Step 3.5 Flash
StepFun flash lane for quick multimodal reasoning and coding assistance
- Context
- 262K
- Released
- 8mo ago
- Parameters
- —
- Input / 1M tokens
- $0.10
- Output / 1M tokens
- $0.30
Sources:models.devOpenRouter
Benchmarks
DeepSWE is the primary coding benchmark where an exact model evaluation exists. Other sourced evaluations appear below; missing scores are not zero.
Not evaluated in DeepSWE 1.1 as of 2026-09-22. Results are only shown for exact model versions in the official dataset. Official leaderboard.
No benchmark results have been matched to this model yet.
Specifications
- Developer
- Stepfun
- Model family
- Step
- Type
- Language model
- Predecessor
- —
- Successor
- —
- Released
- January 29, 2026
- Model ID
stepfun/step-3.5-flash- Openness
- Open weights
- License
- —
- Parameters
- —
- Input modalities
- text
- Output modalities
- text
- Modality
- text->text
- Tokenizer
- Other
- Max output tokens
- 66K
- Knowledge cutoff
- 2025-01
- Hugging Face
- stepfun-ai/Step-3.5-Flash
- Source freshness
- Metadata synced Oct 8, 2026, 10:17 PM UTC
Additional source evidence
- Source model
- stepfun/step-3.5-flash
- Last updated
- 2026-02-13
- Knowledge cutoff
- 2025-01
- Model context
- 256K
- Maximum input
- 256K
- Model output limit
- 256K
- Open weights
- Yes
- File attachments
- No
- Reasoning
- Yes
- Tool calling
- Yes
- Temperature control
- Yes
Downloadable weights
Published benchmarks
Complete source record (JSON)
{
"id": "stepfun/step-3.5-flash",
"name": "Step 3.5 Flash",
"description": "StepFun flash lane for quick multimodal reasoning and coding assistance",
"last_updated": "2026-02-13",
"knowledge": "2025-01",
"attachment": false,
"reasoning": true,
"tool_call": true,
"temperature": true,
"modalities": {
"input": [
"text"
],
"output": [
"text"
]
},
"limit": {
"context": 256000,
"input": 256000,
"output": 256000
},
"benchmarks": [
{
"name": "Artificial Analysis Coding Index",
"score": 31.6,
"source": "https://openrouter.ai/stepfun/step-3.5-flash/benchmarks",
"metric": "index",
"date": "2026-06-02"
},
{
"name": "SciCode",
"score": 40.4,
"source": "https://openrouter.ai/stepfun/step-3.5-flash/benchmarks",
"metric": "percent correct",
"date": "2026-06-02"
},
{
"name": "Terminal-Bench Hard",
"score": 27.3,
"source": "https://openrouter.ai/stepfun/step-3.5-flash/benchmarks",
"metric": "success rate",
"date": "2026-06-02"
},
{
"name": "SWE-Bench Verified",
"score": 74.4,
"source": "https://arxiv.org/abs/2602.10604",
"metric": "resolved"
}
],
"release_date": "2026-01-29",
"open_weights": true,
"weights": [
{
"label": "Hugging Face",
"url": "https://huggingface.co/stepfun-ai/Step-3.5-Flash"
}
]
}Infrastructure
Provider availability
| Provider | Endpoint | Context | Input /M tokens | Output /M tokens | Cached input /M tokens | Cache write /M tokens | Uptime 1d | Data handling |
|---|---|---|---|---|---|---|---|---|
| siliconflow/fp8 | 262K | $0.10 | $0.30 | — | — | 99.39% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 |
Provider data:OpenRouter
Deployment graph
Can I run this?
Vertical Tabs only shows compatibility when it has structured evidence tying this exact model to hardware, runtime, quantization, memory use, and observed performance.