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. | 1M | 1M |
| 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. | $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. | $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. | $0.022 | — |
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. | $0.0999Relace | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.98DeepSeek | — |
| $231DeepSeek list price | — | |
| 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. | 35% | 15% |
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. | 80.6% | 21.4% |
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. | — | 35.1% |
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% | — |
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. | 61.3% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 62.1% |
MMLU-ProKnowledge — accuracy on the ten-option MMLU-Pro benchmark | — | 76.4% |
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. | — | 70.7% |
| Overview | ||
| Company | DeepSeek | |
| Release date | Aug 13 2026 | Dec 11 2024 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek-V4-Pro-0813vsClaude Fable 5.2Gemini 2.0 FlashvsClaude Fable 5.2DeepSeek-V4-Pro-0813vsGPT-6.1 TerraGemini 2.0 FlashvsGPT-6.1 TerraDeepSeek-V4-Pro-0813vsMuse EmberGemini 2.0 FlashvsMuse EmberDeepSeek-V4-Pro-0813vsGrok 4.7Gemini 2.0 FlashvsGrok 4.7DeepSeek-V4-Pro-0813vsMistral Large 4Gemini 2.0 FlashvsMistral Large 4DeepSeek-V4-Pro-0813vsKimi K3.1 ProGemini 2.0 FlashvsKimi K3.1 ProFrequently asked questions
DeepSeek-V4-Pro-0813 leads Gemini 2.0 Flash on 2 of the 2 benchmarks they both report (BullshitBench v2, SWE-Bench Verified). Only DeepSeek-V4-Pro-0813 has a verified first-party price for an available API: $0.66 per million input tokens and $1.98 per million output tokens. No currently available pay-as-you-go API rate is tracked for Gemini 2.0 Flash. Gemini 2.0 Flash shipped 610 days before DeepSeek-V4-Pro-0813, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (DeepSeek-V4-Pro-0813) vs 1M (Gemini 2.0 Flash). DeepSeek-V4-Pro-0813 is open weight, while Gemini 2.0 Flash is closed.
On BullshitBench v2, DeepSeek-V4-Pro-0813 leads at 35% vs Gemini 2.0 Flash at 15%. On SWE-Bench Verified, DeepSeek-V4-Pro-0813 leads at 80.6% vs Gemini 2.0 Flash at 21.4%.