
StepFun multimodal reasoning model with image and video input, tool calling, adjustable reasoning effort, and 256K context.
StepFun multimodal reasoning model with image and video input, tool calling, adjustable reasoning effort, and 256K context.
Use reasoning_effort low, medium, or high to control the reasoning budget. Prompt-cache hits are billed at StepFun cache-hit rates. Linkup web search is available through web_search_linkup and adds $0.013 per call when invoked.
Also known as Step Flash, StepFun Step 3.7 Flash, Step-3.7-Flash, step-3-7-flash
step-3-7-flash/v1/chat/completionsPOST/v1/responsesPOST/v1/messagesPOST/v1beta/models/step-3-7-flash:generateContentstep-3.7-flashstepfun/step-3-7-flashstepfun/step-3.7-flashLive pay-as-you-go rates from the EmpirioLabs catalog. You are billed only for what you use, with no monthly minimum.
Step 3.7 Flash serves the OpenAI-compatible Chat Completions API. Point any OpenAI SDK at https://api.empiriolabs.ai/v1 with your EmpirioLabs API key and use the model id step-3-7-flash. Get an API key from the EmpirioLabs dashboard.
curl https://api.empiriolabs.ai/v1/chat/completions \
-H "Authorization: Bearer $EMPIRIOLABS_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "step-3-7-flash",
"messages": [
{"role": "user", "content": "Write a haiku about the ocean."}
]
}'from openai import OpenAI
client = OpenAI(
base_url="https://api.empiriolabs.ai/v1",
api_key="YOUR_EMPIRIOLABS_API_KEY",
)
response = client.chat.completions.create(
model="step-3-7-flash",
messages=[{"role": "user", "content": "Write a haiku about the ocean."}],
)
print(response.choices[0].message.content)Request parameters supported by the Step 3.7 Flash API on EmpirioLabs. Defaults apply when a field is omitted.
| Parameter | Type | Default | Range / values | Description |
|---|---|---|---|---|
| temperature | number | 0.5 | 0 to 2 | Sampling temperature. |
| top_p | number | 0.9 | 0 to 1 | Nucleus sampling probability mass. |
| max_tokens | integer | 4096 | 1 to 131072 | Maximum output tokens. Reasoning tokens count toward this limit. |
| stop | array | - | - | Stop sequences. |
| frequency_penalty | number | 0 | 0 to 1 | Penalty for repeated tokens. |
| reasoning_effort | enum | low | low, medium, high | Reasoning effort for Step 3.7 Flash. |
| response_format | object | - | - | OpenAI-compatible response format. Use {"type":"json_object"} for JSON object mode. |
| reasoning_format | enum | general | general, deepseek-style | Reasoning trace format returned by StepFun. |
| tools | array | - | - | OpenAI-compatible function tools. |
| tool_choice | string | - | - | OpenAI-compatible tool choice. |
| web_search_linkup | boolean | false | - | Optional web search powered by Linkup. When enabled, recent web sources are retrieved using your latest user message as the query and provided to the model as additional context. Adds $0.013 per call when invoked on top of the model's normal token cost. Disabled by default. |
Supports text, image, and video input with 256K context, function tools, JSON object mode, reasoning_effort low, medium, or high, and optional Linkup web search. Video input supports MP4 under 128 MB, with clips under 5 minutes recommended.
On EmpirioLabs, Step 3.7 Flash is billed pay as you go: Input $0.20 per 1M prompt tokens; Output $1.15 per 1M generated tokens; Implicit cache read $0.04 per 1M cached input tokens. The live rate card on this page always matches what the API charges.
Step 3.7 Flash supports a 256K-token context window with up to 131,072 output tokens per response.
Yes. Step 3.7 Flash serves the OpenAI-compatible Chat Completions API, so existing OpenAI SDKs work by pointing base_url at https://api.empiriolabs.ai/v1 and setting the model id to step-3-7-flash.
Yes. The EmpirioLabs playground runs Step 3.7 Flash in the browser with the same parameters the API exposes, so you can test prompts before writing code.
Create an EmpirioLabs account, then generate a key under API Keys in the dashboard. Billing is pay-as-you-go credits, so you only pay for the requests you make.
Check out our pricing or reach out if you want your own model deployed on our stack.