Compare · ModelsLive · 2 picked · head to head
Claude Opus 4.6 vs Gemini 3 Flash Preview
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Claude Opus 4.6 wins on 14/16 benchmarks
Claude Opus 4.6 wins 14 of 16 shared benchmarks. Leads in agentic · reasoning · arena.
Category leads
agentic·Claude Opus 4.6reasoning·Claude Opus 4.6arena·Claude Opus 4.6knowledge·Gemini 3 Flash Previewmath·Claude Opus 4.6coding·Claude Opus 4.6
Hype vs Reality
Attention vs performance
Claude Opus 4.6
#56 by perf·#4 by attention
Gemini 3 Flash Preview
#98 by perf·no signal
Best value
Gemini 3 Flash Preview
7.3x better value than Claude Opus 4.6
Claude Opus 4.6
3.8 pts/$
$15.00/M
Gemini 3 Flash Preview
28.1 pts/$
$1.75/M
Vendor risk
Who is behind the model
Anthropic
$380.0B·Tier 1
Google DeepMind
$4.00T·Tier 1
Head to head
16 benchmarks · 2 models
Claude Opus 4.6Gemini 3 Flash Preview
APEX-Agents
Claude Opus 4.6 leads by +7.7
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
Claude Opus 4.6
31.7
Gemini 3 Flash Preview
24.0
ARC-AGI
Claude Opus 4.6 leads by +72.5
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
Claude Opus 4.6
94.0
Gemini 3 Flash Preview
21.5
ARC-AGI-2
Claude Opus 4.6 leads by +35.6
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
Claude Opus 4.6
69.2
Gemini 3 Flash Preview
33.6
Chatbot Arena Elo · Coding
Claude Opus 4.6 leads by +106.4
Claude Opus 4.6
1542.9
Gemini 3 Flash Preview
1436.4
Chatbot Arena Elo · Overall
Claude Opus 4.6 leads by +22.7
Claude Opus 4.6
1496.6
Gemini 3 Flash Preview
1473.9
Chess Puzzles
Gemini 3 Flash Preview leads by +21.0
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
Claude Opus 4.6
17.0
Gemini 3 Flash Preview
38.0
FrontierMath-2025-02-28-Private
Claude Opus 4.6 leads by +5.1
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
Claude Opus 4.6
40.7
Gemini 3 Flash Preview
35.6
FrontierMath-Tier-4-2025-07-01-Private
Claude Opus 4.6 leads by +18.7
FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning.
Claude Opus 4.6
22.9
Gemini 3 Flash Preview
4.2
GPQA diamond
Claude Opus 4.6 leads by +9.8
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
Claude Opus 4.6
87.4
Gemini 3 Flash Preview
77.6
GSO-Bench
Claude Opus 4.6 leads by +23.5
GSO-Bench · evaluates AI models on real-world open-source software engineering tasks, testing the ability to understand and resolve actual GitHub issues.
Claude Opus 4.6
33.3
Gemini 3 Flash Preview
9.8
OTIS Mock AIME 2024-2025
Claude Opus 4.6 leads by +1.7
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
Claude Opus 4.6
94.4
Gemini 3 Flash Preview
92.8
SimpleBench
Claude Opus 4.6 leads by +7.8
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
Claude Opus 4.6
61.1
Gemini 3 Flash Preview
53.3
SimpleQA Verified
Gemini 3 Flash Preview leads by +20.9
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
Claude Opus 4.6
46.5
Gemini 3 Flash Preview
67.4
SWE-Bench verified
Claude Opus 4.6 leads by +3.3
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 Opus 4.6
78.7
Gemini 3 Flash Preview
75.4
Terminal Bench
Claude Opus 4.6 leads by +10.4
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 Opus 4.6
74.7
Gemini 3 Flash Preview
64.3
WeirdML
Claude Opus 4.6 leads by +16.3
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
Claude Opus 4.6
77.9
Gemini 3 Flash Preview
61.6
Full benchmark table
| Benchmark | Claude Opus 4.6 | Gemini 3 Flash Preview |
|---|---|---|
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 31.7 | 24.0 |
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 94.0 | 21.5 |
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. | 69.2 | 33.6 |
Chatbot Arena Elo · Coding | 1542.9 | 1436.4 |
Chatbot Arena Elo · Overall | 1496.6 | 1473.9 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 17.0 | 38.0 |
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. | 40.7 | 35.6 |
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. | 22.9 | 4.2 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 87.4 | 77.6 |
GSO-Bench GSO-Bench · evaluates AI models on real-world open-source software engineering tasks, testing the ability to understand and resolve actual GitHub issues. | 33.3 | 9.8 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 94.4 | 92.8 |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 61.1 | 53.3 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 46.5 | 67.4 |
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. | 78.7 | 75.4 |
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. | 74.7 | 64.3 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 77.9 | 61.6 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $5.00 | $25.00 | 1.0M tokens (~500 books) | $100.00 | |
| $0.50 | $3.00 | 1.0M tokens (~524 books) | $11.25 |