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| 3 models | minimaxMiniMax: MiniMax M2Remove × | minimaxMiniMax: MiniMax M2.5Remove × | minimaxMiniMax: MiniMax-01Remove × |
|---|---|---|---|
| What’s different | MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier… | MiniMax-M2.5 is a SOTA large language model designed for real-world productivity. Trained in a diverse range of complex real-world digital working environments, M2.5 builds upon the coding expertise of M2.1... | MiniMax-01 is a combines MiniMax-Text-01 for text generation and MiniMax-VL-01 for image understanding. It has 456 billion parameters, with 45.9 billion parameters activated per inference, and can handle a… |
| Date | Oct 23, 2025Listed | Feb 12, 2026Listed | Jan 15, 2025Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | $0.255 in / $1.02 out/ 1M tokensMinimax · via OpenRouter · all routes ↗minimax/fp8 · quantization=fp8 | $0.27 in / $0.95 out/ 1M tokensVenice · via OpenRouter · all routes ↗venice | $0.20 in / $1.10 out/ 1M tokensMinimax · via OpenRouter · all routes ↗minimax |
| Provider endpoints | 3 endpoints → | 12 endpoints → | 2 endpoints → |
Specifications | |||
| Context window | 204.8K | 204.8K | 1M |
| Max output | 131.1K | 32.8K | 900.2K |
| Parameters | — | — | — |
| Tool calling | Supported | Supported | Not reported |
| Structured output | Supported | Supported | Not reported |
| Image input | Not reported | Not reported | Supported |
| Reasoning | Supported | Supported | Not reported |
| Prompt caching | Not reported | Supported | Not reported |
| Open weights | Supported | Supported | Supported |