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. | — | 30B |
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. | 200k | 1M |
| 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. | — | $0.05Novita |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.20Novita |
| — | $20.00Novita · fp4 | |
| These models have no shared benchmark scores. | ||
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. | — | 28% |
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. | 49% | — |
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. | — | 68.3% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 73% |
| Overview | ||
| Company | Anthropic | NVIDIA |
| Release date | Oct 22 2024 | Dec 15 2025 |
| Access | Closed | Open Source |
| Model details | View model | View model |
Other comparisons
Claude 3.5 Sonnet (upgraded)vsGPT-6.1 SolNemotron 3 NanovsGPT-6.1 SolClaude 3.5 Sonnet (upgraded)vsGemini 4 ArgonNemotron 3 NanovsGemini 4 ArgonClaude 3.5 Sonnet (upgraded)vsMuse Spark OpenNemotron 3 NanovsMuse Spark OpenClaude 3.5 Sonnet (upgraded)vsGrok 4.7Nemotron 3 NanovsGrok 4.7Claude 3.5 Sonnet (upgraded)vsDeepSeek-V4.1-Flash-1017Nemotron 3 NanovsDeepSeek-V4.1-Flash-1017Claude 3.5 Sonnet (upgraded)vsMistral Large 4Nemotron 3 NanovsMistral Large 4Frequently asked questions
Claude 3.5 Sonnet (upgraded) and Nemotron 3 Nano don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Claude 3.5 Sonnet (upgraded) shipped 419 days before Nemotron 3 Nano, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (Claude 3.5 Sonnet (upgraded)) vs 1M (Nemotron 3 Nano). Claude 3.5 Sonnet (upgraded) is closed, while Nemotron 3 Nano is open source.
Direct benchmark comparisons are unavailable — Claude 3.5 Sonnet (upgraded) and Nemotron 3 Nano don't publish scores on any of the same benchmarks.