MiniMax · Open weights
MiniMax M1
MiniMax-M1 is a hybrid MoE reasoning model with 40K thinking budget. World's first open-weight, large-scale hybrid-attention model with lightning attention for efficient test-time compute scaling. Excels at complex tasks requiring extensive reasoning.
- Input
- Text
- Output
- Text
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.14
- Output
- $1.33
Reference prices as published by the model provider. API example
Specifications
- Context window
- 1M
- Max output
- 131.1K
Side by side
MiniMax M1 compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| MiniMax M1MiniMaxThis model | Gemini 2.5 FlashGoogleRemove | |
|---|---|---|
| Provider | MiniMax | |
| Discovery tier | Open weights | Efficient |
| Added | Jan 8, 2025 | Jun 5, 2025 |
| Limits | ||
| Context window | 1M | 1.05MBest |
| Max output | 131.07KBest | 65.54K |
| Average output | — | 368 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.14Best | $0.30 |
| Output | $1.33Best | $2.50 |
| Cache read | — | $0.03 |
| Performance | ||
| Throughput | — | 249.0 tok/s |
| Time to first token | — | 4.9 s |
| Request success (24h) | — | 99.4% |
| Benchmark indexes | ||
| Intelligence Index | 10.0Best | 9.9 |
| Math Index | 13.7 | 60.3Best |
| Capabilities | ||
| Reasoning | No | Yes |
| Tools | No | Yes |
| Vision | No | Yes |
| Audio input | No | Yes |
| Video input | No | Yes |
| Reasoning effort | — | low, medium, high |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- MiniMax M1
- Gemini 2.5 Flash
Intelligence Index
- MiniMax M110.0
- Gemini 2.5 Flash9.9
MiniMax M1: Better than 21% of 326 scored models
Indexes
Math Index
Composite of the math evaluations
- MiniMax M1: 13.7
- Gemini 2.5 Flash: 60.3Top
Document reasoning
AA-LCRv1.1
Reasoning across long documents
- MiniMax M1: —
- Gemini 2.5 Flash: 49.9%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- MiniMax M1: 7.8%Top
- Gemini 2.5 Flash: 4.7%
GPQA Diamond
Graduate-level science questions
- MiniMax M1: 68.2%
- Gemini 2.5 Flash: 68.3%Top
MMLU-Pro
Multi-subject knowledge and reasoning
- MiniMax M1: 80.8%
- Gemini 2.5 Flash: 80.9%Top
IFBench
Following precise output instructions
- MiniMax M1: 41.2%Top
- Gemini 2.5 Flash: 39.0%
Coding
LiveCodeBench
Recent competitive programming problems
- MiniMax M1: 65.7%Top
- Gemini 2.5 Flash: 49.5%
Math
AIME 2025
Competition mathematics, 2025 problems
- MiniMax M1: 13.7%
- Gemini 2.5 Flash: 60.3%Top
AIME
Competition mathematics
- MiniMax M1: 81.3%Top
- Gemini 2.5 Flash: 50.0%
MATH-500
Competition math problems
- MiniMax M1: 97.2%Top
- Gemini 2.5 Flash: 93.2%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- MiniMax M1: 2.3%
- Gemini 2.5 Flash: 12.1%Top
τ²-Bench Telecom
Tool use in a customer-support simulation
- MiniMax M1: 31.6%Top
- Gemini 2.5 Flash: 14.9%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- MiniMax M1: —
- Gemini 2.5 Flash: 49.9%
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
- MiniMax M1
- Gemini 2.5 Flash
| Leaderboard | MiniMax M1 | Gemini 2.5 Flash |
|---|---|---|
| Vision Style Control · Captioning | Not in top 20 | #8 · 1211.4 |
| Vision Style Control · Entity Recognition | Not in top 20 | #13 · 1221.3 |
Ranks among Noumen catalog models on LMArena leaderboards. Only top-20 placements are listed.LMArena
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "MiniMax-M1",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="MiniMax-M1",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)MiniMax
Related models
Other MiniMax models, starting with the same family.