0+), llama.cpp
Quantization formats: BF16, FP8, NVFP4, GGUF
Local minimum: 120 GB unified memory/VRAM
Slide 8 of 8 — Key Takeaways
Key Takeaways
198B sparse MoE model with ~11B active params per token and a 256k context window
Native multimodal support (images, GUIs, documents) — Step 3.5 Flash was text-only
Advisor Mode scores 76.3% on SWE-Bench Verified at $0.19/task vs. Claude Opus 4.6 at $1.76
Cross-harness coding variance narrowed from 43–73% (3.5) to 64.5–71.5% (3.7)
Released Apache 2.0 with BF16, FP8, NVFP4, and GGUF weights on Hugging Face
Compatible harnesses:
Claude Code KiloCode Hermes Agent OpenClaw
← Prev
1 / 8
Next →
Key Takeaways
Step 3.7 Flash is a 198B sparse MoE model with 11B active params and a 256k context window.
Native multimodal support (images, GUIs, documents) is new — Step 3.5 Flash was text-only.
Advisor Mode reaches 97% of Claude Opus 4.6's SWE-Bench Verified performance at $0.19 per task vs. $1.76.
Cross-harness coding variance narrowed from a 43–73% range (3.5 Flash) to 64.5–71.5% (3.7 Flash).
Released under Apache 2.0 with BF16, FP8, NVFP4, and GGUF weights on Hugging Face.
Where (Inferences) to Run Step 3.7 Flash
Where to Run It
Step 3.7 Flash — Inference Providers & Access
StepFun’s 198B MoE vision-language model across hosted APIs and open weights.
Hosted API · Live Now
StepFun Platform (Global) Live
Official API. Base URL: api.stepfun.ai/v1. Model ID: step-3.7-flash.
platform.stepfun.ai →
StepFun Platform (China) Live
China region API. Base URL: api.stepfun.com/v1. Requires +86 verification.
GPU-accelerated endpoints. Containerized microservice for on-prem, cloud, or hybrid.
build.nvidia.com →
Open Weights · Apache 2.0
Hugging Face Weights
Download BF16, FP8, NVFP4, and GGUF checkpoints for self-hosting.
huggingface.co/stepfun-ai/Step-3.7-Flash →
GitHub Repo
Model code and deployment guides for vLLM, SGLang, and llama.cpp.
github.com/stepfun-ai/Step-3.7-Flash →
Sources: StepFun model page, Hugging Face, GitHub, OpenRouter, NVIDIA Technical Blog. Accurate as of May 29, 2026.
Check out the Model Weights, Repo and Technical Details. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us
The post StepFun Releases Step 3.7 Flash: A 198B MoE Vision-Language Model for Coding Agents and Search Workflows appeared first on MarkTechPost.