Google, proprietary

# Gemini 3.1 Pro Preview

> Gemini 3.1 Pro Preview by Google, released February 2026. Ranked #23 of 354 with a Noometry Index of 56.7. API: $2 in / $12 out per M tokens. 1.05M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/gemini-3-1-pro-preview
- Last updated: 2026-10-10
- Title: Gemini 3.1 Pro Preview Benchmarks, Price & Rank (October 2026)

Gemini 3.1 Pro Preview by Google ranks 23rd of 354 ranked models on the Noometry Index as of October 2026, with a score of 56.7. Its strongest category is knowledge, where it ranks 3rd. API pricing starts at $2 per million input tokens and $12 per million output tokens, with a 1.05M-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #23 of 354
- **Index score:** 56.7
- **Evidence:** Confirmed 71 results
- **Provider:** [![](/logos/google.svg) Google](https://noometry.com/providers/google)
- **Released:** February 19, 2026
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 1.05M
- **Max output:** 66K
- **Input price:** $2 / M
- **Output price:** $12 / M
- **Blended price:** $4.50 / M
- **Output speed:** Not measured
- **Value:** #175 of 219
- **Knowledge cutoff:** January 2025
- **Input:** text, image, video, audio, pdf

## Category scores

Each category score combines every public result we have in that category.

Gemini 3.1 Pro Preview category scores

1.  Coding 42.5
2.  Agentic & Tool Use 37.7
3.  Reasoning 71.7
4.  Math 62.1
5.  Knowledge 71.8
6.  Multimodal 37.9
7.  Multilingual 57.0
8.  Instruction Following 77.0
9.  Long Context 47.4
10.  Writing & Preference 66.1
11.  020406080

Gemini 3.1 Pro Preview category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 42.5 | #99 | 8 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 37.7 | #34 | 9 |
| [Reasoning](https://noometry.com/best/reasoning) | 71.7 | #12 | 13 |
| [Math](https://noometry.com/best/math) | 62.1 | #34 | 6 |
| [Knowledge](https://noometry.com/best/knowledge) | 71.8 | #3 | 5 |
| [Multimodal](https://noometry.com/best/multimodal) | 37.9 | #69 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 57.0 | #12 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 77.0 | #32 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 47.4 | #18 | 3 |
| [Writing & Preference](https://noometry.com/best/writing) | 66.1 | #37 | 5 |

## Strengths and weaknesses

Categories where Gemini 3.1 Pro Preview places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Gemini 3.1 Pro Preview: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Knowledge](https://noometry.com/best/knowledge) | 71.8 | +34.5 | #3 of 314, top 1% |
| [Reasoning](https://noometry.com/best/reasoning) | 71.7 | +48.1 | #12 of 350, top 4% |
| [Multilingual](https://noometry.com/best/multilingual) | 57.0 | +9.6 | #12 of 297, top 5% |

### Weakest categories

Gemini 3.1 Pro Preview: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Multimodal](https://noometry.com/best/multimodal) | 37.9 | −0.7 | #69 of 128, top 54% |
| [Coding](https://noometry.com/best/coding) | 42.5 | +3.8 | #99 of 340, top 30% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 37.7 | +7.4 | #34 of 154, top 23% |

## Closest competitors

The models ranked just above and below Gemini 3.1 Pro Preview. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Gemini 3.1 Pro Preview
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Claude Opus 4.7](https://noometry.com/models/claude-opus-4-7) | #19 | 58.3 | $10 | 33 | [Compare](https://noometry.com/compare/claude-opus-4-7-vs-gemini-3-1-pro-preview) |
| [Claude Opus 4.6](https://noometry.com/models/claude-opus-4-6) | #20 | 58.2 | $10 | 19 | [Compare](https://noometry.com/compare/claude-opus-4-6-vs-gemini-3-1-pro-preview) |
| [Grok 4.6](https://noometry.com/models/grok-4-6) | #21 | 56.9 | $3 | — | [Compare](https://noometry.com/compare/gemini-3-1-pro-preview-vs-grok-4-6) |
| [Qwen3.8 Max](https://noometry.com/models/qwen3-8-max) | #22 | 56.8 | $3 | — | [Compare](https://noometry.com/compare/gemini-3-1-pro-preview-vs-qwen3-8-max) |
| [Gemini 4 Argon](https://noometry.com/models/gemini-4-argon) | #24 | 56.5 | — | — | [Compare](https://noometry.com/compare/gemini-3-1-pro-preview-vs-gemini-4-argon) |
| [Grok 4.5](https://noometry.com/models/grok-4-5) | #25 | 55.0 | $3 | 4 | [Compare](https://noometry.com/compare/gemini-3-1-pro-preview-vs-grok-4-5) |
| [GLM-5.3](https://noometry.com/models/glm-5-3) | #26 | 54.8 | $2.15 | — | [Compare](https://noometry.com/compare/gemini-3-1-pro-preview-vs-glm-5-3) |
| [Muse Spark 1.3](https://noometry.com/models/muse-spark-1-3) | #27 | 54.8 | $2 | — | [Compare](https://noometry.com/compare/gemini-3-1-pro-preview-vs-muse-spark-1-3) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

Gemini 3.1 Pro Preview Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified](https://noometry.com/benchmarks/swe-bench-verified) | 75.6% | #12 of 32, top 38% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-24 |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 11.7% | #29 of 29, top 100% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1447 | #56 of 113, top 50% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SciCode](https://noometry.com/benchmarks/scicode) | 58.9% | #11 of 121, top 10% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GSO](https://noometry.com/benchmarks/gso-bench) | 22.6% | #13 of 31, top 42% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 72.1% | #17 of 119, top 15% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1484 | #39 of 294, top 14% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MirrorCode](https://noometry.com/benchmarks/mirrorcode) | 8.9% | #9 of 9, top 100% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 1,161 | #35 of 105, top 34% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [AlgoTune](https://noometry.com/benchmarks/algotune) | 2.02 | #2 of 18, top 12% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

Gemini 3.1 Pro Preview Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 80.2% | #4 of 41, top 10% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 35.3% | #41 of 49, top 84% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [τ²-bench Banking](https://noometry.com/benchmarks/tau2-banking) | 26% | #16 of 26, top 62% | high | [τ²-bench](https://taubench.com/) | 2026-05-05 |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 47.8% | #10 of 24, top 42% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 44.5% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [PostTrainBench](https://noometry.com/benchmarks/posttrainbench) | 22% | #10 of 11, top 91% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BALROG](https://noometry.com/benchmarks/balrog) | 57% | #5 of 35, top 15% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ExploitBench](https://noometry.com/benchmarks/exploitbench) | 26.1% | #4 of 9, top 45% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GBAEval](https://noometry.com/benchmarks/gbaeval) | 0.8% | #19 of 23, top 83% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 17% | #24 of 36, top 67% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 17% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Search](https://noometry.com/benchmarks/arena-search) | 1211 | #9 of 32, top 29% |  | [LMArena](https://lmarena.ai/leaderboard/search) | 2026-08-24 |
| [METR Time Horizons](https://noometry.com/benchmarks/metr-time-horizons) | 77% | #3 of 32, top 10% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 911.21 |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 3,774 | #38 of 60, top 64% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Gemini 3.1 Pro Preview Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 77.1% | #16 of 83, top 20% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 79.6% | #3 of 77, top 4% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 97.4% | #2 of 91, top 3% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 98% | #6 of 83, top 8% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 17.7% | #31 of 134, top 24% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 55% | #7 of 129, top 6% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-19 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 49% |  | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) | 36.8% | #3 of 38, top 8% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Thematic Generalization](https://noometry.com/benchmarks/thematic-generalization) | 79.4% | #3 of 23, top 14% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/generalization) |  |
| [EBR-Bench](https://noometry.com/benchmarks/ebr-bench) | 14.3% | #16 of 24, top 67% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-25 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1485 | #22 of 297, top 8% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 34% | #21 of 74, top 29% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-27 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 29% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 32% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 95.5% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 94.9% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 97.1% | #8 of 151, top 6% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 53.8% | #15 of 125, top 12% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 51.2% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 52.8% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 154.77 | #33 of 213, top 16% |  | [Epoch AI](https://epoch.ai/eci) | 2026-02-19 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 59 | #43 of 72, top 60% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 58.1 |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

Gemini 3.1 Pro Preview Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 59.6% | #35 of 81, top 44% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-11 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 26.8% | #35 of 63, top 56% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-11 |
| [MathArena Final-Answer Competitions](https://noometry.com/benchmarks/matharena) | 86.5% | #4 of 29, top 14% |  | [MathArena](https://matharena.ai/) |  |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 95.6% | #31 of 173, top 18% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-20 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 95.6% |  | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 26% | #43 of 77, top 56% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1485 | #24 of 285, top 9% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 36.9% | #13 of 68, top 20% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-19 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 16.7% | #12 of 55, top 22% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-19 |

### Knowledge

Gemini 3.1 Pro Preview Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 94.1% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-20 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 94.4% | #7 of 186, top 4% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 46.4% | #3 of 41, top 8% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 73.5% | #3 of 77, top 4% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 10.4% | #57 of 96, top 60% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1485 | #37 of 273, top 14% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

Gemini 3.1 Pro Preview Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1296 | #18 of 122, top 15% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [Blueprint-Bench 2](https://noometry.com/benchmarks/blueprint-bench-2) | 26.5% | #20 of 31, top 65% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Furniture Assembly](https://noometry.com/benchmarks/furniture-assembly) | 26.7% | #25 of 31, top 81% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 |
| [LMArena Document](https://noometry.com/benchmarks/arena-document) | 1444 | #26 of 38, top 69% |  | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 |

### Multilingual

Gemini 3.1 Pro Preview Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1477 | #12 of 297, top 5% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1529 | #17 of 285, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1487 | #23 of 223, top 11% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1491 | #13 of 231, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1493 | #9 of 211, top 5% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1455 | #15 of 213, top 8% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1498 | #8 of 283, top 3% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1479 | #14 of 226, top 7% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Gemini 3.1 Pro Preview Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1466 | #28 of 298, top 10% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Gemini 3.1 Pro Preview Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [CL-bench](https://noometry.com/benchmarks/cl-bench) | 20.8% | #5 of 19, top 27% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CL-bench Life](https://noometry.com/benchmarks/cl-bench-life) | 16.9% | #5 of 13, top 39% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1483 | #17 of 291, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Gemini 3.1 Pro Preview Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1481 | #15 of 297, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1482 | #11 of 295, top 4% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1491 | #58 of 115, top 51% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [EQ-Bench 4](https://noometry.com/benchmarks/eqbench-4) | 1142 | #21 of 28, top 75% |  | [EQ-Bench](https://eqbench.com/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1488 | #12 of 295, top 5% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Gemini 3.1 Pro Preview API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [google](https://ai.google.dev/gemini-api/docs/models) | $2 | $12 | $0.20 | 2026-10-10 |
| [openrouter](https://openrouter.ai/google/gemini-3.1-pro-preview) | $2 | $12 | $0.20 | 2026-10-10 |
| [vertex](https://cloud.google.com/vertex-ai/generative-ai/docs/models) | $2 | $12 | $0.20 | 2026-10-10 |

[All Google API prices →](https://noometry.com/llm-pricing/google) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare Gemini 3.1 Pro Preview

-   [Gemini 3.1 Pro Preview vs Qwen3.8 Max](https://noometry.com/compare/gemini-3-1-pro-preview-vs-qwen3-8-max)
-   [Gemini 3.1 Pro Preview vs Gemini 4 Argon](https://noometry.com/compare/gemini-3-1-pro-preview-vs-gemini-4-argon)
-   [Gemini 3.1 Pro Preview vs Grok 4.6](https://noometry.com/compare/gemini-3-1-pro-preview-vs-grok-4-6)
-   [Gemini 3.1 Pro Preview vs Grok 4.5](https://noometry.com/compare/gemini-3-1-pro-preview-vs-grok-4-5)
-   [Gemini 3.1 Pro Preview vs Claude Opus 4.6](https://noometry.com/compare/claude-opus-4-6-vs-gemini-3-1-pro-preview)
-   [Gemini 3.1 Pro Preview vs GLM-5.3](https://noometry.com/compare/gemini-3-1-pro-preview-vs-glm-5-3)
-   [Gemini 3.1 Pro Preview vs GPT-6 Astra](https://noometry.com/compare/gemini-3-1-pro-preview-vs-gpt-6-astra)
-   [Gemini 3.1 Pro Preview vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gemini-3-1-pro-preview)
-   [Gemini 3.1 Pro Preview vs Kimi K3](https://noometry.com/compare/gemini-3-1-pro-preview-vs-kimi-k3)
-   [Gemini 3.1 Pro Preview vs Muse Spark 1.3](https://noometry.com/compare/gemini-3-1-pro-preview-vs-muse-spark-1-3)
-   [Gemini 3.1 Pro Preview vs DeepSeek V4 Pro](https://noometry.com/compare/deepseek-v4-pro-vs-gemini-3-1-pro-preview)
-   [Gemini 3.1 Pro Preview vs MiMo-V2.6-Pro](https://noometry.com/compare/gemini-3-1-pro-preview-vs-mimo-v2-6-pro)

## Other Google models

-   [Gemini 3.8 Flash](https://noometry.com/models/gemini-3-8-flash)61.8
-   [Gemini 3.7 Flash](https://noometry.com/models/gemini-3-7-flash)59.8
-   [Gemini 4 Argon](https://noometry.com/models/gemini-4-argon)56.5
-   [Gemini 3 Pro](https://noometry.com/models/gemini-3-pro)54.8
-   [Gemini 3.5 Flash](https://noometry.com/models/gemini-3-5-flash)54.2
-   [Gemini 3.6 Flash](https://noometry.com/models/gemini-3-6-flash)54.1
-   [Gemini 3 Flash Preview](https://noometry.com/models/gemini-3-flash-preview)52.3
-   [Gemini 2.5 Pro](https://noometry.com/models/gemini-2-5-pro)45.0

## Frequently asked questions

### How good is Gemini 3.1 Pro Preview?

Gemini 3.1 Pro Preview by Google ranks 23rd of 354 ranked models on the Noometry Index as of October 2026, with a score of 56.7. Its strongest category is knowledge, where it ranks 3rd. API pricing starts at $2 per million input tokens and $12 per million output tokens, with a 1.05M-token context window.

### How much does Gemini 3.1 Pro Preview cost?

Gemini 3.1 Pro Preview costs $2 per million input tokens and $12 per million output tokens on Google's own API, with cached input at $0.20.

### What is Gemini 3.1 Pro Preview's context window?

Gemini 3.1 Pro Preview accepts up to 1.05M tokens of input and can write up to 66K tokens in one response.

### Is Gemini 3.1 Pro Preview open source?

No. Gemini 3.1 Pro Preview is proprietary and available only through Google's API and partner platforms.

### What are Gemini 3.1 Pro Preview's strengths and weaknesses?

Relative to other ranked models, Gemini 3.1 Pro Preview places best in knowledge, reasoning, multilingual and lowest in multimodal, coding, agentic & tool use.

### What is Gemini 3.1 Pro Preview best at?

Its best category is knowledge, where it ranks 3rd on Noometry.

### Cite this page

Noometry. (2026). Gemini 3.1 Pro Preview benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/gemini-3-1-pro-preview

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/gemini-3-1-pro-preview.md).