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. | $0.50DeepInfra | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.15DeepInfra | — |
| $208DeepInfra · fp4 | — | |
| Benchmarks | ||
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 81% | 86.43% |
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. | 8% | — |
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. | 57.6% | — |
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. | 73.3% | — |
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% |
| Overview | ||
| Company | DeepSeek | NVIDIA |
| Release date | May 28 2025 | Oct 15 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek R1-0528vsClaude Haiku 5.5Nemotron 3.5 Bolt 74BvsClaude Haiku 5.5DeepSeek R1-0528vsGPT-6.1 SolNemotron 3.5 Bolt 74BvsGPT-6.1 SolDeepSeek R1-0528vsGemini 4 ArgonNemotron 3.5 Bolt 74BvsGemini 4 ArgonDeepSeek R1-0528vsMuse Spark OpenNemotron 3.5 Bolt 74BvsMuse Spark OpenDeepSeek R1-0528vsGrok 4.7Nemotron 3.5 Bolt 74BvsGrok 4.7DeepSeek R1-0528vsMistral Large 4Nemotron 3.5 Bolt 74BvsMistral Large 4Frequently asked questions
Nemotron 3.5 Bolt 74B leads DeepSeek R1-0528 on 1 of the 1 benchmark they both report (GPQA Diamond). DeepSeek R1-0528 shipped 505 days before Nemotron 3.5 Bolt 74B, so benchmark comparisons should account for the intervening progress.
DeepSeek R1-0528 has 671B parameters, while Nemotron 3.5 Bolt 74B has 74B. Context windows are 128k (DeepSeek R1-0528) vs 256k (Nemotron 3.5 Bolt 74B).
On GPQA Diamond, Nemotron 3.5 Bolt 74B leads at 86.43% vs DeepSeek R1-0528 at 81%.