Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | 74B | 397B |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 256k | 1M |
| 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. | — | $0.40 |
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. | — | $2.40 |
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. | — | $0.26Alibaba |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $1.56Alibaba |
| — | $130Alibaba | |
| These models have no shared benchmark scores. | ||
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 | Feb 16 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 Bolt 74BvsClaude Haiku 5.5Qwen3.5-PlusvsClaude Haiku 5.5Nemotron 3.5 Bolt 74BvsGPT-6.1 SolQwen3.5-PlusvsGPT-6.1 SolNemotron 3.5 Bolt 74BvsGemini 4 ArgonQwen3.5-PlusvsGemini 4 ArgonNemotron 3.5 Bolt 74BvsMuse Spark OpenQwen3.5-PlusvsMuse Spark OpenNemotron 3.5 Bolt 74BvsGrok 4.7Qwen3.5-PlusvsGrok 4.7Nemotron 3.5 Bolt 74BvsDeepSeek-V4.1-Flash-1017Qwen3.5-PlusvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Nemotron 3.5 Bolt 74B and Qwen3.5-Plus don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Qwen3.5-Plus has a verified first-party API price: $0.40 per million input tokens and $2.40 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3.5 Bolt 74B. Qwen3.5-Plus shipped 241 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 Qwen3.5-Plus has 397B. Context windows are 256k (Nemotron 3.5 Bolt 74B) vs 1M (Qwen3.5-Plus). Nemotron 3.5 Bolt 74B is open weight, while Qwen3.5-Plus is closed.
Direct benchmark comparisons are unavailable — Nemotron 3.5 Bolt 74B and Qwen3.5-Plus don't publish scores on any of the same benchmarks.