Compare AI models
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | — | 9B |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $3.00 | — |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $15.00 | — |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.30 | — |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $3.00Amazon Bedrock | — |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $15.00Amazon Bedrock | — |
| Benchmarks | ||
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. | 79% | 5% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 83.4% | 64% |
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. | 77.2% | — |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 39% | — |
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. | — | 71.1% |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 13.6% | — |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 61.4% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 68% | — |
| Overview | ||
| Company | Anthropic | NVIDIA |
| Release date | Sep 29 2025 | Aug 18 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Sonnet 4.5vsGPT-6.1 SolNemotron Nano 2vsGPT-6.1 SolClaude Sonnet 4.5vsGemini 4 ArgonNemotron Nano 2vsGemini 4 ArgonClaude Sonnet 4.5vsMuse Spark 1.3Nemotron Nano 2vsMuse Spark 1.3Claude Sonnet 4.5vsGrok 4.7Nemotron Nano 2vsGrok 4.7Claude Sonnet 4.5vsDeepSeek-V4.1-FlashNemotron Nano 2vsDeepSeek-V4.1-FlashClaude Sonnet 4.5vsMistral Large 4Nemotron Nano 2vsMistral Large 4Frequently asked questions
Claude Sonnet 4.5 leads Nemotron Nano 2 on 2 of the 2 benchmarks they both report (BullshitBench v2, GPQA Diamond). Only Claude Sonnet 4.5 has a verified first-party API price: $3.00 per million input tokens and $15.00 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron Nano 2. Nemotron Nano 2 shipped 42 days before Claude Sonnet 4.5, so benchmark comparisons should account for the intervening progress.
Claude Sonnet 4.5 is closed, while Nemotron Nano 2 is open weight.
On BullshitBench v2, Claude Sonnet 4.5 leads at 79% vs Nemotron Nano 2 at 5%. On GPQA Diamond, Claude Sonnet 4.5 leads at 83.4% vs Nemotron Nano 2 at 64%.