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. | 671B | 1B/4B/12B/27B |
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. | 128k | 128k |
| 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. | — | $0.04DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.05Parasail |
| — | $10.50Parasail · bf16 | |
| Benchmarks | ||
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. | 65.2% | 87.8% |
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. | 89.3% | 95.9% |
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. | — | 3% |
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. | — | 29.7% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 42.4% |
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. | 87.1% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | — | 64.9% |
| Overview | ||
| Company | DeepSeek | |
| Release date | Dec 25 2024 | Mar 12 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V3 BasevsClaude Haiku 5.5Gemma 3vsClaude Haiku 5.5DeepSeek V3 BasevsGPT-6.1 SolGemma 3vsGPT-6.1 SolDeepSeek V3 BasevsMuse Spark OpenGemma 3vsMuse Spark OpenDeepSeek V3 BasevsGrok 4.7Gemma 3vsGrok 4.7DeepSeek V3 BasevsMistral Large 4Gemma 3vsMistral Large 4DeepSeek V3 BasevsKimi K3Gemma 3vsKimi K3Frequently asked questions
Gemma 3 leads DeepSeek V3 Base on 2 of the 2 benchmarks they both report (HumanEval, GSM8K). DeepSeek V3 Base shipped 77 days before Gemma 3, so benchmark comparisons should account for the intervening progress.
DeepSeek V3 Base has 671B parameters, while Gemma 3 has 1B/4B/12B/27B. Context windows are 128k (DeepSeek V3 Base) vs 128k (Gemma 3).
On HumanEval, Gemma 3 leads at 87.8% vs DeepSeek V3 Base at 65.2%. On GSM8K, Gemma 3 leads at 95.9% vs DeepSeek V3 Base at 89.3%.