开源模型首次在性能上追平顶级闭源模型，且推理速度和成本优势巨大，做 AI 应用开发或模型选型的团队值得立即体验。
开发者 Santiago 表示首次感到开源权重模型不可忽视，MiniMax-M2.7 以 230B 参数在 SambaNova 上实现 440+ tokens/s 的极速推理。该模型在 SWE-Pro 上得分 56.22%，Terminal Bench 2 得分 57.0%，SWE Multilingual 得分 76.5%，性能接近 Opus 4.6 和 GPT-5.4 级别。使用成本仅为专有模型的 5%，且完全开源。SambaNova 提供免费 playground 供测试。
For the first time, I feel open-weight models are …
For the first time, I feel open-weight models are impossible to ignore.
We are at a point where these models are competitive with the best models out there.
MiniMax-M2.7 is the latest beast to come out, and I'm running it at 440+ tokens/s.
230B parameters. It's a beast.
Just for comparison, I've found that Gemma4 31b is good enough for many of the things I do, so imagine what an additional 200B parameters bring here.
Of course, I can't run MiniMax-M2.7 locally, so I use SambaNova.
• Extremely fast inference (probably one of the fastest in the market) • Extremely cheap (around 5% of what you'd pay for proprietary models) • MiniMax 2.7 scores 56.22% on SWE-Pro, 57.0% on Terminal Bench 2, and 76.5% on SWE Multilingual
That puts it around the Opus 4.6/GPT-5.4 league, with the difference that MiniMax is open-weight.
I recorded a real-time video of MiniMax-M2.7 running on SambaNova, so you can get a sense of how fast it is. I'm not using streaming here. The model is consistently running at ~440 tokens/second.
Use this playground to test MiniMax M2.7: https://t.co/c2jUA8PsJh
Thanks to the team for partnering with me on this post.