Compare · ModelsLive · 2 picked · head to head
Gemini 3.5 Flash vs Gemini 3 Pro
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Gemini 3.5 Flash wins on 12/17 benchmarks
Gemini 3.5 Flash wins 12 of 17 shared benchmarks. Leads in speed · agentic · reasoning.
Category leads
speed·Gemini 3.5 Flashagentic·Gemini 3.5 Flashreasoning·Gemini 3.5 Flasharena·Gemini 3.5 Flashknowledge·Gemini 3.5 Flashmath·Gemini 3.5 Flashcoding·Gemini 3.5 Flash
Hype vs Reality
Attention vs performance
Gemini 3.5 Flash
#44 by perf·no signal
Gemini 3 Pro
#52 by perf·no signal
Vendor risk
Who is behind the model
Google DeepMind
$4.00T·Tier 1
Google DeepMind
$4.00T·Tier 1
Head to head
17 benchmarks · 2 models
Gemini 3.5 FlashGemini 3 Pro
Artificial Analysis · Agentic Index
Gemini 3 Pro leads by +7.6
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?"
Gemini 3.5 Flash
37.5
Gemini 3 Pro
45.0
Artificial Analysis · Coding Index
Gemini 3.5 Flash leads by +30.8
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.
Gemini 3.5 Flash
70.1
Gemini 3 Pro
39.4
Artificial Analysis · Quality Index
Gemini 3.5 Flash leads by +8.9
Gemini 3.5 Flash
50.2
Gemini 3 Pro
41.3
APEX-Agents
Gemini 3.5 Flash leads by +31.2
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
Gemini 3.5 Flash
49.6
Gemini 3 Pro
18.4
ARC-AGI
Gemini 3.5 Flash leads by +17.5
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
Gemini 3.5 Flash
92.5
Gemini 3 Pro
75.0
ARC-AGI-2
Gemini 3.5 Flash leads by +41.0
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
Gemini 3.5 Flash
72.1
Gemini 3 Pro
31.1
Chatbot Arena Elo · Coding
Gemini 3.5 Flash leads by +66.8
Gemini 3.5 Flash
1505.5
Gemini 3 Pro
1438.8
Chatbot Arena Elo · Overall
Gemini 3 Pro leads by +9.5
Gemini 3.5 Flash
1476.3
Gemini 3 Pro
1485.7
Chess Puzzles
Gemini 3.5 Flash leads by +19.0
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
Gemini 3.5 Flash
50.0
Gemini 3 Pro
31.0
FrontierMath-2025-02-28-Private
Gemini 3.5 Flash leads by +1.4
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
Gemini 3.5 Flash
39.0
Gemini 3 Pro
37.6
FrontierMath-Tier-4-2025-07-01-Private
Gemini 3 Pro leads by +4.2
FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning.
Gemini 3.5 Flash
14.6
Gemini 3 Pro
18.8
GPQA diamond
Gemini 3.5 Flash leads by +0.3
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
Gemini 3.5 Flash
90.4
Gemini 3 Pro
90.2
OTIS Mock AIME 2024-2025
Gemini 3.5 Flash leads by +4.2
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
Gemini 3.5 Flash
95.5
Gemini 3 Pro
91.4
SimpleBench
Gemini 3.5 Flash leads by +0.4
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
Gemini 3.5 Flash
72.0
Gemini 3 Pro
71.7
SimpleQA Verified
Gemini 3 Pro leads by +4.5
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
Gemini 3.5 Flash
68.4
Gemini 3 Pro
72.9
SWE-Bench verified
Gemini 3.5 Flash leads by +6.4
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.
Gemini 3.5 Flash
79.3
Gemini 3 Pro
72.9
WeirdML
Gemini 3 Pro leads by +7.3
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
Gemini 3.5 Flash
62.6
Gemini 3 Pro
69.9
Full benchmark table
| Benchmark | Gemini 3.5 Flash | Gemini 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?" | 37.5 | 45.0 |
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. | 70.1 | 39.4 |
Artificial Analysis · Quality Index | 50.2 | 41.3 |
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 49.6 | 18.4 |
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 92.5 | 75.0 |
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. | 72.1 | 31.1 |
Chatbot Arena Elo · Coding | 1505.5 | 1438.8 |
Chatbot Arena Elo · Overall | 1476.3 | 1485.7 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 50.0 | 31.0 |
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. | 39.0 | 37.6 |
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. | 14.6 | 18.8 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 90.4 | 90.2 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 95.5 | 91.4 |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 72.0 | 71.7 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 68.4 | 72.9 |
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. | 79.3 | 72.9 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 62.6 | 69.9 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $1.50 | $9.00 | 1.0M tokens (~524 books) | $33.75 | |
| — | — | — | — |
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