We evaluated MiniMax-M2.7 across our benchmarks.

• It placed #12 (60.14%) on the Vals Index, comparable to both GLM 5 and Claude Sonnet 4.5 (Thinking).
• On the Vals Index, MiniMax-M2.7 marks a significant jump over both MiniMax-M2.5 and MiniMax-M2.1, a rapid rate of improvement over the last three months.
• MiniMax-M2.7 had a strong performance on coding benchmarks, ranking #10 on SWE-Bench beating out Gemini 3 Pro (11/25) and #12 on Terminal-Bench 2, beating out its predecessor MiniMax-M2.5.
• Performance was weak on our Formal Math benchmark scoring just 3% on Proof Bench ranking at #23.
• On legal and finance tasks, results were mixed - it scored #19 on our Finance Agent v1.1 and #24 on our Corp Fin benchmark.

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

Congratulations to MiniMax on the release!

We evaluated GPT 5.4 Mini across our benchmarks.

• It had quite strong performance for its cost, coming in at #13 on the Vals Index (equivalent to GPT 5).
• Its performance on Vibe Code Bench was especially impressive, coming in at #7, and beating out both Gemini 3 Flash (12/25) and Claude Haiku 4.5 (Thinking).
• GPT 5.4 Mini also had a strong performance on other coding benchmarks, ranking #11 on SWE-Bench Bench and #18 on on Terminal-Bench 2, again beating out GPT 5.

All evaluations were ran using the official OpenAI API using “xhigh” reasoning, except on Terminal Bench 2, where we used “high”. The numbers on the Vals Index are finalized, but on certain benchmarks, we are still confirming results based on the latest parameters.

We evaluated GPT 5.4 Nano across our full benchmark suite.

• GPT 5.4 Nano ranks #18 on both our Vals Index and #16 on the Vals Multimodal Index.
• Its performance on Vibe Code Bench was impressive, coming in at #10, and beating out both Gemini 3 Flash (12/25) and Claude Haiku 4.5 (Thinking)
• On SWE-bench, it scored 69.0%, comparable to GPT 5 Codex (69.4%) at a 20x lower cost.
• At ~$0.05 per test on the index, the model offers strong cost-effectiveness for its tier.

Evaluations were run via the official OpenAI API using “high” reasoning.

We evaluated Grok 4.20 Beta (Reasoning) across our full benchmark suite.

• Grok 4.20 Beta (Reasoning) ranks #13/33 on Vals Index (57.70% accuracy) and #14/23 on Vals Multimodal Index (53.96%).
• It achieves strong results on academic and coding-heavy benchmarks, including #5 on AIME (96.46%), #6 on GPQA (88.64%), #9 on MMMU (83.47%), and #9 on SWE-bench (74.20%).
• On our finance and tax benchmarks, it delivers solid performance with #15 on Corp Fin (v2) (63.68%), #14 on Finance Agent (v1.1) (52.29%), and #16 on Tax Eval (v2) (74.12%).
• On healthcare tasks, performance is mixed: #16 on MedQA (94.55%), alongside #36 on MedCode (32.16%) and #43 on MedScribe (63.41%).
• On legal benchmarks, it currently ranks #30 on Case Law (v2) (54.45%) and #62 on LegalBench (77.74%).

Overall, the model generally is an improvement over previous xAI models, with room for improvement in certain domains.

Evaluations were run with a temperature of 0.7 and a top_p of 0.95 via the xAI API. This model is still in beta, and we will update results as and when updates are released by xAI.