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 | 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. | 128k | 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. | $0.25SiliconFlow | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.00SiliconFlow | — |
| $100SiliconFlow · fp8 | — | |
| These models have no shared benchmark scores. | ||
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% |
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% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 72.41% |
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 | Mar 24 2025 | Oct 16 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V3-0324vsClaude Fable 5.2Nemotron Labs Dory 1.0 4BvsClaude Fable 5.2DeepSeek V3-0324vsGPT-6.1 TerraNemotron Labs Dory 1.0 4BvsGPT-6.1 TerraDeepSeek V3-0324vsGemini 4 CarbonNemotron Labs Dory 1.0 4BvsGemini 4 CarbonDeepSeek V3-0324vsMuse EmberNemotron Labs Dory 1.0 4BvsMuse EmberDeepSeek V3-0324vsGrok 4.7Nemotron Labs Dory 1.0 4BvsGrok 4.7DeepSeek V3-0324vsMistral Large 4Nemotron Labs Dory 1.0 4BvsMistral Large 4Frequently asked questions
DeepSeek V3-0324 and Nemotron Labs Dory 1.0 4B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek V3-0324 shipped 571 days before Nemotron Labs Dory 1.0 4B, so benchmark comparisons should account for the intervening progress.
DeepSeek V3-0324 has 671B parameters, while Nemotron Labs Dory 1.0 4B has 4B. Context windows are 128k (DeepSeek V3-0324) vs 256k (Nemotron Labs Dory 1.0 4B).
Direct benchmark comparisons are unavailable — DeepSeek V3-0324 and Nemotron Labs Dory 1.0 4B don't publish scores on any of the same benchmarks.