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. | — | 1B/4B/12B/27B |
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 | 128k |
| 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. | — | $0.04DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.10DeepInfra |
| — | $15.00DeepInfra · 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. | — | 3% |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 29.7% |
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. | — | 87.8% |
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. | — | 42.4% |
GSM8KGrade-school math — Grade-school maths word problems that take a few steps of arithmetic to work through. It separated the models of 2022 and 2023 sharply, then saturated. One caveat on the historical numbers: OpenAI included part of the GSM8K training set in GPT-4's pre-training mix, so GPT-4's score is not a clean few-shot result. Higher is better. | — | 95.9% |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | — | 64.9% |
| Overview | ||
| Company | Anthropic | |
| Release date | Sep 1 2026 | Mar 12 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Mythos 5.1vsGPT-6.1 SolGemma 3vsGPT-6.1 SolClaude Mythos 5.1vsMuse Spark OpenGemma 3vsMuse Spark OpenClaude Mythos 5.1vsGrok 4.7Gemma 3vsGrok 4.7Claude Mythos 5.1vsDeepSeek-V4.1-Flash-1017Gemma 3vsDeepSeek-V4.1-Flash-1017Claude Mythos 5.1vsMistral Large 4Gemma 3vsMistral Large 4Claude Mythos 5.1vsKimi K3Gemma 3vsKimi K3Frequently asked questions
Claude Mythos 5.1 and Gemma 3 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Gemma 3 shipped 538 days before Claude Mythos 5.1, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Mythos 5.1) vs 128k (Gemma 3). Claude Mythos 5.1 is closed, while Gemma 3 is open weight.
Direct benchmark comparisons are unavailable — Claude Mythos 5.1 and Gemma 3 don't publish scores on any of the same benchmarks.