Compare AI models
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $3.00 | $0.66 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $15.00 | $1.98 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.30 | $0.022 |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $3.00Amazon Bedrock | $0.2254Wafer |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $15.00Amazon Bedrock | $1.98DeepSeek |
| 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. | 79% | 35% |
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. | 77.2% | 80.6% |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 13.6% | 61.3% |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 39% | — |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | — | 87.9% |
Toolathlon-VerifiedPersonal tool use — Tests how well the AI uses everyday personal tools and apps to get things done — a human-checked version of Toolathlon. Higher is better. | — | 74.1% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 83.4% |
CyberGymCybersecurity — Tests the AI on cybersecurity challenges — finding and exploiting software weaknesses inside a safe sandbox. Higher is better. | — | 83.3% |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | — | 42.7% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 60% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 83.4% | — |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 61.4% | — |
AutomationBenchBusiness workflows — Tests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | — | 31.8% |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 68% | — |
| Overview | ||
| Company | Anthropic | DeepSeek |
| Release date | Sep 29 2025 | Aug 13 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Sonnet 4.5vsGPT-6 SolDeepSeek-V4-Pro-0813vsGPT-6 SolClaude Sonnet 4.5vsGemini 3.8 FlashDeepSeek-V4-Pro-0813vsGemini 3.8 FlashClaude Sonnet 4.5vsMuse Spark 1.3DeepSeek-V4-Pro-0813vsMuse Spark 1.3Claude Sonnet 4.5vsGrok 4.7DeepSeek-V4-Pro-0813vsGrok 4.7Claude Sonnet 4.5vsMistral Medium 3.5DeepSeek-V4-Pro-0813vsMistral Medium 3.5Claude Sonnet 4.5vsKimi K3DeepSeek-V4-Pro-0813vsKimi K3Frequently asked questions
DeepSeek-V4-Pro-0813 leads Claude Sonnet 4.5 on 2 of the 3 benchmarks they both report (BullshitBench v2, SWE-Bench Verified, ARC-AGI-2). DeepSeek-V4-Pro-0813 is cheaper on both input and output: $0.66 vs $3.00 per million input tokens, and $1.98 vs $15.00 per million output tokens. Figures are base-tier rates. Claude Sonnet 4.5 shipped 318 days before DeepSeek-V4-Pro-0813, so benchmark comparisons should account for the intervening progress.
Claude Sonnet 4.5 is closed, while DeepSeek-V4-Pro-0813 is open weight.
On BullshitBench v2, Claude Sonnet 4.5 leads at 79% vs DeepSeek-V4-Pro-0813 at 35%. On SWE-Bench Verified, DeepSeek-V4-Pro-0813 leads at 80.6% vs Claude Sonnet 4.5 at 77.2%. On ARC-AGI-2, DeepSeek-V4-Pro-0813 leads at 61.3% vs Claude Sonnet 4.5 at 13.6%.