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. | — | 671B |
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 |
| 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. | — | 65.2% |
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% | — |
MMLUGeneral knowledge — A 57-subject multiple-choice exam — history, law, medicine, maths — that was the standard measure of how much a model knows from 2020 until roughly 2024, when frontier scores crowded into the high 80s and labs moved on to harder tests. The scores here were published years apart under different testing setups, so read them as a historical record rather than a like-for-like ranking. Higher is better. | — | 87.1% |
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. | — | 89.3% |
| Overview | ||
| Company | Anthropic | DeepSeek |
| Release date | Sep 1 2026 | Dec 25 2024 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Mythos 5.1vsGPT-6.1 SolDeepSeek V3 BasevsGPT-6.1 SolClaude Mythos 5.1vsGemini 4 ArgonDeepSeek V3 BasevsGemini 4 ArgonClaude Mythos 5.1vsMuse Spark OpenDeepSeek V3 BasevsMuse Spark OpenClaude Mythos 5.1vsGrok 4.7DeepSeek V3 BasevsGrok 4.7Claude Mythos 5.1vsMistral Large 4DeepSeek V3 BasevsMistral Large 4Claude Mythos 5.1vsKimi K3DeepSeek V3 BasevsKimi K3Frequently asked questions
Claude Mythos 5.1 and DeepSeek V3 Base don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek V3 Base shipped 615 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 (DeepSeek V3 Base). Claude Mythos 5.1 is closed, while DeepSeek V3 Base is open weight.
Direct benchmark comparisons are unavailable — Claude Mythos 5.1 and DeepSeek V3 Base don't publish scores on any of the same benchmarks.