MiniMax M3
everyais/minimax-m3
MiniMax M3 (everyais/minimax-m3) availability, capabilities, context limits, and public reference pricing on everyais.
Method
The page starts with current public catalog metadata and reference costs; operational metrics are added only when measured samples are available.
Source and update
GET /models/catalogUpdated
- Model type
- Chat
- Released
- Not published
- Context window
- 1.0M
- Price unit
- Per 1M tokens
- Maximum output
- 1,048,576
- Model reference price (USD)
- Input / 1M tokens
- $0.3
- Output / 1M tokens
- $1.2
Capabilities
- No capability information published
Supported endpoints
- /v1/chat/completions
Benchmarks
Evaluation results published by external sources. Only mappings approved by a human administrator are shown.
| Benchmark | Score | Source |
|---|---|---|
| aime-2024-2025 | 71.1% | Epoch AI Benchmarking HubCC-BYSource model label: MiniMax-M3 |
| eci | 146.57 (source-specific scale) | Epoch AI Benchmarking HubCC-BYSource model label: MiniMax-M3 |
| gpqa-diamond | 90.9% | Epoch AI Benchmarking HubCC-BYSource model label: MiniMax-M3 |
| scicode | 45.4% | Epoch AI Benchmarking HubCC-BYSource model label: MiniMax-M3 |
Scores are reproduced as published; everyais does not re-measure them. Scores using different units cannot be compared. See benchmark sources and licenses.
Usage and availability trend
Collecting data. Trends appear after more than one day of usage is recorded.
Code example
Use the OpenAI SDK by changing only base_url.
from openai import OpenAI
client = OpenAI(
api_key="everyais_...",
base_url="https://api.everyais.com/v1",
)
response = client.chat.completions.create(
model="everyais/minimax-m3",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)Compare models side by side
Compare capabilities, context windows, and reference prices to find a model for your use case. Your actual billed rate can differ by account.