Compare · ModelsLive · 3 picked · head to head
Gemini 3.1 Pro Preview vs GPT-5.4 vs GPT-5.3-Codex
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Gemini 3.1 Pro Preview wins on 10/17 benchmarks
Gemini 3.1 Pro Preview wins 10 of 17 shared benchmarks. Leads in knowledge · reasoning · arena.
Category leads
speed·GPT-5.4agentic·GPT-5.4knowledge·Gemini 3.1 Pro Previewcoding·GPT-5.4reasoning·Gemini 3.1 Pro Previewarena·Gemini 3.1 Pro Previewmath·GPT-5.4
Hype vs Reality
Attention vs performance
Gemini 3.1 Pro Preview
#38 by perf·no signal
GPT-5.4
#46 by perf·no signal
GPT-5.3-Codex
#86 by perf·no signal
Best value
Gemini 3.1 Pro Preview
1.3x better value than GPT-5.4
Gemini 3.1 Pro Preview
8.7 pts/$
$7.00/M
GPT-5.4
6.7 pts/$
$8.75/M
GPT-5.3-Codex
6.6 pts/$
$7.88/M
Vendor risk
Who is behind the model
Google DeepMind
$4.00T·Tier 1
OpenAI
$840.0B·Tier 1
OpenAI
$840.0B·Tier 1
Head to head
17 benchmarks · 3 models
Gemini 3.1 Pro PreviewGPT-5.4GPT-5.3-Codex
Artificial Analysis · Agentic Index
GPT-5.4 leads by +7.2
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
GPT-5.3-Codex
62.2
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
GPT-5.3-Codex
53.1
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
GPT-5.3-Codex
54.0
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
GPT-5.3-Codex
31.7
PostTrainBench
Gemini 3.1 Pro Preview leads by +1.4
Gemini 3.1 Pro Preview
21.6
GPT-5.4
20.2
GPT-5.3-Codex
17.8
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
GPT-5.3-Codex
74.8
WeirdML
GPT-5.3-Codex leads by +7.2
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
GPT-5.3-Codex
79.3
ARC-AGI
Gemini 3.1 Pro Preview leads by +4.3
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
Gemini 3.1 Pro Preview
98.0
GPT-5.4
93.7
ARC-AGI-2
Gemini 3.1 Pro Preview leads by +3.1
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
Gemini 3.1 Pro Preview
77.1
GPT-5.4
74.0
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
Chess Puzzles
Gemini 3.1 Pro Preview leads by +11.0
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
Gemini 3.1 Pro Preview
55.0
GPT-5.4
44.0
FrontierMath-2025-02-28-Private
GPT-5.4 leads by +10.7
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
Gemini 3.1 Pro Preview
36.9
GPT-5.4
47.6
FrontierMath-Tier-4-2025-07-01-Private
GPT-5.4 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.
Gemini 3.1 Pro Preview
16.7
GPT-5.4
27.1
GPQA diamond
Gemini 3.1 Pro Preview leads by +1.1
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
Gemini 3.1 Pro Preview
92.1
GPT-5.4
91.1
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
SimpleQA Verified
Gemini 3.1 Pro Preview leads by +32.5
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
Gemini 3.1 Pro Preview
77.3
GPT-5.4
44.8
Terminal Bench
Gemini 3.1 Pro Preview leads by +1.1
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.
Gemini 3.1 Pro Preview
78.4
GPT-5.3-Codex
77.3
Full benchmark table
| Benchmark | Gemini 3.1 Pro Preview | GPT-5.4 | GPT-5.3-Codex |
|---|---|---|---|
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 | 62.2 |
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 | 53.1 |
Artificial Analysis · Quality Index | 57.2 | 57.2 | 54.0 |
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 | 31.7 |
PostTrainBench | 21.6 | 20.2 | 17.8 |
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 | 74.8 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 72.1 | 57.4 | 79.3 |
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 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. | 77.1 | 74.0 | — |
Chatbot Arena Elo · Overall | 1492.6 | 1465.8 | — |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 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. | 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. | 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. | 92.1 | 91.1 | — |
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 | — |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 77.3 | 44.8 | — |
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. | 78.4 | — | 77.3 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $2.00 | $12.00 | 1.0M tokens (~524 books) | $45.00 | |
| $2.50 | $15.00 | 1.1M tokens (~525 books) | $56.25 | |
| $1.75 | $14.00 | 400K tokens (~200 books) | $48.13 |