Google, proprietary

# Gemini 3 Pro

> Gemini 3 Pro by Google, released November 2025. Ranked #28 of 354 with a Noometry Index of 54.8. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/gemini-3-pro
- Last updated: 2026-10-10
- Title: Gemini 3 Pro Benchmarks, Price & Rank (October 2026)

Gemini 3 Pro by Google ranks 28th of 354 ranked models on the Noometry Index as of October 2026, with a score of 54.8. Its strongest category is multimodal, where it ranks 2nd.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #28 of 354
- **Index score:** 54.8
- **Evidence:** Confirmed 67 results
- **Provider:** [![](/logos/google.svg) Google](https://noometry.com/providers/google)
- **Released:** November 18, 2025
- **Weights:** Proprietary
- **Reasoning:** Unknown
- **Context window:** —
- **Max output:** —
- **Input price:** Not listed
- **Output price:** Not listed
- **Blended price:** Not listed
- **Output speed:** 1 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** Not ranked
- **Knowledge cutoff:** Unknown

## Category scores

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

Gemini 3 Pro category scores

1.  Coding 51.6
2.  Agentic & Tool Use 40.6
3.  Reasoning 52.5
4.  Math 49.9
5.  Knowledge 64.4
6.  Multimodal 57.6
7.  Multilingual 56.9
8.  Instruction Following 76.3
9.  Long Context 44.0
10.  Writing & Preference 66.4
11.  304050607080

Gemini 3 Pro category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 51.6 | #39 | 7 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 40.6 | #23 | 10 |
| [Reasoning](https://noometry.com/best/reasoning) | 52.5 | #31 | 9 |
| [Math](https://noometry.com/best/math) | 49.9 | #59 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 64.4 | #16 | 6 |
| [Multimodal](https://noometry.com/best/multimodal) | 57.6 | #2 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 56.9 | #16 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 76.3 | #45 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 44.0 | #79 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 66.4 | #35 | 5 |

## Strengths and weaknesses

Categories where Gemini 3 Pro places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Gemini 3 Pro: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Multimodal](https://noometry.com/best/multimodal) | 57.6 | +19.0 | #2 of 128, top 2% |
| [Knowledge](https://noometry.com/best/knowledge) | 64.4 | +27.1 | #16 of 314, top 6% |
| [Multilingual](https://noometry.com/best/multilingual) | 56.9 | +9.5 | #16 of 297, top 6% |

### Weakest categories

Gemini 3 Pro: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 44.0 | +3.1 | #79 of 296, top 27% |
| [Math](https://noometry.com/best/math) | 49.9 | +13.3 | #59 of 327, top 19% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 40.6 | +10.3 | #23 of 154, top 15% |

## Closest competitors

The models ranked just above and below Gemini 3 Pro. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Gemini 3 Pro
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Gemini 4 Argon](https://noometry.com/models/gemini-4-argon) | #24 | 56.5 | — | — | [Compare](https://noometry.com/compare/gemini-3-pro-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-pro-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-pro-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-pro-vs-muse-spark-1-3) |
| [Claude Sonnet 5](https://noometry.com/models/claude-sonnet-5) | #29 | 54.6 | $4 | — | [Compare](https://noometry.com/compare/claude-sonnet-5-vs-gemini-3-pro) |
| [GPT-5.6 Luna](https://noometry.com/models/gpt-5-6-luna) | #30 | 54.6 | $0.45 | 12 | [Compare](https://noometry.com/compare/gemini-3-pro-vs-gpt-5-6-luna) |
| [DeepSeek V4 Pro](https://noometry.com/models/deepseek-v4-pro) | #31 | 54.3 | $0.99 | 16 | [Compare](https://noometry.com/compare/deepseek-v4-pro-vs-gemini-3-pro) |
| [Gemini 3.5 Flash](https://noometry.com/models/gemini-3-5-flash) | #32 | 54.2 | $3.38 | — | [Compare](https://noometry.com/compare/gemini-3-5-flash-vs-gemini-3-pro) |

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 Pro Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified](https://noometry.com/benchmarks/swe-bench-verified) | 72.9% | #21 of 32, top 66% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-13 |
| [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only) | 74.2% | #5 of 39, top 13% |  | [SWE-bench](https://www.swebench.com/) | 2025-11-18 |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1440 | #58 of 113, top 52% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SWE-bench Multilingual](https://noometry.com/benchmarks/swe-bench-multilingual) | 68.7% | #5 of 13, top 39% |  | [SWE-bench](https://www.swebench.com/) | 2026-02-13 |
| [GSO](https://noometry.com/benchmarks/gso-bench) | 18.6% | #14 of 31, top 46% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 69.9% | #19 of 119, top 16% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1481 | #45 of 294, top 16% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 1,177 | #33 of 105, top 32% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [AlgoTune](https://noometry.com/benchmarks/algotune) | 1.83 | #4 of 18, top 23% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

Gemini 3 Pro Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 69.4% | #7 of 41, top 18% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 72.5% | #3 of 49, top 7% | prompt | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| [GDPval](https://noometry.com/benchmarks/gdpval) | 40.3% | #5 of 11, top 46% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Remote Labor Index](https://noometry.com/benchmarks/remote-labor-index) | 1.3% | #13 of 14, top 93% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [τ²-bench Airline](https://noometry.com/benchmarks/tau2-airline) | 80.5% | #6 of 7, top 86% | high | [τ²-bench](https://taubench.com/) | 2026-03-02 |
| [τ²-bench Banking](https://noometry.com/benchmarks/tau2-banking) | 18% | #20 of 26, top 77% | high | [τ²-bench](https://taubench.com/) | 2026-03-02 |
| [τ²-bench Retail](https://noometry.com/benchmarks/tau2-retail) | 75.9% | #5 of 7, top 72% | high | [τ²-bench](https://taubench.com/) | 2026-03-02 |
| [τ²-bench Telecom](https://noometry.com/benchmarks/tau2-telecom) | 91% | #4 of 7, top 58% | high | [τ²-bench](https://taubench.com/) | 2026-03-02 |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 46.3% | #14 of 24, top 59% | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BALROG](https://noometry.com/benchmarks/balrog) | 58.1% | #4 of 35, top 12% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Search](https://noometry.com/benchmarks/arena-search) | 1207 | #10 of 32, top 32% |  | [LMArena](https://lmarena.ai/leaderboard/search) | 2026-08-24 |
| [METR Time Horizons](https://noometry.com/benchmarks/metr-time-horizons) | 71% | #8 of 32, top 25% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 5,478 | #24 of 60, top 40% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Gemini 3 Pro Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 31.1% | #40 of 83, top 49% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 76.4% | #6 of 77, top 8% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 80.1% | #8 of 99, top 9% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 94.4% | #6 of 91, top 7% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 75% | #41 of 83, top 50% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 6.9% | #55 of 134, top 42% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 31% | #33 of 129, top 26% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-08 |
| [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) | 18.2% | #8 of 38, top 22% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1480 | #33 of 297, top 12% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 152.92 | #42 of 213, top 20% |  | [Epoch AI](https://epoch.ai/eci) | 2025-11-18 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 61.2 | #14 of 72, top 20% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

Gemini 3 Pro Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [MathArena Final-Answer Competitions](https://noometry.com/benchmarks/matharena) | 67% | #18 of 29, top 63% | preview | [MathArena](https://matharena.ai/) |  |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 91.4% | #47 of 173, top 28% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-19 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 20% | #49 of 77, top 64% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 55.5% | #13 of 57, top 23% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1476 | #31 of 285, top 11% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 37.6% | #12 of 68, top 18% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-21 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 18.8% | #11 of 55, top 20% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-21 |

### Knowledge

Gemini 3 Pro Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 92.6% | #22 of 186, top 12% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-19 |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 37.5% | #7 of 41, top 18% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 90.3% | Best of 58 |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 13.6% | #82 of 96, top 86% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 80.3% | Best of 57 |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1475 | #47 of 273, top 18% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

Gemini 3 Pro Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1305 | #13 of 122, top 11% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [GeoBench](https://noometry.com/benchmarks/geobench) | 84% | #3 of 25, top 12% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [VPCT](https://noometry.com/benchmarks/vpct) | 91% | Best of 24 |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Document](https://noometry.com/benchmarks/arena-document) | 1434 | #28 of 38, top 74% |  | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 |

### Multilingual

Gemini 3 Pro Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1474 | #16 of 297, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1523 | #23 of 285, top 9% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1492 | #18 of 223, top 9% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1515 | #2 of 231, top 1% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1510 | #4 of 211, top 2% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1448 | #18 of 213, top 9% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1493 | #12 of 283, top 5% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1470 | #25 of 226, top 12% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Gemini 3 Pro Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 87.7% | #13 of 57, top 23% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1458 | #36 of 298, top 13% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Gemini 3 Pro Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [CL-bench](https://noometry.com/benchmarks/cl-bench) | 15.8% | #15 of 19, top 79% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1471 | #33 of 291, top 12% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Gemini 3 Pro Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1479 | #16 of 297, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1482 | #10 of 295, top 4% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1525 | #52 of 115, top 46% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 85.9% | #5 of 57, top 9% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1484 | #15 of 295, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## Compare Gemini 3 Pro

-   [Gemini 3 Pro vs Gemini 2.5 Pro](https://noometry.com/compare/gemini-2-5-pro-vs-gemini-3-pro)
-   [Gemini 3 Pro vs Muse Spark 1.3](https://noometry.com/compare/gemini-3-pro-vs-muse-spark-1-3)
-   [Gemini 3 Pro vs Claude Sonnet 5](https://noometry.com/compare/claude-sonnet-5-vs-gemini-3-pro)
-   [Gemini 3 Pro vs GLM-5.3](https://noometry.com/compare/gemini-3-pro-vs-glm-5-3)
-   [Gemini 3 Pro vs GPT-5.6 Luna](https://noometry.com/compare/gemini-3-pro-vs-gpt-5-6-luna)
-   [Gemini 3 Pro vs Grok 4.5](https://noometry.com/compare/gemini-3-pro-vs-grok-4-5)
-   [Gemini 3 Pro vs DeepSeek V4 Pro](https://noometry.com/compare/deepseek-v4-pro-vs-gemini-3-pro)
-   [Gemini 3 Pro vs GPT-6 Astra](https://noometry.com/compare/gemini-3-pro-vs-gpt-6-astra)
-   [Gemini 3 Pro vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gemini-3-pro)
-   [Gemini 3 Pro vs Kimi K3](https://noometry.com/compare/gemini-3-pro-vs-kimi-k3)
-   [Gemini 3 Pro vs Grok 4.6](https://noometry.com/compare/gemini-3-pro-vs-grok-4-6)
-   [Gemini 3 Pro vs Qwen3.8 Max](https://noometry.com/compare/gemini-3-pro-vs-qwen3-8-max)
-   [Gemini 3 Pro vs MiMo-V2.6-Pro](https://noometry.com/compare/gemini-3-pro-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 3.1 Pro Preview](https://noometry.com/models/gemini-3-1-pro-preview)56.7
-   [Gemini 4 Argon](https://noometry.com/models/gemini-4-argon)56.5
-   [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 Pro?

Gemini 3 Pro by Google ranks 28th of 354 ranked models on the Noometry Index as of October 2026, with a score of 54.8. Its strongest category is multimodal, where it ranks 2nd.

### Is Gemini 3 Pro open source?

No. Gemini 3 Pro is proprietary and available only through Google's API and partner platforms.

### How fast is Gemini 3 Pro?

Gemini 3 Pro generated about 1 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are Gemini 3 Pro's strengths and weaknesses?

Relative to other ranked models, Gemini 3 Pro places best in multimodal, knowledge, multilingual and lowest in long context, math, agentic & tool use.

### What is Gemini 3 Pro best at?

Its best category is multimodal, where it ranks 2nd on Noometry.

### Cite this page

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

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