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. | 1B/3B/11B/90B | — |
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. | 128k | 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. | — | $0.50 |
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. | — | $3.00 |
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. | $0.027Cloudflare | $0.325Alibaba |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.201Cloudflare | $1.95Alibaba |
| $15.45Cloudflare | $163Alibaba | |
| 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 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. | — | 78.8% |
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. | — | 28.8% |
| Overview | ||
| Company | Meta | Qwen |
| Release date | Sep 25 2024 | Apr 2 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
LLaMA 3.2vsClaude Haiku 5.5Qwen3.6-PlusvsClaude Haiku 5.5LLaMA 3.2vsGPT-6.1 SolQwen3.6-PlusvsGPT-6.1 SolLLaMA 3.2vsGemini 4 ArgonQwen3.6-PlusvsGemini 4 ArgonLLaMA 3.2vsGrok 4.7Qwen3.6-PlusvsGrok 4.7LLaMA 3.2vsDeepSeek-V4.1-Flash-1017Qwen3.6-PlusvsDeepSeek-V4.1-Flash-1017LLaMA 3.2vsMistral Large 4Qwen3.6-PlusvsMistral Large 4Frequently asked questions
LLaMA 3.2 and Qwen3.6-Plus don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Qwen3.6-Plus has a verified first-party API price: $0.50 per million input tokens and $3.00 per million output tokens. No pay-as-you-go API rate is tracked for LLaMA 3.2. LLaMA 3.2 shipped 554 days before Qwen3.6-Plus, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (LLaMA 3.2) vs 1M (Qwen3.6-Plus). LLaMA 3.2 is open weight, while Qwen3.6-Plus is closed.
Direct benchmark comparisons are unavailable — LLaMA 3.2 and Qwen3.6-Plus don't publish scores on any of the same benchmarks.