Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | — | 400B |
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 |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $0.1875DigitalOcean |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.6525DigitalOcean |
| — | $70.13DigitalOcean | |
| These models have no shared benchmark scores. | ||
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. | — | 29% |
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. | 95.5% | — |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | 88% | — |
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. | 88% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 88% | — |
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. | 64.5% | — |
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. | 85% | — |
| Overview | ||
| Company | Anthropic | Meta |
| Release date | Jun 9 2026 | Apr 5 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Mythos 5vsGPT-6.1 SolLLaMA 4 MaverickvsGPT-6.1 SolClaude Mythos 5vsGemini 4 ArgonLLaMA 4 MaverickvsGemini 4 ArgonClaude Mythos 5vsGrok 4.7LLaMA 4 MaverickvsGrok 4.7Claude Mythos 5vsDeepSeek-V4.1-Flash-1017LLaMA 4 MaverickvsDeepSeek-V4.1-Flash-1017Claude Mythos 5vsMistral Large 4LLaMA 4 MaverickvsMistral Large 4Claude Mythos 5vsKimi K3LLaMA 4 MaverickvsKimi K3Frequently asked questions
Claude Mythos 5 and LLaMA 4 Maverick don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 4 Maverick shipped 430 days before Claude Mythos 5, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Mythos 5) vs 1M (LLaMA 4 Maverick). Claude Mythos 5 is closed, while LLaMA 4 Maverick is open weight.
Direct benchmark comparisons are unavailable — Claude Mythos 5 and LLaMA 4 Maverick don't publish scores on any of the same benchmarks.