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. | — | 120B |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | — | 1M |
| 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 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. | $3.00Amazon Bedrock | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $15.00Amazon Bedrock | — |
| $1,350Anthropic list price | — | |
| 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. | 77.2% | 71.1% |
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% | 89.52% |
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% | — |
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | — | 51.78% |
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. | — | 67.72% |
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. | — | 88.22% |
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. | — | 66.7% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 78% |
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% | — |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | — | 70.86% |
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 | Oct 15 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Sonnet 4.5vsGPT-6.1 SolNemotron 3.5 Super VLvsGPT-6.1 SolClaude Sonnet 4.5vsGemini 4 ArgonNemotron 3.5 Super VLvsGemini 4 ArgonClaude Sonnet 4.5vsMuse Spark OpenNemotron 3.5 Super VLvsMuse Spark OpenClaude Sonnet 4.5vsGrok 4.7Nemotron 3.5 Super VLvsGrok 4.7Claude Sonnet 4.5vsDeepSeek-V4.1-FlashNemotron 3.5 Super VLvsDeepSeek-V4.1-FlashClaude Sonnet 4.5vsMistral Large 4Nemotron 3.5 Super VLvsMistral Large 4Frequently asked questions
Claude Sonnet 4.5 and Nemotron 3.5 Super VL are evenly matched across the 2 benchmarks they both report (SWE-Bench Verified, 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 3.5 Super VL. Claude Sonnet 4.5 shipped 381 days before Nemotron 3.5 Super VL, so benchmark comparisons should account for the intervening progress.
Claude Sonnet 4.5 is closed, while Nemotron 3.5 Super VL is open weight.
On SWE-Bench Verified, Claude Sonnet 4.5 leads at 77.2% vs Nemotron 3.5 Super VL at 71.1%. On GPQA Diamond, Nemotron 3.5 Super VL leads at 89.52% vs Claude Sonnet 4.5 at 83.4%.