Gemini 3.1 Pro PreviewvsGemini 3.0 Pro (Preview 11-2025)
Across 15 shared benchmarks, Gemini 3.1 Pro Preview leads overall: Gemini 3.1 Pro Preview wins 12, Gemini 3.0 Pro (Preview 11-2025) wins 3, with 0 ties and an average score difference of +7.84.
Google Deep Mind · 2026-02-20 · Multimodal model
Google Deep Mind · 2025-11-18 · Multimodal model
Benchmark scores
Grouped by capability, sorted by largest gap within each. 15 shared benchmarks.
General Knowledge
Gemini 3.1 Pro Preview 4/4| Benchmark | Gemini 3.1 Pro Preview | Gemini 3.0 Pro (Preview 11-2025) | Diff |
|---|---|---|---|
| ARC-AGI-2 | 77.109 / 62Thinking High (No Tools) | 45.1026 / 62 | +32 |
| LiveBench | 79.933 / 115Thinking High (No Tools) | 73.3924 / 115Thinking High (No Tools) | +6.54 |
| HLE | 51.4022 / 172Thinking High (With Tools) | 45.8040 / 172 | +5.60 |
| GPQA Diamond | 94.303 / 187Thinking High (No Tools) | 93.805 / 187 | +0.50 |
Agent Level Benchmark
Gemini 3.1 Pro Preview 2/2| Benchmark | Gemini 3.1 Pro Preview | Gemini 3.0 Pro (Preview 11-2025) | Diff |
|---|---|---|---|
| τ²-Bench | 90.802 / 43Thinking High (With Tools) | 85.408 / 43 | +5.40 |
| τ²-Bench - Telecom | 99.301 / 35Thinking High (With Tools) | 985 / 35 | +1.30 |
AI Agent - Tool Usage
Gemini 3.1 Pro Preview 2/2| Benchmark | Gemini 3.1 Pro Preview | Gemini 3.0 Pro (Preview 11-2025) | Diff |
|---|---|---|---|
| Terminal Bench 2.0 | 68.508 / 47Thinking High (With Tools) | 56.9025 / 47 | +11.60 |
| MCP-Atlas | 78.209 / 27Thinking High (With Tools) | 70.3015 / 27Normal (With Tools) | +7.90 |
Coding and Software Engineer
Even 2/2| Benchmark | Gemini 3.1 Pro Preview | Gemini 3.0 Pro (Preview 11-2025) | Diff |
|---|---|---|---|
| SWE-bench Verified | 80.6011 / 112Thinking High (With Tools) | 76.2036 / 112 | +4.40 |
| LiveCodeBench | 91.703 / 123Thinking High (With Tools) | 922 / 123 | -0.30 |
Math and Reasoning
Gemini 3.0 Pro (Preview 11-2025) 2/2| Benchmark | Gemini 3.1 Pro Preview | Gemini 3.0 Pro (Preview 11-2025) | Diff |
|---|---|---|---|
| FrontierMath - Tier 4 | 16.7020 / 80Normal (No Tools) | 18.8016 / 80 | -2.10 |
| FrontierMath | 36.9011 / 60Thinking High (No Tools) | 3810 / 60 | -1.10 |
AI Agent - Information Search
Gemini 3.1 Pro Preview 1/1| Benchmark | Gemini 3.1 Pro Preview | Gemini 3.0 Pro (Preview 11-2025) | Diff |
|---|---|---|---|
| BrowseComp | 85.905 / 53Thinking High (With Tools + Internet) | 59.2038 / 53 | +26.70 |
Claw-style Agent Evaluation
Gemini 3.1 Pro Preview 1/1| Benchmark | Gemini 3.1 Pro Preview | Gemini 3.0 Pro (Preview 11-2025) | Diff |
|---|---|---|---|
| Pinch Bench | 86.7010 / 37Thinking (With Tools) | 70.7031 / 37Thinking (With Tools) | +16 |
Commonsense Reasoning
Gemini 3.1 Pro Preview 1/1| Benchmark | Gemini 3.1 Pro Preview | Gemini 3.0 Pro (Preview 11-2025) | Diff |
|---|---|---|---|
| Simple Bench | 79.602 / 63Normal (No Tools) | 76.405 / 63Thinking (No Tools) | +3.20 |
Specs
| Field | Gemini 3.1 Pro Preview | Gemini 3.0 Pro (Preview 11-2025) |
|---|---|---|
| Publisher | Google Deep Mind | Google Deep Mind |
| Release date | 2026-02-20 | 2025-11-18 |
| Model type | Multimodal model | Multimodal model |
| Architecture | Dense | Dense |
| Parameters | Not available | Not available |
| Context length | 1M | 1000K |
| Max output | 64K | 64K |
API pricing
Prices use DataLearner records when available; missing fields are not inferred.
| Item | Gemini 3.1 Pro Preview | Gemini 3.0 Pro (Preview 11-2025) |
|---|---|---|
| Text input | $2 / 1M tokens | $2 / 1M tokens |
| Text output | $12 / 1M tokens | $12 / 1M tokens |
Summary
- Gemini 3.1 Pro Previewleads in:General Knowledge (4/4), Agent Level Benchmark (2/2), AI Agent - Tool Usage (2/2), AI Agent - Information Search (1/1), Claw-style Agent Evaluation (1/1), Commonsense Reasoning (1/1)
- Gemini 3.0 Pro (Preview 11-2025)leads in:Math and Reasoning (2/2)
- Tied in:Coding and Software Engineer
On average across the 15 shared benchmarks, Gemini 3.1 Pro Preview scores 7.84 higher.
Largest single-benchmark gap: ARC-AGI-2 — Gemini 3.1 Pro Preview 77.10 vs Gemini 3.0 Pro (Preview 11-2025) 45.10 (+32).
Page generated from structured model, pricing and benchmark records. No real-time LLM is used to write the prose.