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. | 284B | 4B |
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 | 256k |
| 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. | $0.05Relace | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.1652StreamLake | — |
| $24.78StreamLake · fp8 | — | |
| Benchmarks | ||
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 88.1% | 72.41% |
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. | 18% | — |
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. | — | 59.5% |
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. | 91.6% | — |
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. | — | 18.1% |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | — | 77.94% |
| Overview | ||
| Company | DeepSeek | NVIDIA |
| Release date | Apr 24 2026 | Oct 16 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek-V4-FlashvsClaude Fable 5.2Nemotron Labs Dory 1.0 4BvsClaude Fable 5.2DeepSeek-V4-FlashvsGPT-6.1 TerraNemotron Labs Dory 1.0 4BvsGPT-6.1 TerraDeepSeek-V4-FlashvsGemini 4 CarbonNemotron Labs Dory 1.0 4BvsGemini 4 CarbonDeepSeek-V4-FlashvsMuse EmberNemotron Labs Dory 1.0 4BvsMuse EmberDeepSeek-V4-FlashvsGrok 4.7Nemotron Labs Dory 1.0 4BvsGrok 4.7DeepSeek-V4-FlashvsMistral Large 4Nemotron Labs Dory 1.0 4BvsMistral Large 4Frequently asked questions
DeepSeek-V4-Flash leads Nemotron Labs Dory 1.0 4B on 1 of the 1 benchmark they both report (GPQA Diamond). DeepSeek-V4-Flash shipped 175 days before Nemotron Labs Dory 1.0 4B, so benchmark comparisons should account for the intervening progress.
DeepSeek-V4-Flash has 284B parameters, while Nemotron Labs Dory 1.0 4B has 4B. Context windows are 1M (DeepSeek-V4-Flash) vs 256k (Nemotron Labs Dory 1.0 4B).
On GPQA Diamond, DeepSeek-V4-Flash leads at 88.1% vs Nemotron Labs Dory 1.0 4B at 72.41%.