Compare AI models
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | — | 120B |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 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. | — | $0.08DekaLLM |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.40DeepInfra |
| — | $37.00DeepInfra · bf16 | |
| 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. | — | 54% |
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. | — | 60.5% |
Terminal-Bench 4.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better. | 60.9% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 79.2% |
AA Intelligence IndexOverall intelligence — Artificial Analysis composite intelligence index across evals | — | 36 |
| Overview | ||
| Company | Anthropic | NVIDIA |
| Release date | Sep 1 2026 | Mar 11 2026 |
| Access | Closed | Open Source |
| Model details | View model | View model |
Other comparisons
Claude Mythos 5.1vsGPT-6.1 SolNemotron 3 SupervsGPT-6.1 SolClaude Mythos 5.1vsGemini 4 ArgonNemotron 3 SupervsGemini 4 ArgonClaude Mythos 5.1vsMuse Spark OpenNemotron 3 SupervsMuse Spark OpenClaude Mythos 5.1vsGrok 4.7Nemotron 3 SupervsGrok 4.7Claude Mythos 5.1vsDeepSeek-V4.1-FlashNemotron 3 SupervsDeepSeek-V4.1-FlashClaude Mythos 5.1vsMistral Large 4Nemotron 3 SupervsMistral Large 4Frequently asked questions
Claude Mythos 5.1 and Nemotron 3 Super don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Nemotron 3 Super shipped 174 days before Claude Mythos 5.1, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Mythos 5.1) vs 1M (Nemotron 3 Super). Claude Mythos 5.1 is closed, while Nemotron 3 Super is open source.
Direct benchmark comparisons are unavailable — Claude Mythos 5.1 and Nemotron 3 Super don't publish scores on any of the same benchmarks.