Model:
Sampling temperature.
Maximum output tokens.
USD estimate $0.0205-$0.041: highest recharge package uses $1 = 2,000 credits; entry package uses $1 = 1,000 credits.
History
Saved locally in this browser
0 running · 0 completed
Input
≈ $0.0012-$0.0024
Output
≈ $0.005-$0.01
Cache
≈ $0.0015-$0.003
Context
Max output
Model overview
MiniMax M3 is a large language model for long-document understanding, complex reasoning, tool use, and multilingual conversational applications.
Evaluate the model against reasoning depth, workflow fit, and production constraints before standardizing a route.
APIAny exposes MiniMax M3 through one documented API surface with live pricing, model status, request examples, and an on-page playground. Teams can evaluate the model here, then keep authentication, usage records, and production routing in the same platform.
Try MiniMax M3 in the playground
Use cases
These workflows show where MiniMax M3 fits in a real product. Test the same inputs in the playground, compare the output with related models, and choose the route that meets your quality, latency, and budget requirements.
01
Apply MiniMax M3 to contracts, reports, research collections, and large technical documents. Structured prompts can extract evidence, compare sections, and produce summaries that retain links to the original context.
Try MiniMax M3 in the playground
02
Use MiniMax M3 to decompose difficult questions, compare evidence, and produce decision-ready analysis. Ask the model to distinguish sourced facts, assumptions, and recommendations so reviewers can verify the result.
Try MiniMax M3 in the playground
03
Use MiniMax M3 to classify tasks, select tools, prepare structured arguments, and summarize results inside a larger workflow. Keep permissions and action validation outside the model boundary.
Try MiniMax M3 in the playground
APIAny advantages
APIAny combines direct model access with the operational controls needed to move from evaluation to production without maintaining a separate integration for every provider.
Call MiniMax M3 with an APIAny key and a documented request format. Keep your application integration stable while model versions and upstream routes evolve behind the gateway.
Review the live MiniMax M3 pricing rules before a request, estimate credits in the playground, and inspect request-level usage records after the call completes.
Use model status, channel health, retry policy, and usage logs to operate MiniMax M3 as part of a production workflow instead of treating it as an isolated demo.
Core capabilities
The following capabilities explain the practical input, output, and workflow characteristics that matter when evaluating MiniMax M3. Available request parameters remain visible in the live API reference on this page.
/01
Use a large context window for codebases, documents, conversation history, and multi-step instructions while monitoring token cost.
/02
Use MiniMax M3 to break down multi-step problems, compare alternatives, and produce structured conclusions that can be reviewed.
/03
Let MiniMax M3 prepare or select tool calls while your application enforces permissions, validates arguments, and records actions.
/04
Build cross-language understanding and generation workflows with MiniMax M3, then validate locale-specific terminology and tone.
/05
Render supported MiniMax M3 text output incrementally for interactive assistants and long-running generation tasks.
Integration
Move from a playground test to an authenticated production request in three steps. The page keeps the model identifier, request schema, pricing, and response examples together. Move from a representative prompt to monitored production traffic with explicit quality and fallback checks.
Sign in to APIAny, create a project API key, and assign only the model scope and budget controls your application needs.
Copy the request example from this page, set the model field to the selected MiniMax M3 version, and send it to the documented APIAny endpoint.
Track status, latency, credits, and returned usage. Compare versions or related models with the same workload before shifting production traffic.
FAQ
Direct answers about capabilities, use cases, integration, and choosing the right MiniMax M3 route.
MiniMax M3 is a large language model for long-document understanding, complex reasoning, tool use, and multilingual conversational applications. APIAny makes this model callable through an authenticated API with live pricing, status information, request examples, and a browser playground on the same page.
Common MiniMax M3 workflows include Long-document analysis, Research and complex reasoning, Tool-connected workflow automation. The best fit depends on the input format, output quality, latency, and cost your product requires.
Create an APIAny key, use the endpoint documented on this page, and set the request model field to the selected MiniMax M3 version. The playground can generate a working payload before you integrate it into application code.
Compare the versions shown on this page by price, supported parameters, and capabilities such as Long-context processing, Complex reasoning, Tool and agent workflows. Test a representative production input before choosing the default route.
Authentication
Every request needs a Bearer token in the Authorization header. Create an API key in the console.
Authorization: Bearer YOUR_API_KEYhttps://apiany.ai/v1/chat/completionsRequest parameters
| Parameter | Type | Required | Notes |
|---|---|---|---|
model | string | Required | Model identifier to invoke. Use this model's ID. |
messages | array | Required | Conversation history in OpenAI chat format (role + content). |
stream | boolean | Optional | When true, the response streams back as server-sent events. |
Request example
curl "https://apiany.ai/v1/chat/completions" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "minimax-m3",
"messages": [
{
"role": "user",
"content": "Summarize APIAny.AI's multi-channel model aggregation in three sentences, and give one production recommendation."
}
],
"temperature": 0.7,
"max_tokens": 4096
}'Response example
{
"id": "chatcmpl-abc123",
"object": "chat.completion",
"model": "minimax-m3",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello! How can I help?"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 12,
"completion_tokens": 18,
"total_tokens": 30
}
}
