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. | — | 284B |
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 | 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. | — | $0.05Relace |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.1652StreamLake |
| — | $24.78StreamLake · fp8 | |
| Benchmarks | ||
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. | 46% | 18% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 59.4% | 88.1% |
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. | 33.4% | — |
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. | — | 91.6% |
| Overview | ||
| Company | Anthropic | DeepSeek |
| Release date | Jun 20 2024 | Apr 24 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude 3.5 SonnetvsGPT-6.1 SolDeepSeek-V4-FlashvsGPT-6.1 SolClaude 3.5 SonnetvsGemini 4 ArgonDeepSeek-V4-FlashvsGemini 4 ArgonClaude 3.5 SonnetvsMuse Spark OpenDeepSeek-V4-FlashvsMuse Spark OpenClaude 3.5 SonnetvsGrok 4.7DeepSeek-V4-FlashvsGrok 4.7Claude 3.5 SonnetvsMistral Large 4DeepSeek-V4-FlashvsMistral Large 4Claude 3.5 SonnetvsKimi K3DeepSeek-V4-FlashvsKimi K3Frequently asked questions
Claude 3.5 Sonnet and DeepSeek-V4-Flash are evenly matched across the 2 benchmarks they both report (BullshitBench v2, GPQA Diamond). Claude 3.5 Sonnet shipped 673 days before DeepSeek-V4-Flash, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (Claude 3.5 Sonnet) vs 1M (DeepSeek-V4-Flash). Claude 3.5 Sonnet is closed, while DeepSeek-V4-Flash is open weight.
On BullshitBench v2, Claude 3.5 Sonnet leads at 46% vs DeepSeek-V4-Flash at 18%. On GPQA Diamond, DeepSeek-V4-Flash leads at 88.1% vs Claude 3.5 Sonnet at 59.4%.