Compare AI models
| Specifications | ||
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 1M | 1M |
| Benchmarks | ||
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | 77.8% | 43.3% |
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 93.9% | 76.2% |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | 82% | 54.2% |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 56.8% | 37.5% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 64.7% | 45.8% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 94.5% | 91.9% |
CharXiv ReasoningChart reasoning — Can the AI read and reason about complex charts and figures, not just text? Higher is better. | 86.1% | 81.4% |
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | — | 48% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | 87.3% | — |
SWE-Bench MultimodalMultimodal coding — Real bug reports that arrive with pictures attached — a screenshot, a mockup, a page rendering wrongly — so the AI has to read the image as well as the code to work out what to fix. Higher is better. | 59% | — |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | — | 50% |
LiveCodeBench ProCompetitive programming — Elo rating on competitive programming problems in LiveCodeBench Pro | — | 2439 |
APEX-AgentsExpert agentic work — expert-level agentic work tasks (AI Productivity Index) | — | 18.4% |
MCP Atlas · original harnessMulti-step tool use — pass rate under the original tool-use harness, before Scale changed the judge and retry handling in April 2026 | — | 54.1% |
τ²-bench · retailAgentic tasks — task success in the retail domain of the second-generation customer-service evaluation | — | 85.3% |
τ²-bench · telecomAgentic tasks — task success in the telecom domain of the second-generation customer-service evaluation | — | 98% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 59.2% |
CyberGymCybersecurity — Tests the AI on cybersecurity challenges — finding and exploiting software weaknesses inside a safe sandbox. Higher is better. | 83.1% | — |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | — | 31.1% |
AIME 2025Reasoning — single-answer accuracy on the 2025 invitational mathematics exam without tools | — | 95% |
SimpleQA VerifiedKnowledge — accuracy on the vetted factual questions in SimpleQA Verified | — | 72.1% |
MMMLUKnowledge — average accuracy on multilingual translations of MMLU | — | 91.8% |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 79.6% | — |
CharXiv Reasoning · with toolsChart reasoning — The same chart-and-figure reasoning test, run with the AI allowed to use tools — writing code to inspect the image, for instance — rather than reading the chart unaided. Scores run higher than the unaided version, so read the two as separate tests. Higher is better. | 93.2% | — |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | — | 81% |
GraphWalks BFS (256K–1M)Long context — breadth-first search F1 on the 200 GraphWalks problems with 256K to 1M tokens of context | 80% | — |
MRCR v2 (8-needle) · 128k averageLong context — Tests whether the AI can find specific details buried inside a very long document (around 128k tokens — roughly a long book). Higher is better. | — | 77% |
MRCR v2 (8-needle) · 1M pointwiseLong context — Tests whether the AI can find specific details buried inside an enormous document (around 1 million tokens — many books). Higher is better. | — | 26.3% |
| Overview | ||
| Company | Anthropic | |
| Release date | Apr 7 2026 | Nov 18 2025 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
Claude Mythos PreviewvsGPT-6.1 TerraGemini 3.0 ProvsGPT-6.1 TerraClaude Mythos PreviewvsMuse EmberGemini 3.0 ProvsMuse EmberClaude Mythos PreviewvsGrok 4.7Gemini 3.0 ProvsGrok 4.7Claude Mythos PreviewvsDeepSeek-V4.1-Flash-1017Gemini 3.0 ProvsDeepSeek-V4.1-Flash-1017Claude Mythos PreviewvsMistral Large 4Gemini 3.0 ProvsMistral Large 4Claude Mythos PreviewvsKimi K3.1 ProGemini 3.0 ProvsKimi K3.1 ProFrequently asked questions
Claude Mythos Preview leads Gemini 3.0 Pro on 7 of the 7 benchmarks they both report. Gemini 3.0 Pro shipped 140 days before Claude Mythos Preview, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Mythos Preview) vs 1M (Gemini 3.0 Pro).
On SWE-Bench Pro, Claude Mythos Preview leads at 77.8% vs Gemini 3.0 Pro at 43.3%. On SWE-Bench Verified, Claude Mythos Preview leads at 93.9% vs Gemini 3.0 Pro at 76.2%. On Terminal-Bench 2.0, Claude Mythos Preview leads at 82% vs Gemini 3.0 Pro at 54.2%. On Humanity's Last Exam · no tools, Claude Mythos Preview leads at 56.8% vs Gemini 3.0 Pro at 37.5%. On Humanity's Last Exam · with tools, Claude Mythos Preview leads at 64.7% vs Gemini 3.0 Pro at 45.8%. On GPQA Diamond, Claude Mythos Preview leads at 94.5% vs Gemini 3.0 Pro at 91.9%. On CharXiv Reasoning, Claude Mythos Preview leads at 86.1% vs Gemini 3.0 Pro at 81.4%.