MiniMax: MiniMax M1✓ Catalog verified
MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it to process long sequences—up to 1 million tokens—while maintaining competitive FLOP efficiency. With 456 billion total parameters and 45.9B active per token, this variant is optimized for complex, multi-step reasoning tasks. Trained via a custom reinforcement learning pipeline (CISPO), M1 excels in long-context understanding, software engineering, agentic tool use, and mathematical reasoning. Benchmarks show strong performance across FullStackBench, SWE-bench, MATH, GPQA, and TAU-Bench, often outperforming other open models like DeepSeek R1 and Qwen3-235B.
Overview
MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it to process long sequences—up to 1 million tokens—while maintaining competitive FLOP efficiency. With 456 billion total parameters and 45.9B active per token, this variant is optimized for complex, multi-step reasoning tasks. Trained via a custom reinforcement learning pipeline (CISPO), M1 excels in long-context understanding, software engineering, agentic tool use, and mathematical reasoning. Benchmarks show strong performance across FullStackBench, SWE-bench, MATH, GPQA, and TAU-Bench, often outperforming other open models like DeepSeek R1 and Qwen3-235B.
Access: available through the official Minimax API. Context window: 1M.
Specifications
| API identifier | minimax/minimax-m1 |
| Provider | Minimax |
| Model type | Text LLM |
| Context window | 1M catalog |
| Input modalities | text |
| Output modalities | text |
| Released | Jun 17, 2025 |
| Moderated | No |
| Architecture modality | text->text |
API defaults
{
"top_p": null,
"temperature": null,
"frequency_penalty": null
}Pricing
Live pricing components for minimax/minimax-m1 as published in the catalog.
| Pricing component | Raw unit price | Normalized | Unit |
|---|---|---|---|
| Prompt tokens | $4e-7 | $0.40 / 1M | Per input token |
| Completion tokens | $0.0000022 | $2.20 / 1M | Per output token |
| Request fee | N/A | N/A | Per request |
| Image fee | N/A | N/A | Per image unit |
| Web search fee | N/A | N/A | Per search request |
Cost calculator
Estimate monthly spend from your own token volumes.
Estimated Total Cost
$0.64Calculated from current list pricing in the ModelsAtlas catalog.Capabilities
| Capability | Status | What it means |
|---|---|---|
| Visual Understanding | Not advertised | Image and document analysis support |
| Audio Processing | Not advertised | Speech and voice aligned flows |
| Tool Calling | ✓ Supported | Supports tools / function calling |
| Self-Hosting | Not advertised | Deploy outside managed APIs |
Capability signals
Directional signals derived from model metadata and capability tags — not official benchmark submissions.
Quick start
Call MiniMax: MiniMax M1 through an OpenAI-compatible client.
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="YOUR_API_KEY"
)
response = client.chat.completions.create(
model="minimax/minimax-m1",
messages=[{"role": "user", "content": "Explain quantum physics."}]
)
print(response.choices[0].message.content)Alternatives
Closest models by context window from a different provider.
| Model | Provider | Context | Input /1M | Actions |
|---|---|---|---|---|
| Amazon: Nova 2 Lite | Amazon | 1M | $0.30 | View → |
| Amazon: Nova Premier 1.0 | Amazon | 1M | $2.50 | View → |
Sources & attribution
Pricing and metadata are maintained in the ModelsAtlas catalog.
Frequently asked questions
How much does MiniMax: MiniMax M1 cost?
$0.40 per 1M input tokens and $2.20 per 1M output tokens on the official Minimax API.
What is the context window of MiniMax: MiniMax M1?
MiniMax: MiniMax M1 supports up to 1M tokens of context.
Does MiniMax: MiniMax M1 support tool / function calling?
Yes, MiniMax: MiniMax M1 supports tool / function calling — you can register tools and the model will emit structured tool calls.
How do I access MiniMax: MiniMax M1?
Use the official Minimax API with the model id `minimax/minimax-m1`. See the Quick start section above for code examples.