Compare · ModelsLive · 3 picked · head to head
Claude Mythos Preview vs Claude Sonnet 4.6 vs GPT-5.5
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Claude Mythos Preview wins on 3/5 benchmarks
Claude Mythos Preview wins 3 of 5 shared benchmarks. Leads in knowledge · agentic.
Category leads
knowledge·Claude Mythos Previewagentic·Claude Mythos Previewcoding·GPT-5.5reasoning·GPT-5.5
Hype vs Reality
Attention vs performance
Claude Mythos Preview
#5 by perf·no signal
Claude Sonnet 4.6
#149 by perf·#10 by attention
GPT-5.5
#2 by perf·#4 by attention
Best value
Claude Sonnet 4.6
1.1x better value than GPT-5.5
Claude Mythos Preview
n/a
no price
Claude Sonnet 4.6
5.1 pts/$
$9.00/M
GPT-5.5
4.9 pts/$
$17.50/M
Vendor risk
Who is behind the model
Anthropic
$965.0B·Tier 1
Anthropic
$965.0B·Tier 1
OpenAI
$840.0B·Tier 1
Head to head
5 benchmarks · 3 models
Claude Mythos PreviewClaude Sonnet 4.6GPT-5.5
GPQA diamond
Claude Mythos Preview leads by +0.9
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 Sonnet 4.6
83.2
GPT-5.5
93.6
OSWorld
Claude Mythos Preview leads by +0.9
OSWorld · tests AI agents on real-world computer tasks across operating systems, including web browsing, file management, and application use.
Claude Mythos Preview
79.6
Claude Sonnet 4.6
72.1
GPT-5.5
78.7
Terminal Bench
GPT-5.5 leads by +0.7
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 Sonnet 4.6
53.4
GPT-5.5
82.7
ARC-AGI
GPT-5.5 leads by +8.5
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
Claude Sonnet 4.6
86.5
GPT-5.5
95.0
SWE-Bench verified
Claude Mythos Preview leads by +18.7
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 Sonnet 4.6
75.2
Full benchmark table
| Benchmark | Claude Mythos Preview | Claude Sonnet 4.6 | GPT-5.5 |
|---|---|---|---|
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 | 83.2 | 93.6 |
OSWorld OSWorld · tests AI agents on real-world computer tasks across operating systems, including web browsing, file management, and application use. | 79.6 | 72.1 | 78.7 |
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 | 53.4 | 82.7 |
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | — | 86.5 | 95.0 |
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 | 75.2 | — |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| — | — | 1.0M tokens (~500 books) | — | |
| $3.00 | $15.00 | 1.0M tokens (~500 books) | $60.00 | |
| $5.00 | $30.00 | 400K tokens (~200 books) | $112.50 |