MiniMax M3 - Kyma API

Overview

MiniMax M3 uses MSA sparse attention to process text, image, and video inputs within a 1,048,576-token context window. It supports tool calling, structured outputs, and explicit reasoning traces, making it suitable for multi-step coding tasks and repository-level analysis. On Kyma, the model runs through an OpenAI-compatible endpoint with automatic request failover and exact cost reporting in the usage.cost field. Prompt caching applies to repeated prefixes, and new accounts receive a $0.50 credit to test throughput. Output generation caps at 32,768 tokens, and generation is slow. It is not designed for high-speed conversational streaming or low-latency real-time applications.

Specs

Field	Value
Model ID	`minimax-m3`
Best for	Agentic coding, debugging, long-horizon tasks, multimodal input
Context window	1.048576M
Max output tokens	33K
Input modalities	Text, Image, Video
Output modalities	Text
Tool calling	Yes
Structured outputs	Yes
Prompt caching	Yes
Speed	Medium
Cost band	Balanced
Release stage	Stable

Pricing

	Per 1M tokens
Input	$0.40
Output	$1.59

Use this when

Agentic Code Generation — Handles multi-step repository edits and tool chaining across long context windows.
Multimodal Input Analysis — Processes text, image, and video inputs to extract structured data or debug visual workflows.
Long-Horizon Debugging — Maintains state over extended sessions to trace errors across large codebases.
Structured Output Parsing — Returns deterministic JSON or XML formats for downstream pipeline integration.

Not ideal for

Avoid this model for low-latency chat or high-throughput streaming where sub-second response times are required.

Pick something else when

You need faster token generation for interactive chat: use gemini-3.5-flash-lite.
You need lower cost for high-volume text processing: use deepseek-v3.
You need longer output sequences for document generation: use qwen-3.7-max.

Example

from openai import OpenAI

client = OpenAI(base_url="https://kymaapi.com/v1", api_key="ky-...")

response = client.chat.completions.create(
    model="minimax-m3",
    messages=[{"role": "user", "content": "..."}],
    tools=[...],  # function calling supported
)

Agent query example

Ask the API which models fit, instead of hardcoding an id:

curl "https://kymaapi.com/v1/models?recommended_for=chat&tools=true&quality_tier=frontier-open"

FAQ

Does it support image and video inputs? Yes, the model accepts text, image, and video inputs but outputs text only. How does prompt caching work on Kyma? Repeated prompt prefixes are billed at this model’s cached input rate, reducing costs for iterative agent loops. Can I rely on it for structured JSON outputs? Yes, it natively supports structured outputs and tool calling, which you can enforce via system prompts or API parameters.

​Overview

​Specs

​Pricing

​Use this when

​Not ideal for

​Pick something else when

​Example

​Agent query example

​FAQ