We’ve launched a new benchmark, Poker Agent, as a collaborative experiment with Graphite Digital. Poker Agent pits 17 models against each other in a head-to-head, ten-handed Poker competition.

• Unlike our other benchmarks, models compete directly against each other, and have to respond to each others actions.
• Models employed a variety of strategies. Some models like Grok 4.1 Fast (Reasoning) were very aggressive, others took a more conservative approach.
• GPT 5.2 was the top overall performer.

Allowing models to compete against each other in a shared, rather than isolated, environment represents an exciting new area of research. Stay tuned for more benchmark releases in 2026!

Evaluations are finished for MiniMax-M2.1.

• It does well among open-weight models on coding tasks, ranking among the top 5 performers in SWE Bench, Terminal Bench and Live Code Bench.
• It also ties for first place among open-weight models on our Case Law v2 benchmark, and is second among open-weight models on our Finance Agent benchmark.
• The model also offers these capabilities at a relatively low cost and latency compared with its peers.

While there remains significant room for improvement, it is a strong open-weight release from MiniMax AI, and we look forward to continued improvements on all our benchmarks.

NOTE: The model was tested with temperature=1 and top_p=0.95, via the Anthropic-compatible MiniMax API endpoint.

Evaluations are finished for GLM 4.7.

• It is neck and neck with DeepSeek V3.2 (Thinking) on SWE Bench, 67.0% and 68.8% respectively. This is a +11% bump on SWE Bench compared to GLM 4.6
• On Terminal Bench, it was one of the top open-weight models, along with DeepSeek V3.2 (Nonthinking)
• Overall, it is more token efficient than its predecessor, and was much cheaper per-task, despite having the same input and output pricing.

The model was tested with temperature=1 and default top_p for all benchmarks but SWE Bench and Terminal Bench, which used temperature=0.7 and top_p=1. Reasoning was enabled for all benchmarks.

MiniMax-M2.1 ranks #2 among open-weight models on our Vals Index, but offers competitive performance with lower latency and cost compared to the #1 ranked open-weight model, GLM 4.7.

• It is #12 on the overall leaderboard, behind models from the likes of OpenAI, Gemini, and Anthropic.
• It ranks in the top 5 open-weight performers in all the benchmarks in our Vals Index.

The model was tested with temperature=1 and top_p=0.95, via the Anthropic-compatible MiniMax API endpoint.

Congratulations to MiniMax for a strong open-weight release! Results on the full suite of benchmarks will be released soon!