StepFun: Step 3.7 Flash
UnavailableStep 3.7 Flash is StepFun's latest high-efficiency multimodal Mixture-of-Experts model. It pairs a 196B-parameter language backbone with a vision encoder for native image and video understanding, activating roughly 11B parameters per token. The model supports a 256K context window and exposes selectable reasoning levels (high/medium/low), letting callers trade off speed, cost, and depth of reasoning. Designed for coding, agentic workflows, structured outputs, and long-context productivity tasks.
- By
- StepFun
- Input
- Text, Image, Video
- Output
- Text
- Context
- 256K tokens
- Max output
- 256K tokens
- Published
- 2026-05-29
- Capabilities
- ReasoningMultimodalLong context
Providers
The provider that serves this model. Click the provider row to inspect live telemetry, pricing, and supported parameters.
| Provider | Input / 1M | Output / 1M | Context | Latency P50 | Throughput P50 | Success 24h |
|---|---|---|---|---|---|---|
Stepfun stepfun · offline | 10x Rp4.160/1M | 60x Rp23.920/1M | 256K | — | — | — |
Uptime
Share of requests that succeeded, per provider.
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Last 24 hours, per hour. Measured from requests through Morphic; none yet.
Throughput
P50 output tokens per second, whole request.
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Last 24 hours, per hour. Measured from requests through Morphic; none yet.
Latency
P50 time to the full response.
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Last 24 hours, per hour. Measured from requests through Morphic; none yet.
Activity
Tokens processed per day over the last 30 days.
| Oct 4 | 0 |
|---|---|
| Oct 5 | 0 |
| Oct 6 | 0 |
| Oct 7 | 0 |
| Oct 8 | 0 |
| Oct 9 | 0 |
| Oct 10 | 0 |
| Oct 11 | 0 |
| Oct 12 | 0 |
| Oct 13 | 0 |
| Oct 14 | 0 |
| Oct 15 | 0 |
| Oct 16 | 0 |
| Oct 17 | 0 |
| Oct 18 | 0 |
| Oct 19 | 0 |
| Oct 20 | 0 |
| Oct 21 | 0 |
| Oct 22 | 0 |
| Oct 23 | 0 |
| Oct 24 | 0 |
| Oct 25 | 0 |
| Oct 26 | 0 |
| Oct 27 | 0 |
| Oct 28 | 0 |
| Oct 29 | 0 |
| Oct 30 | 0 |
| Oct 31 | 0 |
| Nov 1 | 0 |
| Nov 2 | 0 |
Counts every request routed through Morphic.
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StepFun: Step 3.5 FlashStep 3.5 Flash is StepFun's most capable open-source foundation model. Built on a sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token. It is a reasoning model that is incredibly speed efficient even at long contexts.