May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass.…
Model details →DeepSeek: R1 0528 vs MiniMax: MiniMax M2
MiniMax: MiniMax M2 wins on 3 of 6 axes — pricing and capability skew in its favour for most workloads.
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 intelligence across general reasoning, …
Model details →Side-by-side comparison
| Capability | DeepSeek: R1 0528 | MiniMax: MiniMax M2 | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 164K | 197K | 🏆 MiniMax: MiniMax M2 |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $0.45 | $0.26 | 🏆 MiniMax: MiniMax M2 |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $2.15 | $1.00 | 🏆 MiniMax: MiniMax M2 |
| Tool / function calling First-class support for emitting structured tool calls. | Yes | Yes | Tie |
| Vision input Accepts image inputs alongside text. | — | — | Tie |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | Yes | Yes | Tie |
Frequently asked questions
Is DeepSeek: R1 0528 better than MiniMax: MiniMax M2?
MiniMax: MiniMax M2 wins on 3 of 6 axes — pricing and capability skew in its favour for most workloads.
What's the price difference between DeepSeek: R1 0528 and MiniMax: MiniMax M2?
Input: $0.45 vs $0.26 per 1M tokens. Output: $2.15 vs $1.00 per 1M tokens.
What context windows do DeepSeek: R1 0528 and MiniMax: MiniMax M2 support?
DeepSeek: R1 0528 supports up to 164K tokens. MiniMax: MiniMax M2 supports up to 197K tokens.
Do both DeepSeek: R1 0528 and MiniMax: MiniMax M2 support tool calling?
DeepSeek: R1 0528: yes. MiniMax: MiniMax M2: yes.