Compare · ModelsLive · 2 picked · head to head

Claude Sonnet 4.6 vs Gemini 3 Pro

Side by side · benchmarks, pricing, and signals you can act on.

Winner summary

Gemini 3 Pro wins 11 of 20 shared benchmarks. Leads in knowledge · math · coding.

Category leads
speed·Claude Sonnet 4.6agentic·Claude Sonnet 4.6reasoning·Claude Sonnet 4.6arena·Claude Sonnet 4.6knowledge·Gemini 3 Promath·Gemini 3 Progeneral·Claude Sonnet 4.6coding·Gemini 3 Pro
Hype vs Reality
Claude Sonnet 4.6
#149 by perf·#10 by attention
DESERVED
Gemini 3 Pro
#71 by perf·#5 by attention
DESERVED
Best value
Claude Sonnet 4.6
5.1 pts/$
$9.00/M
Gemini 3 Pro
n/a
no price
Vendor risk
Anthropic logo
Anthropic
$965.0B·Tier 1
Medium risk
Google DeepMind logo
Google DeepMind
$4.20T·Tier 1
Low risk
Head to head
Claude Sonnet 4.6Gemini 3 Pro
Artificial Analysis · Agentic Index
Gemini 3 Pro leads by +4.3
Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?"
Claude Sonnet 4.6
40.8
Gemini 3 Pro
45.0
Artificial Analysis · Coding Index
Claude Sonnet 4.6 leads by +23.7
Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads.
Claude Sonnet 4.6
63.0
Gemini 3 Pro
39.4
Artificial Analysis · Quality Index
Claude Sonnet 4.6 leads by +5.9
Claude Sonnet 4.6
47.2
Gemini 3 Pro
41.3
APEX-Agents
Claude Sonnet 4.6 leads by +24.6
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
Claude Sonnet 4.6
43.0
Gemini 3 Pro
18.4
ARC-AGI
Claude Sonnet 4.6 leads by +11.5
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
Claude Sonnet 4.6
86.5
Gemini 3 Pro
75.0
ARC-AGI-2
Claude Sonnet 4.6 leads by +29.3
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
Claude Sonnet 4.6
60.4
Gemini 3 Pro
31.1
Chatbot Arena Elo · Coding
Claude Sonnet 4.6 leads by +82.4
Claude Sonnet 4.6
1521.4
Gemini 3 Pro
1439.0
Chatbot Arena Elo · Overall
Gemini 3 Pro leads by +13.3
Claude Sonnet 4.6
1472.2
Gemini 3 Pro
1485.5
Chess Puzzles
Gemini 3 Pro leads by +18.9
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
Claude Sonnet 4.6
8.5
Gemini 3 Pro
27.4
DeepResearch Bench
Claude Sonnet 4.6 leads by +8.6
DeepResearch Bench · evaluates AI on complex multi-step research tasks requiring information gathering, synthesis, and producing comprehensive analyses.
Claude Sonnet 4.6
54.9
Gemini 3 Pro
46.3
FrontierMath-2025-02-28-Private
Gemini 3 Pro leads by +9.1
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
Claude Sonnet 4.6
56.8
Gemini 3 Pro
66.0
FrontierMath-Tier-4-2025-07-01-Private
Gemini 3 Pro leads by +17.4
FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning.
Claude Sonnet 4.6
13.8
Gemini 3 Pro
31.3
GPQA diamond
Gemini 3 Pro leads by +7.0
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
Claude Sonnet 4.6
83.2
Gemini 3 Pro
90.2
OTIS Mock AIME 2024-2025
Gemini 3 Pro leads by +5.6
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
Claude Sonnet 4.6
85.8
Gemini 3 Pro
91.4
PostTrainBench
Gemini 3 Pro leads by +1.7
Claude Sonnet 4.6
16.4
Gemini 3 Pro
18.1
Proofbench
Claude Sonnet 4.6 leads by +25.0
Claude Sonnet 4.6
45.0
Gemini 3 Pro
20.0
SimpleQA Verified
Gemini 3 Pro leads by +37.4
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
Claude Sonnet 4.6
35.5
Gemini 3 Pro
72.9
SWE-Bench verified
Claude Sonnet 4.6 leads by +2.3
SWE-bench Verified · 500 human-validated tasks from 12 real Python repositories (Django, Flask, scikit-learn, sympy, and others). Each task requires the model to produce a git patch that resolves a real GitHub issue and passes the test suite. The verified subset eliminates ambiguous tasks from the original SWE-bench. Claude Mythos Preview leads at 93.9%, crossing 90% for the first time in 2026. Opus 4.6 scores 80.8%. The benchmark remains the most-cited evaluation for code-generation capability.
Claude Sonnet 4.6
75.2
Gemini 3 Pro
72.9
Terminal Bench
Gemini 3 Pro leads by +16.0
Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence.
Claude Sonnet 4.6
53.4
Gemini 3 Pro
69.4
WeirdML
Gemini 3 Pro leads by +3.9
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
Claude Sonnet 4.6
66.1
Gemini 3 Pro
69.9
Full benchmark table
BenchmarkClaude Sonnet 4.6Gemini 3 Pro
Artificial Analysis · Agentic Index
Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?"
40.845.0
Artificial Analysis · Coding Index
Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads.
63.039.4
Artificial Analysis · Quality Index
47.241.3
APEX-Agents
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
43.018.4
ARC-AGI
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
86.575.0
ARC-AGI-2
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
60.431.1
Chatbot Arena Elo · Coding
1521.41439.0
Chatbot Arena Elo · Overall
1472.21485.5
Chess Puzzles
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
8.527.4
DeepResearch Bench
DeepResearch Bench · evaluates AI on complex multi-step research tasks requiring information gathering, synthesis, and producing comprehensive analyses.
54.946.3
FrontierMath-2025-02-28-Private
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
56.866.0
FrontierMath-Tier-4-2025-07-01-Private
FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning.
13.831.3
GPQA diamond
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
83.290.2
OTIS Mock AIME 2024-2025
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
85.891.4
PostTrainBench
16.418.1
Proofbench
45.020.0
SimpleQA Verified
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
35.572.9
SWE-Bench verified
SWE-bench Verified · 500 human-validated tasks from 12 real Python repositories (Django, Flask, scikit-learn, sympy, and others). Each task requires the model to produce a git patch that resolves a real GitHub issue and passes the test suite. The verified subset eliminates ambiguous tasks from the original SWE-bench. Claude Mythos Preview leads at 93.9%, crossing 90% for the first time in 2026. Opus 4.6 scores 80.8%. The benchmark remains the most-cited evaluation for code-generation capability.
75.272.9
Terminal Bench
Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence.
53.469.4
WeirdML
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
66.169.9
Pricing · per 1M tokens · projected $/mo at 10M tokens
ModelInputOutputContextProjected $/mo
Anthropic logoClaude Sonnet 4.6$3.00$15.001.0M tokens (~500 books)$60.00
Google DeepMind logoGemini 3 Pro————