Compare · ModelsLive · 3 picked · head to head
GPT-5.4 Pro vs Gemini 3.1 Pro Preview vs GPT-5.4
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Gemini 3.1 Pro Preview wins on 8/17 benchmarks
Gemini 3.1 Pro Preview wins 8 of 17 shared benchmarks. Leads in reasoning · arena.
Category leads
reasoning·Gemini 3.1 Pro Previewknowledge·GPT-5.4 Promath·GPT-5.4 Prospeed·GPT-5.4agentic·GPT-5.4arena·Gemini 3.1 Pro Previewcoding·GPT-5.4
Hype vs Reality
Attention vs performance
GPT-5.4 Pro
#26 by perf·no signal
Gemini 3.1 Pro Preview
#38 by perf·no signal
GPT-5.4
#46 by perf·no signal
Best value
Gemini 3.1 Pro Preview
1.3x better value than GPT-5.4
GPT-5.4 Pro
0.6 pts/$
$105.00/M
Gemini 3.1 Pro Preview
8.7 pts/$
$7.00/M
GPT-5.4
6.7 pts/$
$8.75/M
Vendor risk
Who is behind the model
OpenAI
$840.0B·Tier 1
Google DeepMind
$4.00T·Tier 1
OpenAI
$840.0B·Tier 1
Head to head
17 benchmarks · 3 models
GPT-5.4 ProGemini 3.1 Pro PreviewGPT-5.4
ARC-AGI
Gemini 3.1 Pro Preview leads by +3.5
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
GPT-5.4 Pro
94.5
Gemini 3.1 Pro Preview
98.0
GPT-5.4
93.7
ARC-AGI-2
GPT-5.4 Pro leads by +6.2
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
GPT-5.4 Pro
83.3
Gemini 3.1 Pro Preview
77.1
GPT-5.4
74.0
Chess Puzzles
GPT-5.4 Pro leads by +3.6
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
GPT-5.4 Pro
58.6
Gemini 3.1 Pro Preview
55.0
GPT-5.4
44.0
FrontierMath-2025-02-28-Private
GPT-5.4 Pro leads by +2.4
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
GPT-5.4 Pro
50.0
Gemini 3.1 Pro Preview
36.9
GPT-5.4
47.6
FrontierMath-Tier-4-2025-07-01-Private
GPT-5.4 Pro leads by +10.4
FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning.
GPT-5.4 Pro
37.5
Gemini 3.1 Pro Preview
16.7
GPT-5.4
27.1
GPQA diamond
GPT-5.4 Pro leads by +0.7
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
GPT-5.4 Pro
92.8
Gemini 3.1 Pro Preview
92.1
GPT-5.4
91.1
SimpleQA Verified
Gemini 3.1 Pro Preview leads by +29.5
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
GPT-5.4 Pro
47.8
Gemini 3.1 Pro Preview
77.3
GPT-5.4
44.8
Artificial Analysis · Agentic Index
GPT-5.4 leads by +10.3
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?"
Gemini 3.1 Pro Preview
59.1
GPT-5.4
69.4
Artificial Analysis · Coding Index
GPT-5.4 leads by +1.8
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 3.1 Pro Preview
55.5
GPT-5.4
57.3
Artificial Analysis · Quality Index
Gemini 3.1 Pro Preview leads by +0.0
Gemini 3.1 Pro Preview
57.2
GPT-5.4
57.2
APEX-Agents
GPT-5.4 leads by +2.4
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
Gemini 3.1 Pro Preview
33.5
GPT-5.4
35.9
Chatbot Arena Elo · Overall
Gemini 3.1 Pro Preview leads by +26.8
Gemini 3.1 Pro Preview
1492.6
GPT-5.4
1465.8
OTIS Mock AIME 2024-2025
Gemini 3.1 Pro Preview leads by +0.3
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
Gemini 3.1 Pro Preview
95.6
GPT-5.4
95.3
PostTrainBench
Gemini 3.1 Pro Preview leads by +1.4
Gemini 3.1 Pro Preview
21.6
GPT-5.4
20.2
SimpleBench
Gemini 3.1 Pro Preview leads by +6.6
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
GPT-5.4 Pro
68.9
Gemini 3.1 Pro Preview
75.5
SWE-Bench verified
GPT-5.4 leads by +1.2
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 3.1 Pro Preview
75.6
GPT-5.4
76.9
WeirdML
Gemini 3.1 Pro Preview leads by +14.7
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
Gemini 3.1 Pro Preview
72.1
GPT-5.4
57.4
Full benchmark table
| Benchmark | GPT-5.4 Pro | Gemini 3.1 Pro Preview | GPT-5.4 |
|---|---|---|---|
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 94.5 | 98.0 | 93.7 |
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. | 83.3 | 77.1 | 74.0 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 58.6 | 55.0 | 44.0 |
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. | 50.0 | 36.9 | 47.6 |
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. | 37.5 | 16.7 | 27.1 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 92.8 | 92.1 | 91.1 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 47.8 | 77.3 | 44.8 |
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?" | — | 59.1 | 69.4 |
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. | — | 55.5 | 57.3 |
Artificial Analysis · Quality Index | — | 57.2 | 57.2 |
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | — | 33.5 | 35.9 |
Chatbot Arena Elo · Overall | — | 1492.6 | 1465.8 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | — | 95.6 | 95.3 |
PostTrainBench | — | 21.6 | 20.2 |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 68.9 | 75.5 | — |
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. | — | 75.6 | 76.9 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | — | 72.1 | 57.4 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $30.00 | $180.00 | 1.1M tokens (~525 books) | $675.00 | |
| $2.00 | $12.00 | 1.0M tokens (~524 books) | $45.00 | |
| $2.50 | $15.00 | 1.1M tokens (~525 books) | $56.25 |