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. | — | 120B |
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. | 1M | 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. | — | $0.00Nvidia |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.00Nvidia |
| — | $29.50Darkbloom · nvfp4 | |
| Benchmarks | ||
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. | 77.8% | 51.78% |
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. | 93.9% | 71.1% |
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. | 87.3% | 67.72% |
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. | 64.7% | 29.2% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 94.5% | 89.52% |
SWE-Bench MultimodalMultimodal coding — Real bug reports that arrive with pictures attached — a screenshot, a mockup, a page rendering wrongly — so the AI has to read the image as well as the code to work out what to fix. Higher is better. | 59% | — |
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% |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | 82% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 78% |
CyberGymCybersecurity — Tests the AI on cybersecurity challenges — finding and exploiting software weaknesses inside a safe sandbox. Higher is better. | 83.1% | — |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 56.8% | — |
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. | 79.6% | — |
CharXiv ReasoningChart reasoning — Can the AI read and reason about complex charts and figures, not just text? Higher is better. | 86.1% | — |
CharXiv Reasoning · with toolsChart reasoning — The same chart-and-figure reasoning test, run with the AI allowed to use tools — writing code to inspect the image, for instance — rather than reading the chart unaided. Scores run higher than the unaided version, so read the two as separate tests. Higher is better. | 93.2% | — |
GraphWalks BFS (256K–1M)Long context — breadth-first search F1 on the 200 GraphWalks problems with 256K to 1M tokens of context | 80% | — |
| Overview | ||
| Company | Anthropic | NVIDIA |
| Release date | Apr 7 2026 | Oct 15 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Mythos PreviewvsGPT-6.1 TerraNemotron 3.5 Super VLvsGPT-6.1 TerraClaude Mythos PreviewvsGemini 4 CarbonNemotron 3.5 Super VLvsGemini 4 CarbonClaude Mythos PreviewvsMuse EmberNemotron 3.5 Super VLvsMuse EmberClaude Mythos PreviewvsGrok 4.7Nemotron 3.5 Super VLvsGrok 4.7Claude Mythos PreviewvsDeepSeek-V4.1-Flash-1017Nemotron 3.5 Super VLvsDeepSeek-V4.1-Flash-1017Claude Mythos PreviewvsMistral Large 4Nemotron 3.5 Super VLvsMistral Large 4Frequently asked questions
Claude Mythos Preview leads Nemotron 3.5 Super VL on 5 of the 5 benchmarks they both report. Claude Mythos Preview shipped 191 days before Nemotron 3.5 Super VL, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Mythos Preview) vs 1M (Nemotron 3.5 Super VL). Claude Mythos Preview is closed, while Nemotron 3.5 Super VL is open weight.
On SWE-Bench Pro, Claude Mythos Preview leads at 77.8% vs Nemotron 3.5 Super VL at 51.78%. On SWE-Bench Verified, Claude Mythos Preview leads at 93.9% vs Nemotron 3.5 Super VL at 71.1%. On SWE-Bench Multilingual, Claude Mythos Preview leads at 87.3% vs Nemotron 3.5 Super VL at 67.72%. On Humanity's Last Exam · with tools, Claude Mythos Preview leads at 64.7% vs Nemotron 3.5 Super VL at 29.2%. On GPQA Diamond, Claude Mythos Preview leads at 94.5% vs Nemotron 3.5 Super VL at 89.52%.