Compare · ModelsLive · 3 picked · head to head
Claude Mythos Preview vs Claude Opus 4.6 vs GPT-5.4
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GPT-5.4 wins on 6/15 benchmarks
GPT-5.4 wins 6 of 15 shared benchmarks. Leads in agentic · math.
Category leads
knowledge·Claude Mythos Previewcoding·Claude Mythos Previewagentic·GPT-5.4reasoning·Claude Opus 4.6arena·Claude Opus 4.6math·GPT-5.4
Hype vs Reality
Attention vs performance
Claude Mythos Preview
#4 by perf·#2 by attention
Claude Opus 4.6
#56 by perf·#4 by attention
GPT-5.4
#46 by perf·no signal
Best value
GPT-5.4
1.8x better value than Claude Opus 4.6
Claude Mythos Preview
—
no price
Claude Opus 4.6
3.8 pts/$
$15.00/M
GPT-5.4
6.7 pts/$
$8.75/M
Vendor risk
Who is behind the model
Anthropic
$380.0B·Tier 1
Anthropic
$380.0B·Tier 1
OpenAI
$840.0B·Tier 1
Head to head
15 benchmarks · 3 models
Claude Mythos PreviewClaude Opus 4.6GPT-5.4
GPQA diamond
Claude Mythos Preview leads by +3.4
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
Claude Mythos Preview
94.5
Claude Opus 4.6
87.4
GPT-5.4
91.1
SWE-Bench verified
Claude Mythos Preview leads by +15.2
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.
Claude Mythos Preview
93.9
Claude Opus 4.6
78.7
GPT-5.4
76.9
APEX-Agents
GPT-5.4 leads by +4.2
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
GPT-5.4
35.9
ARC-AGI
Claude Opus 4.6 leads by +0.3
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
GPT-5.4
93.7
ARC-AGI-2
GPT-5.4 leads by +4.8
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
GPT-5.4
74.0
Chatbot Arena Elo · Overall
Claude Opus 4.6 leads by +30.8
Claude Opus 4.6
1496.6
GPT-5.4
1465.8
Chess Puzzles
GPT-5.4 leads by +27.0
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
GPT-5.4
44.0
FrontierMath-2025-02-28-Private
GPT-5.4 leads by +6.9
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
GPT-5.4
47.6
FrontierMath-Tier-4-2025-07-01-Private
GPT-5.4 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.
Claude Opus 4.6
22.9
GPT-5.4
27.1
HLE
Claude Mythos Preview leads by +25.7
HLE (Humanity's Last Exam) · a reasoning benchmark designed to be the hardest public evaluation of AI. Questions span mathematics, physics, philosophy, and logic · curated to be at or beyond the frontier of human expert capability. Tested with and without tool augmentation. Claude Opus 4.7 scores 46.9% without tools and 54.7% with tools · making it one of the few benchmarks where the top score is below 60%.
Claude Mythos Preview
56.8
Claude Opus 4.6
31.1
OTIS Mock AIME 2024-2025
GPT-5.4 leads by +0.9
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
Claude Opus 4.6
94.4
GPT-5.4
95.3
PostTrainBench
Claude Opus 4.6 leads by +2.9
Claude Opus 4.6
23.2
GPT-5.4
20.2
SimpleQA Verified
Claude Opus 4.6 leads by +1.7
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
GPT-5.4
44.8
Terminal Bench
Claude Mythos Preview leads by +7.3
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 Mythos Preview
82.0
Claude Opus 4.6
74.7
WeirdML
Claude Opus 4.6 leads by +20.5
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
Claude Opus 4.6
77.9
GPT-5.4
57.4
Full benchmark table
| Benchmark | Claude Mythos Preview | Claude Opus 4.6 | GPT-5.4 |
|---|---|---|---|
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 94.5 | 87.4 | 91.1 |
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. | 93.9 | 78.7 | 76.9 |
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 | 35.9 |
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | — | 94.0 | 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. | — | 69.2 | 74.0 |
Chatbot Arena Elo · Overall | — | 1496.6 | 1465.8 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | — | 17.0 | 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. | — | 40.7 | 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. | — | 22.9 | 27.1 |
HLE HLE (Humanity's Last Exam) · a reasoning benchmark designed to be the hardest public evaluation of AI. Questions span mathematics, physics, philosophy, and logic · curated to be at or beyond the frontier of human expert capability. Tested with and without tool augmentation. Claude Opus 4.7 scores 46.9% without tools and 54.7% with tools · making it one of the few benchmarks where the top score is below 60%. | 56.8 | 31.1 | — |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | — | 94.4 | 95.3 |
PostTrainBench | — | 23.2 | 20.2 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | — | 46.5 | 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. | 82.0 | 74.7 | — |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | — | 77.9 | 57.4 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| — | — | 1.0M tokens (~500 books) | — | |
| $5.00 | $25.00 | 1.0M tokens (~500 books) | $100.00 | |
| $2.50 | $15.00 | 1.1M tokens (~525 books) | $56.25 |