We evaluated Gemini 3.1 Pro Preview (02/26) across our full benchmark suite. Here are the key takeaways:

• It is the 3rd best model on both the Vals Index and the Vals Multimodal Index.
• It is first on several of our benchmarks, including: AIME, GPQA, Live Code Bench, Terminal Bench 2, LegalBench, MMMU, MMLU Pro and MedCode.
• The model shows dramatic improvement compared with Gemini 3 Pro (11/25) on Case Law (v2), jumping from rank #50 (53.4% accuracy) to rank #11 (65.6% accuracy)—a 12 percentage point improvement.
• It performs slightly worse than Gemini 3 Pro (11/25) on SWE-bench, achieving 69.6% accuracy.
• It also gets 59.72 on our Finance Agent, which puts it at 3rd place. It beats Gemini 3 Pro (11/25) and GPT 5.2.

One notable metric to call out here is the model achieves this performance at a lower cost than models like Claude Opus 4.6 (Thinking), Claude Sonnet 4.6, GPT 5.2 and o3.

Evaluations were run with a temperature of 1.0 and a “high” thinking level, via the official Google API.

Congratulations to the Google team on another outstanding model!

We evaluated Gemini 3.1 Pro Preview (02/26) on our Vals Index. Here are the key takeaways:

• Gemini 3.1 Pro Preview (02/26) demonstrates strong performance on Terminal Bench 2, achieving 67.4% accuracy and taking first place by a distance, beating out the 2nd-ranked model, Claude Sonnet 4.6, at 59.55%.
• The model shows dramatic improvement compared with Gemini 3 Pro (11/25) on Case Law (v2)**, jumping from rank #50 (53.4% accuracy) to rank #11 (65.6% accuracy)—a 12 percentage point improvement.
• On Corp Fin (v2)‘s Vals Index split, the model achieves 65.7% accuracy. The model ties for 6th place on the Finance Agent subset (achieving 58.9% accuracy), matching Gemini 3 Pro (11/25).
• On SWE-bench, the model achieves 69.6% accuracy, demonstrating solid performance on software engineering tasks, but falling behind Gemini 3 Pro (11/25).

We are evaluating Gemini 3.1 Pro Preview (02/26) on our full suite of benchmarks and will be sharing updates soon!

We’re releasing MedScribe, a new benchmark evaluating AI systems on clinical SOAP note generation, built in collaboration with Harvard Medical School and Protege.

• GPT 5.1 takes the lead, exceeding 88% accuracy. The Claude family also demonstrates strong performance.
• Results suggest models are capable of producing structurally sound, clinically relevant medical notes and can meaningfully assist scribes in their workflow.
• Most models perform marginally worse on the Plan section, where even small errors can affect care continuity and billing accuracy.

We’re releasing MedCode, a new benchmark evaluating AI systems on medical coding from realistic patient documentation, built in collaboration with Harvard Medical School and Protege.

• Models barely exceed 55% accuracy on this critical healthcare task, with Gemini 3 Flash (12/25) taking the lead at 55.9%.
• Models perform better on physical conditions like diabetes and hypertension but struggle significantly with mental health diagnoses.
• Even at the category level, models frequently miss diagnoses entirely rather than mislabelling specificity.