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. | 8B/49B | 32B |
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. | — | 128k |
| 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. | — | $0.66Cloudflare |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $1.00Cloudflare |
| Overview | ||
| Company | NVIDIA | Qwen |
| Release date | Mar 18 2025 | Nov 12 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
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Llama Nemotron (Nano/Super) and Qwen2.5-Coder don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen2.5-Coder shipped 126 days before Llama Nemotron (Nano/Super), so benchmark comparisons should account for the intervening progress.
Llama Nemotron (Nano/Super) has 8B/49B parameters, while Qwen2.5-Coder has 32B.
Direct benchmark comparisons are unavailable — Llama Nemotron (Nano/Super) and Qwen2.5-Coder don't publish scores on any of the same benchmarks.