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 | 74B |
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. | $0.25DeepInfra | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.95DeepInfra | — |
| $97.50DeepInfra · fp4 | — | |
| 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. | 66% | — |
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. | 74.8% | — |
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. | — | 50.98% |
MCP AtlasMulti-step tool use — Can the AI chain together many tools and steps to complete one bigger task, rather than doing just a single thing? Higher is better. | — | 52.2% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 66.75% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 15.9% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 86.43% |
| Overview | ||
| Company | DeepSeek | NVIDIA |
| Release date | Aug 21 2025 | Oct 15 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.1vsClaude Haiku 5.5Nemotron 3.5 Bolt 74BvsClaude Haiku 5.5DeepSeek V3.1vsGPT-6.1 SolNemotron 3.5 Bolt 74BvsGPT-6.1 SolDeepSeek V3.1vsGemini 4 ArgonNemotron 3.5 Bolt 74BvsGemini 4 ArgonDeepSeek V3.1vsMuse Spark OpenNemotron 3.5 Bolt 74BvsMuse Spark OpenDeepSeek V3.1vsGrok 4.7Nemotron 3.5 Bolt 74BvsGrok 4.7DeepSeek V3.1vsMistral Large 4Nemotron 3.5 Bolt 74BvsMistral Large 4Frequently asked questions
DeepSeek V3.1 and Nemotron 3.5 Bolt 74B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek V3.1 shipped 420 days before Nemotron 3.5 Bolt 74B, so benchmark comparisons should account for the intervening progress.
DeepSeek V3.1 has 671B parameters, while Nemotron 3.5 Bolt 74B has 74B. Context windows are 128k (DeepSeek V3.1) vs 256k (Nemotron 3.5 Bolt 74B).
Direct benchmark comparisons are unavailable — DeepSeek V3.1 and Nemotron 3.5 Bolt 74B don't publish scores on any of the same benchmarks.