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. | 8B | — |
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. | 64k | 1M |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $2.50 |
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. | — | $7.50 |
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. | — | $1.475Alibaba |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $4.425Alibaba |
| — | $516Alibaba | |
| 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. | — | 72% |
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. | — | 60.6% |
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. | — | 80.4% |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 91.6% |
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. | — | 69.7% |
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. | — | 76.4% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 41.4% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 92.4% |
| Overview | ||
| Company | NVIDIA | Qwen |
| Release date | Oct 23 2026 | May 20 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 Bolt E4B 4GBvsClaude Haiku 5.5Qwen3.7-MaxvsClaude Haiku 5.5Nemotron 3.5 Bolt E4B 4GBvsGPT-6.1 SolQwen3.7-MaxvsGPT-6.1 SolNemotron 3.5 Bolt E4B 4GBvsGemini 4 ArgonQwen3.7-MaxvsGemini 4 ArgonNemotron 3.5 Bolt E4B 4GBvsMuse Spark OpenQwen3.7-MaxvsMuse Spark OpenNemotron 3.5 Bolt E4B 4GBvsGrok 4.7Qwen3.7-MaxvsGrok 4.7Nemotron 3.5 Bolt E4B 4GBvsDeepSeek-V4.1-Flash-1017Qwen3.7-MaxvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Nemotron 3.5 Bolt E4B 4GB and Qwen3.7-Max don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Qwen3.7-Max has a verified first-party API price: $2.50 per million input tokens and $7.50 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3.5 Bolt E4B 4GB. Qwen3.7-Max shipped 156 days before Nemotron 3.5 Bolt E4B 4GB, so benchmark comparisons should account for the intervening progress.
Context windows are 64k (Nemotron 3.5 Bolt E4B 4GB) vs 1M (Qwen3.7-Max). Nemotron 3.5 Bolt E4B 4GB is open weight, while Qwen3.7-Max is closed.
Direct benchmark comparisons are unavailable — Nemotron 3.5 Bolt E4B 4GB and Qwen3.7-Max don't publish scores on any of the same benchmarks.