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 | 128k |
| These models have no shared benchmark scores. | ||
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 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. | 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. | 67.72% | — |
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% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 89.52% | — |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | 70.86% | — |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Oct 15 2026 | Feb 12 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 Super VLvsClaude Haiku 5.5GPT-5.3-Codex-SparkvsClaude Haiku 5.5Nemotron 3.5 Super VLvsGemini 4 ArgonGPT-5.3-Codex-SparkvsGemini 4 ArgonNemotron 3.5 Super VLvsMuse Spark OpenGPT-5.3-Codex-SparkvsMuse Spark OpenNemotron 3.5 Super VLvsGrok 4.7GPT-5.3-Codex-SparkvsGrok 4.7Nemotron 3.5 Super VLvsDeepSeek-V4.1-FlashGPT-5.3-Codex-SparkvsDeepSeek-V4.1-FlashNemotron 3.5 Super VLvsMistral Large 4GPT-5.3-Codex-SparkvsMistral Large 4Frequently asked questions
Nemotron 3.5 Super VL and GPT-5.3-Codex-Spark don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GPT-5.3-Codex-Spark shipped 245 days before Nemotron 3.5 Super VL, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Nemotron 3.5 Super VL) vs 128k (GPT-5.3-Codex-Spark). Nemotron 3.5 Super VL is open weight, while GPT-5.3-Codex-Spark is closed.
Direct benchmark comparisons are unavailable — Nemotron 3.5 Super VL and GPT-5.3-Codex-Spark don't publish scores on any of the same benchmarks.