Side by side
See the difference.
Scores, capabilities and costs for the models you’re considering.
Scroll to compare models →
| 2 models | minimaxMiniMax: MiniMax M2.5Remove × | deepseek-aiDeepSeek-V4-ProRemove × |
|---|---|---|
| What’s different | 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... | DeepSeek V4 Pro is an MoE model with 1.6T total parameters (49B active) and a 1M-token context window. It's built for advanced reasoning, coding, and long-running agent tasks, and performs well on… |
| Date | Feb 12, 2026Listed | Apr 22, 2026Listed |
API prices USD · provider configuration shown below | ||
| Listed rate | $0.27 in / $0.95 out/ 1M tokensVenice · via OpenRouter · all routes ↗venice | $0.1775 in / $0.3549 out/ 1M tokensBaidu · via OpenRouter · all routes ↗baidu/fp8 · quantization=fp8 |
| Provider endpoints | 12 endpoints → | 22 endpoints → |
Specifications | ||
| Context window | 204.8K | 1M |
| Max output | 32.8K | 393.2K |
| Parameters | — | — |
| Tool calling | Supported | Supported |
| Structured output | Supported | Supported |
| Reasoning | Supported | Supported |
| Prompt caching | Supported | Supported |
| Open weights | Supported | Supported |