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. | — | 1.6T |
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. | — | $0.90Relace |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $1.50Relace |
| — | $255Relace · fp4 | |
| These models have no shared benchmark scores. | ||
CheatBenchCheating rate — Measures how often AI agents try to cheat on difficult assignments, such as reading hidden answers, copying work or manipulating grading. The overall score gives equal weight to ten categories; the sycophancy category measures how far an agent shifts its beliefs toward a user's stated views. Scores describe each model in its tested agent setup, and task success is measured separately. Lower is better. | — | 75.1% |
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. | — | 14% |
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. | 49% | — |
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. | — | 93.5% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 83.4% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 90.1% |
| Overview | ||
| Company | Anthropic | DeepSeek |
| Release date | Oct 22 2024 | Apr 24 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude 3.5 Sonnet (upgraded)vsGPT-6.1 SolDeepSeek-V4-ProvsGPT-6.1 SolClaude 3.5 Sonnet (upgraded)vsGemini 4 ArgonDeepSeek-V4-ProvsGemini 4 ArgonClaude 3.5 Sonnet (upgraded)vsMuse Spark OpenDeepSeek-V4-ProvsMuse Spark OpenClaude 3.5 Sonnet (upgraded)vsGrok 4.7DeepSeek-V4-ProvsGrok 4.7Claude 3.5 Sonnet (upgraded)vsMistral Large 4DeepSeek-V4-ProvsMistral Large 4Claude 3.5 Sonnet (upgraded)vsKimi K3DeepSeek-V4-ProvsKimi K3Frequently asked questions
Claude 3.5 Sonnet (upgraded) and DeepSeek-V4-Pro don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Claude 3.5 Sonnet (upgraded) shipped 549 days before DeepSeek-V4-Pro, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (Claude 3.5 Sonnet (upgraded)) vs 1M (DeepSeek-V4-Pro). Claude 3.5 Sonnet (upgraded) is closed, while DeepSeek-V4-Pro is open weight.
Direct benchmark comparisons are unavailable — Claude 3.5 Sonnet (upgraded) and DeepSeek-V4-Pro don't publish scores on any of the same benchmarks.