Fast multimodal coding and agent model with 1M context, adjustable reasoning depth, image and video input, function calling, and built-in web search.
MiniMaxText Generation1M contextReleased Oct 19, 2026SingaporeProprietary EndpointNewFast multimodal coding and agent model with 1M context, adjustable reasoning depth, image and video input, function calling, and built-in web search.
Supports text, image, and video input, five reasoning effort levels with reasoning always on, function calling, implicit prompt cache reads, and built-in web search billed per executed search. Reasoning tokens are billed as output tokens.
minimax-m3-1-flash/v1/chat/completionsPOST/v1/responsesPOST/v1/messagesPOST/v1beta/models/minimax-m3-1-flash:generateContentminimax-m3.1-flashminimax/m3.1-flashminimax/minimax-m3.1-flashLive pay-as-you-go rates from the EmpirioLabs catalog. You are billed only for what you use, with no monthly minimum.
MiniMax M3.1 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 minimax-m3-1-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": "minimax-m3-1-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="minimax-m3-1-flash",
messages=[{"role": "user", "content": "Write a haiku about the ocean."}],
)
print(response.choices[0].message.content)Request parameters supported by the MiniMax M3.1 Flash API on EmpirioLabs. Defaults apply when a field is omitted.
| Parameter | Type | Default | Range / values | Description |
|---|---|---|---|---|
| reasoning_effort | enum | max | low, medium, high, xhigh, max | Reasoning is always on; this sets how much effort the model spends before answering. Higher effort uses more reasoning tokens. |
| temperature | number | 1 | 0 to 2 | Controls randomness. Lower values are more deterministic; higher values are more exploratory. |
| top_p | number | 0.95 | 0 to 1 | Controls nucleus sampling by limiting generation to the most likely token mass. |
| max_completion_tokens | integer | 32768 | 1 to 524288 | Maximum generated tokens, including reasoning tokens. |
| stop | array | - | - | Optional stop sequence or list of stop sequences. |
| tool_web_search | boolean | false | - | Search the web for real-time information. Adds $0.01 to the request cost for each invoked call. |
| tools | array | - | - | OpenAI-compatible tool definitions for function calling. |
| tool_choice | object | - | - | Optional OpenAI-compatible tool_choice value. |
| disable_formatting | boolean | false | - | Return raw provider-style output without EmpirioLabs source formatting where supported. |
Text, image, and video input with text output, a 1M token context, and up to 524,288 output tokens. The model always reasons: reasoning_effort sets how deeply (low, medium, high, xhigh, or max; the default is max), and the reasoning text is returned in reasoning_content. Reasoning tokens are billed as output tokens. Repeated prompt prefixes are cached automatically and billed at the cached input rate. Set tool_web_search to true to let the model search the web; each executed search is billed, and one request can run several searches. Answers that used web search end with a Sources list unless disable_formatting is true. Structured output through response_format is not supported; ask for JSON in the prompt instead.
When this model invokes tools (web search, code interpreter, etc.) inside a single request, the response carries a normalized usage.tool_usage map alongside the token counts. The example below shows the shape, exact field names, units, and which tools appear can vary slightly per provider:
"usage": {
"prompt_tokens": 123,
"completion_tokens": 456,
"cost_usd": 0.0042,
"tool_usage": {"web_search": 3, "code_interpreter": 1}
}The tool counts are already factored into cost_usd, they are surfaced for transparency so you can audit per-tool billing. The field is omitted when no tools were invoked.
On EmpirioLabs, MiniMax M3.1 Flash is billed pay as you go. The live rate card on this page always matches what the API charges.
MiniMax M3.1 Flash supports a 1M-token context window with up to 524,288 output tokens per response.
Yes. MiniMax M3.1 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 minimax-m3-1-flash.
Yes. The EmpirioLabs playground runs MiniMax M3.1 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.
