Gemini 3.1 Pro
May 2026 · Proprietary · Current · supported evidence
Google’s Pro that actually shipped. Knowledge and multimodal are real; agentic scores are the hole in the card.
Intelligence
47.7
AA Index
API price
$2 / $12
Input / output per 1M
Context
1M
80 tok/s
Capability
Intelligence Index47.7
Coding index68.8
Agentic index23
Arena Elo (offset)180
Use it when
- ▸Multimodal analysis
- ▸Long documents
Skip if
- –You need agents or the Intelligence Index lead
Benchmarks
GPQA Diamond Graduate-level science questions designed so Google search is not enough. Still one of the cleanest knowledge/reasoning splits. | 94.4% |
|---|---|
Humanity’s Last Exam Expert-written questions across fields. Harder than MMLU; the current differentiator for “does this model actually know things.” | 51.4% |
MMLU-Pro Harder, less-saturated successor to MMLU. Classic MMLU is above 90% for every flagship and no longer ranks the field. | — |
SWE-bench Verified 500 human-validated GitHub issues. Score swings 5–15 points by harness — treat vendor numbers as an upper bound. | 80.6% |
SWE-bench Pro Harder, contamination-resistant coding eval. Currently the best public split between “can code” and “can maintain a repo.” | — |
Terminal-Bench 2.1 End-to-end tasks in a real terminal. Better proxy for coding agents than HumanEval, which is fully saturated. | — |
ARC-AGI-2 Abstract visual puzzles. Rewards generalization over memorization. GPT-5.6 Sol currently leads the published set. | — |
AIME 2025 American Invitational Mathematics Examination. Contest math; reasoning-mode models dominate. | 95.6% |
MMMU College-level multimodal understanding across diagrams, charts, and exam figures. | — |
Modalities
text · vision · audio · video · tools
Reasoning mode
hybrid
Hybrid and reasoning models spend tokens thinking. That raises GPQA and agents, and also raises latency and bill.