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. | 100k | 1M |
| API pricingUSD per 1M tokens · lower wins | ||
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. | — | $0.08DekaLLM |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $0.40DeepInfra |
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. | — | 54% |
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. | — | 60.5% |
HumanEvalFunction synthesis — 164 small Python problems: the AI is given a function's description and has to write the working function. This was the coding benchmark of the GPT-3.5 and GPT-4 era, before the field moved to fixing real bugs in real repositories. Higher is better. | 58.7% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 79.2% |
GSM8KGrade-school math — Grade-school maths word problems that take a few steps of arithmetic to work through. It separated the models of 2022 and 2023 sharply, then saturated. One caveat on the historical numbers: OpenAI included part of the GSM8K training set in GPT-4's pre-training mix, so GPT-4's score is not a clean few-shot result. Higher is better. | 86.7% | — |
AA Intelligence IndexOverall intelligence — Artificial Analysis composite intelligence index across evals | — | 36 |
| Overview | ||
| Company | Anthropic | NVIDIA |
| Release date | Aug 9 2023 | Mar 11 2026 |
| Access | Closed | Open Source |
| Model details | View model | View model |
Other comparisons
Claude Instant 1.2vsGPT-6.1 SolNemotron 3 SupervsGPT-6.1 SolClaude Instant 1.2vsGemini 4 ArgonNemotron 3 SupervsGemini 4 ArgonClaude Instant 1.2vsMuse Spark 1.3Nemotron 3 SupervsMuse Spark 1.3Claude Instant 1.2vsGrok 4.7Nemotron 3 SupervsGrok 4.7Claude Instant 1.2vsDeepSeek-V4.1-FlashNemotron 3 SupervsDeepSeek-V4.1-FlashClaude Instant 1.2vsMistral Large 4Nemotron 3 SupervsMistral Large 4Frequently asked questions
Claude Instant 1.2 and Nemotron 3 Super don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Claude Instant 1.2 shipped 945 days before Nemotron 3 Super, so benchmark comparisons should account for the intervening progress.
Context windows are 100k (Claude Instant 1.2) vs 1M (Nemotron 3 Super). Claude Instant 1.2 is closed, while Nemotron 3 Super is open source.
Direct benchmark comparisons are unavailable — Claude Instant 1.2 and Nemotron 3 Super don't publish scores on any of the same benchmarks.