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 | 480B |
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 | 256k |
| 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. | — | $1.00 |
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. | — | $5.00 |
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 | $0.22Google |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.13Wafer | $1.00DeepInfra |
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. | — | 20% |
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 | Jul 22 2025 |
| Access | Open Source | Open Weight |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 LightningvsClaude Haiku 5.5Qwen3-CodervsClaude Haiku 5.5Nemotron 3.5 LightningvsGPT-6.1 SolQwen3-CodervsGPT-6.1 SolNemotron 3.5 LightningvsGemini 4 ArgonQwen3-CodervsGemini 4 ArgonNemotron 3.5 LightningvsMuse Spark 1.3Qwen3-CodervsMuse Spark 1.3Nemotron 3.5 LightningvsGrok 4.7Qwen3-CodervsGrok 4.7Nemotron 3.5 LightningvsDeepSeek-V4.1-FlashQwen3-CodervsDeepSeek-V4.1-FlashFrequently asked questions
Nemotron 3.5 Lightning and Qwen3-Coder don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Qwen3-Coder has a verified first-party API price: $1.00 per million input tokens and $5.00 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3.5 Lightning. Qwen3-Coder shipped 385 days before Nemotron 3.5 Lightning, so benchmark comparisons should account for the intervening progress.
Nemotron 3.5 Lightning has 30B parameters, while Qwen3-Coder has 480B. Context windows are 1M (Nemotron 3.5 Lightning) vs 256k (Qwen3-Coder). Nemotron 3.5 Lightning is open source, while Qwen3-Coder is open weight.
Direct benchmark comparisons are unavailable — Nemotron 3.5 Lightning and Qwen3-Coder don't publish scores on any of the same benchmarks.