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. | — | 355B |
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 | 200k |
| 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 | $0.60 |
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 | $2.20 |
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 | $0.11 |
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. | — | $0.40DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $1.60DeepInfra |
| $3,600Anthropic list price | $174Venice · fp4 | |
| These models have no shared benchmark scores. | ||
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. | — | 68% |
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 | Z.ai |
| Release date | Nov 9 2026 | Sep 30 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Mythos 5.2vsGPT-6.1 TerraGLM-4.6vsGPT-6.1 TerraClaude Mythos 5.2vsGemini 4 CarbonGLM-4.6vsGemini 4 CarbonClaude Mythos 5.2vsMuse EmberGLM-4.6vsMuse EmberClaude Mythos 5.2vsGrok 4.7GLM-4.6vsGrok 4.7Claude Mythos 5.2vsDeepSeek-V4.1-Flash-1017GLM-4.6vsDeepSeek-V4.1-Flash-1017Claude Mythos 5.2vsMistral Large 4GLM-4.6vsMistral Large 4Frequently asked questions
Claude Mythos 5.2 and GLM-4.6 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GLM-4.6 is cheaper on both input and output: $0.60 vs $8.00 per million input tokens, and $2.20 vs $40.00 per million output tokens. Claude Mythos 5.2 requires organization verification. GLM-4.6 shipped 405 days before Claude Mythos 5.2, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Mythos 5.2) vs 200k (GLM-4.6). Claude Mythos 5.2 is closed, while GLM-4.6 is open weight.
Direct benchmark comparisons are unavailable — Claude Mythos 5.2 and GLM-4.6 don't publish scores on any of the same benchmarks.