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Gemini 3 Pro

GoogleCurrentReleased Nov 18, 2025
Overall Score
81Prov. #18 of 115
Arena Elo
1486
Categories Ranked
8of 8
Price (1M tokens)
$2 in / $12 out
Speed
109tok/s
Context
2M
ProprietaryNon-Reasoning
Confidence
base

According to BenchLM.ai, Gemini 3 Pro ranks #18 out of 115 models on the provisional leaderboard with an overall score of 81/100. It does not yet have enough sourced coverage for BenchLM's verified leaderboard. This places it in the mid-tier of AI models, with strengths in specific benchmark categories.

Gemini 3 Pro is a proprietary model with a 2M token context window. It processes queries without explicit chain-of-thought reasoning, offering faster response times and lower token usage.

Gemini 3 Pro sits inside the Gemini 3 Pro family alongside Gemini 3 Pro Deep Think. This profile currently has 8 of 193 tracked benchmarks. BenchLM only exposes non-generated benchmark rows publicly, so missing categories stay blank until a sourced evaluation is available.

Its strongest category is Knowledge (#12), while its weakest is Instruction Following (#34). This performance profile makes it particularly effective for knowledge-intensive tasks like research, analysis, and factual Q&A.

Ranking Distribution

Category rank across 8 benchmark categories — sorted by best rank

Category Performance

Scores across all benchmark categories (0-100 scale)

Category Breakdown

Agentic

#18
73.1/ 100
Weight: 22%0 benchmarks
Terminal-Bench 2.0BrowseCompOSWorld-VerifiedGAIATAU-benchWebArena

Coding

#32
74.1/ 100
Weight: 20%1 benchmark
SWE-bench VerifiedLiveCodeBenchSWE-bench ProSWE-RebenchSciCode

Reasoning

#14
82.3/ 100
Weight: 17%1 benchmark
MuSRLongBench v2MRCRv2ARC-AGI-2

Knowledge

#12
83.5/ 100
Weight: 12%0 benchmarks
GPQASuperGPQAMMLU-ProHLEFrontierScienceSimpleQA

Math

#22
81.2/ 100
Weight: 5%0 benchmarks
AIME 2025BRUMO 2025MATH-500FrontierMath

Multilingual

#20
81.7/ 100
Weight: 7%0 benchmarks
MGSMMMLU-ProX

Multimodal

#16
79.2/ 100
Weight: 12%6 benchmarks
MMMU-ProOfficeQA ProCharXivCharXiv w/o tools

Inst. Following

#34
80.3/ 100
Weight: 5%0 benchmarks
IFEvalIFBench

Chatbot Arena Performance

Text Overall1486CI: ±3.941,369 votes
Coding1519CI: ±7.28,582 votes
Math1479CI: ±11.52,654 votes
Instruction Following1474CI: ±6.511,199 votes
Creative Writing1485CI: ±8.46,308 votes
Multi-turn1495CI: ±8.06,769 votes
Hard Prompts1504CI: ±5.022,480 votes
Hard Prompts (English)1502CI: ±6.610,723 votes
Longer Query1492CI: ±6.710,516 votes

Benchmark Details

Only benchmark rows with an attached exact-source record are shown here. Source-unverified manual rows and generated rows are hidden from model pages.

Gemini 3 Pro Family

Base entry

Frequently Asked Questions

How does Gemini 3 Pro perform overall in AI benchmarks?

Gemini 3 Pro currently ranks #18 out of 115 models on BenchLM's provisional leaderboard with an overall score of 81. It is created by Google and features a 2M context window.

Is Gemini 3 Pro good for coding and programming?

Gemini 3 Pro ranks #32 out of 115 models in coding and programming benchmarks with an average score of 74.1. There are stronger options in this category.

Is Gemini 3 Pro good for reasoning and logic?

Gemini 3 Pro ranks #14 out of 115 models in reasoning and logic benchmarks with an average score of 82.3. There are stronger options in this category.

Is Gemini 3 Pro good for multimodal and grounded tasks?

Gemini 3 Pro ranks #16 out of 115 models in multimodal and grounded tasks benchmarks with an average score of 79.2. There are stronger options in this category.

Which sibling models are related to Gemini 3 Pro?

Gemini 3 Pro belongs to the Gemini 3 Pro family. Related variants on BenchLM include Gemini 3 Pro Deep Think.

Does Gemini 3 Pro have full benchmark coverage on BenchLM?

Not yet. Gemini 3 Pro currently has 8 published benchmark scores out of the 193 benchmarks BenchLM tracks. BenchLM only exposes non-generated public benchmark rows, so missing categories stay blank until a sourced evaluation is available.

What is the context window size of Gemini 3 Pro?

Gemini 3 Pro has a context window of 2M, which determines how much text it can process in a single interaction.

Last updated: May 13, 2026 · Runtime metrics stay blank until BenchLM has a sourced snapshot.

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