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. | 200k | 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. | — | $0.50DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $2.15DeepInfra |
| — | $208DeepInfra · fp4 | |
| Benchmarks | ||
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 40.4% | 81% |
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. | — | 8% |
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. | — | 57.6% |
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. | — | 73.3% |
| Overview | ||
| Company | Anthropic | DeepSeek |
| Release date | Mar 4 2024 | May 28 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude 3 SonnetvsGPT-6.1 SolDeepSeek R1-0528vsGPT-6.1 SolClaude 3 SonnetvsGemini 4 ArgonDeepSeek R1-0528vsGemini 4 ArgonClaude 3 SonnetvsMuse Spark OpenDeepSeek R1-0528vsMuse Spark OpenClaude 3 SonnetvsGrok 4.7DeepSeek R1-0528vsGrok 4.7Claude 3 SonnetvsMistral Large 4DeepSeek R1-0528vsMistral Large 4Claude 3 SonnetvsKimi K3DeepSeek R1-0528vsKimi K3Frequently asked questions
DeepSeek R1-0528 leads Claude 3 Sonnet on 1 of the 1 benchmark they both report (GPQA Diamond). Claude 3 Sonnet shipped 450 days before DeepSeek R1-0528, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (Claude 3 Sonnet) vs 128k (DeepSeek R1-0528). Claude 3 Sonnet is closed, while DeepSeek R1-0528 is open weight.
On GPQA Diamond, DeepSeek R1-0528 leads at 81% vs Claude 3 Sonnet at 40.4%.