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. | $0.27SiliconFlow | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.00SiliconFlow | — |
| $104SiliconFlow · fp8 | — | |
| Benchmarks | ||
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. | 68.4% | 59.5% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 80.7% | 72.41% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | 57.8% | — |
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. | 74.9% | — |
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 | Sep 22 2025 | Oct 16 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.1 TerminusvsClaude Fable 5.2Nemotron Labs Dory 1.0 4BvsClaude Fable 5.2DeepSeek V3.1 TerminusvsGPT-6.1 TerraNemotron Labs Dory 1.0 4BvsGPT-6.1 TerraDeepSeek V3.1 TerminusvsGemini 4 CarbonNemotron Labs Dory 1.0 4BvsGemini 4 CarbonDeepSeek V3.1 TerminusvsMuse EmberNemotron Labs Dory 1.0 4BvsMuse EmberDeepSeek V3.1 TerminusvsGrok 4.7Nemotron Labs Dory 1.0 4BvsGrok 4.7DeepSeek V3.1 TerminusvsMistral Large 4Nemotron Labs Dory 1.0 4BvsMistral Large 4Frequently asked questions
DeepSeek V3.1 Terminus leads Nemotron Labs Dory 1.0 4B on 2 of the 2 benchmarks they both report (SWE-Bench Verified, GPQA Diamond). DeepSeek V3.1 Terminus shipped 389 days before Nemotron Labs Dory 1.0 4B, so benchmark comparisons should account for the intervening progress.
DeepSeek V3.1 Terminus has 671B parameters, while Nemotron Labs Dory 1.0 4B has 4B. Context windows are 128k (DeepSeek V3.1 Terminus) vs 256k (Nemotron Labs Dory 1.0 4B).
On SWE-Bench Verified, DeepSeek V3.1 Terminus leads at 68.4% vs Nemotron Labs Dory 1.0 4B at 59.5%. On GPQA Diamond, DeepSeek V3.1 Terminus leads at 80.7% vs Nemotron Labs Dory 1.0 4B at 72.41%.