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. | 30B | 235B |
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 |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $0.70 |
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. | — | $2.80 |
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. | $0.045Wafer | $0.08DeepInfra |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.13Wafer | $0.22NextBit |
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. | — | 6% |
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. | 51.6% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 75.4% | — |
| Overview | ||
| Company | NVIDIA | Qwen |
| Release date | Aug 11 2026 | Apr 29 2025 |
| Access | Open Source | Open Weight |
| Model details | View model | View model |
Other comparisons
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Nemotron 3.5 Lightning and Qwen3 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Qwen3 has a verified first-party API price: $0.70 per million input tokens and $2.80 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3.5 Lightning. Qwen3 shipped 469 days before Nemotron 3.5 Lightning, so benchmark comparisons should account for the intervening progress.
Nemotron 3.5 Lightning has 30B parameters, while Qwen3 has 235B. Context windows are 1M (Nemotron 3.5 Lightning) vs 128k (Qwen3). Nemotron 3.5 Lightning is open source, while Qwen3 is open weight.
Direct benchmark comparisons are unavailable — Nemotron 3.5 Lightning and Qwen3 don't publish scores on any of the same benchmarks.