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 | 8k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $30.00 |
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. | — | $60.00 |
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 | $30.00Azure |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.50Relace | $60.00Azure |
| $255Relace · fp4 | $9,000OpenAI list price | |
| 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% | — |
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% | — |
HumanEvalFunction synthesis — 164 small Python problems: the AI is given a function's description and has to write the working function. This was the coding benchmark of the GPT-3.5 and GPT-4 era, before the field moved to fixing real bugs in real repositories. Higher is better. | — | 67% |
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% | — |
MMLUGeneral knowledge — A 57-subject multiple-choice exam — history, law, medicine, maths — that was the standard measure of how much a model knows from 2020 until roughly 2024, when frontier scores crowded into the high 80s and labs moved on to harder tests. The scores here were published years apart under different testing setups, so read them as a historical record rather than a like-for-like ranking. Higher is better. | — | 86.4% |
GSM8KGrade-school math — Grade-school maths word problems that take a few steps of arithmetic to work through. It separated the models of 2022 and 2023 sharply, then saturated. One caveat on the historical numbers: OpenAI included part of the GSM8K training set in GPT-4's pre-training mix, so GPT-4's score is not a clean few-shot result. Higher is better. | — | 92% |
| Overview | ||
| Company | DeepSeek | OpenAI |
| Release date | Apr 24 2026 | Mar 14 2023 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek-V4-ProvsClaude Haiku 5.5GPT-4vsClaude Haiku 5.5DeepSeek-V4-ProvsGemini 4 ArgonGPT-4vsGemini 4 ArgonDeepSeek-V4-ProvsMuse Spark OpenGPT-4vsMuse Spark OpenDeepSeek-V4-ProvsGrok 4.7GPT-4vsGrok 4.7DeepSeek-V4-ProvsMistral Large 4GPT-4vsMistral Large 4DeepSeek-V4-ProvsKimi K3GPT-4vsKimi K3Frequently asked questions
DeepSeek-V4-Pro and GPT-4 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-4 has a verified first-party API price: $30.00 per million input tokens and $60.00 per million output tokens. No pay-as-you-go API rate is tracked for DeepSeek-V4-Pro. GPT-4 shipped 1137 days before DeepSeek-V4-Pro, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (DeepSeek-V4-Pro) vs 8k (GPT-4). DeepSeek-V4-Pro is open weight, while GPT-4 is closed.
Direct benchmark comparisons are unavailable — DeepSeek-V4-Pro and GPT-4 don't publish scores on any of the same benchmarks.