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. | 340B | 80B |
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. | — | 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. | — | $0.30 |
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. | — | $1.50 |
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. | — | $0.12Parasail |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $0.80Parasail |
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. | — | 44.3% |
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. | — | 70.6% |
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. | — | 62.8% |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | — | 36.2% |
| Overview | ||
| Company | NVIDIA | Qwen |
| Release date | Jun 14 2024 | Feb 3 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Nemotron-4 340BvsClaude Haiku 5.5Qwen3-Coder-NextvsClaude Haiku 5.5Nemotron-4 340BvsGPT-6.1 SolQwen3-Coder-NextvsGPT-6.1 SolNemotron-4 340BvsGemini 4 ArgonQwen3-Coder-NextvsGemini 4 ArgonNemotron-4 340BvsMuse Spark 1.3Qwen3-Coder-NextvsMuse Spark 1.3Nemotron-4 340BvsGrok 4.7Qwen3-Coder-NextvsGrok 4.7Nemotron-4 340BvsDeepSeek-V4.1-FlashQwen3-Coder-NextvsDeepSeek-V4.1-FlashFrequently asked questions
Nemotron-4 340B and Qwen3-Coder-Next don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Qwen3-Coder-Next has a verified first-party API price: $0.30 per million input tokens and $1.50 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron-4 340B. Nemotron-4 340B shipped 599 days before Qwen3-Coder-Next, so benchmark comparisons should account for the intervening progress.
Nemotron-4 340B has 340B parameters, while Qwen3-Coder-Next has 80B.
Direct benchmark comparisons are unavailable — Nemotron-4 340B and Qwen3-Coder-Next don't publish scores on any of the same benchmarks.