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. | 74B | 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. | — | 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 every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $0.22Google |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $1.00DeepInfra |
| — | $110DeepInfra · turbo · fp4 | |
| 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% |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | 50.98% | — |
MCP AtlasMulti-step tool use — Can the AI chain together many tools and steps to complete one bigger task, rather than doing just a single thing? Higher is better. | 52.2% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 66.75% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 86.43% | — |
| Overview | ||
| Company | NVIDIA | Qwen |
| Release date | Oct 15 2026 | Jul 22 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 Bolt 74BvsClaude Haiku 5.5Qwen3-CodervsClaude Haiku 5.5Nemotron 3.5 Bolt 74BvsGPT-6.1 SolQwen3-CodervsGPT-6.1 SolNemotron 3.5 Bolt 74BvsGemini 4 ArgonQwen3-CodervsGemini 4 ArgonNemotron 3.5 Bolt 74BvsMuse Spark OpenQwen3-CodervsMuse Spark OpenNemotron 3.5 Bolt 74BvsGrok 4.7Qwen3-CodervsGrok 4.7Nemotron 3.5 Bolt 74BvsDeepSeek-V4.1-FlashQwen3-CodervsDeepSeek-V4.1-FlashFrequently asked questions
Nemotron 3.5 Bolt 74B 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 Bolt 74B. Qwen3-Coder shipped 450 days before Nemotron 3.5 Bolt 74B, so benchmark comparisons should account for the intervening progress.
Nemotron 3.5 Bolt 74B has 74B parameters, while Qwen3-Coder has 480B.
Direct benchmark comparisons are unavailable — Nemotron 3.5 Bolt 74B and Qwen3-Coder don't publish scores on any of the same benchmarks.