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. | — | 7B/13B/34B |
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 | 16k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $8.00Verified organizations only | — |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $40.00 | — |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.20 | — |
| $3,600Anthropic list price | — | |
| These models have no shared benchmark scores. | ||
HumanEvalFunction synthesis — 164 small Python problems: the AI is given a function's description and has to write the working function. This was the coding benchmark of the GPT-3.5 and GPT-4 era, before the field moved to fixing real bugs in real repositories. Higher is better. | — | 48.8% |
Terminal-Bench-Science 0.1Agentic scientific computing — The same command-line setup as Terminal-Bench, pointed at scientific work: the AI has to drive research tooling and computational workflows through to a result, rather than administer a machine. Version 0.1 is the first release of the task set, and scores run lower than on the general board. Higher is better. | 75.3% | — |
De novo binder design (24 hr)Protein design — de novo protein binder design within a 24-hour budget, as reported by Anthropic | 85.1% | — |
CyScenarioBenchCybersecurity — multi-step cybersecurity scenarios; each lab runs its own unique task set, so scores from different labs do not compare | 76.3% | — |
CVE RediscoveryCybersecurity — Anthropic internal evaluation: share of 1,829 published CVEs a model finds on its own from a snapshot of the project before the fix | 89.6% | — |
| Overview | ||
| Company | Anthropic | Meta |
| Release date | Nov 9 2026 | Aug 24 2023 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Mythos 5.2vsGPT-6.1 TerraCode LlamavsGPT-6.1 TerraClaude Mythos 5.2vsGemini 4 CarbonCode LlamavsGemini 4 CarbonClaude Mythos 5.2vsGrok 4.7Code LlamavsGrok 4.7Claude Mythos 5.2vsDeepSeek-V4.1-Flash-1017Code LlamavsDeepSeek-V4.1-Flash-1017Claude Mythos 5.2vsMistral Large 4Code LlamavsMistral Large 4Claude Mythos 5.2vsKimi K3.1 ProCode LlamavsKimi K3.1 ProFrequently asked questions
Claude Mythos 5.2 and Code Llama don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Claude Mythos 5.2 has a verified first-party price for an available API: $8.00 per million input tokens and $40.00 per million output tokens. No currently available pay-as-you-go API rate is tracked for Code Llama. Claude Mythos 5.2 requires organization verification. Code Llama shipped 1173 days before Claude Mythos 5.2, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Mythos 5.2) vs 16k (Code Llama). Claude Mythos 5.2 is closed, while Code Llama is open weight.
Direct benchmark comparisons are unavailable — Claude Mythos 5.2 and Code Llama don't publish scores on any of the same benchmarks.