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 | 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. | 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. | — | $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. | $0.08DekaLLM | $0.12Parasail |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.40DeepInfra | $0.80Parasail |
| Benchmarks | ||
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% | 70.6% |
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 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 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% |
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% | — |
AA Intelligence IndexOverall intelligence — Artificial Analysis composite intelligence index across evals | 36 | — |
| Overview | ||
| Company | NVIDIA | Qwen |
| Release date | Mar 11 2026 | Feb 3 2026 |
| Access | Open Source | Open Weight |
| Model details | View model | View model |
Other comparisons
Nemotron 3 SupervsClaude Haiku 5.5Qwen3-Coder-NextvsClaude Haiku 5.5Nemotron 3 SupervsGPT-6.1 SolQwen3-Coder-NextvsGPT-6.1 SolNemotron 3 SupervsGemini 4 ArgonQwen3-Coder-NextvsGemini 4 ArgonNemotron 3 SupervsMuse Spark 1.3Qwen3-Coder-NextvsMuse Spark 1.3Nemotron 3 SupervsGrok 4.7Qwen3-Coder-NextvsGrok 4.7Nemotron 3 SupervsDeepSeek-V4.1-FlashQwen3-Coder-NextvsDeepSeek-V4.1-FlashFrequently asked questions
Qwen3-Coder-Next leads Nemotron 3 Super on 1 of the 1 benchmark they both report (SWE-Bench Verified). 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 3 Super. Qwen3-Coder-Next shipped 36 days before Nemotron 3 Super, so benchmark comparisons should account for the intervening progress.
Nemotron 3 Super has 120B parameters, while Qwen3-Coder-Next has 80B. Context windows are 1M (Nemotron 3 Super) vs 256k (Qwen3-Coder-Next). Nemotron 3 Super is open source, while Qwen3-Coder-Next is open weight.
On SWE-Bench Verified, Qwen3-Coder-Next leads at 70.6% vs Nemotron 3 Super at 60.5%.