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 | ||
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 | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.201Cloudflare | — |
| $15.45Cloudflare | — | |
| These models have no shared benchmark scores. | ||
MATHReasoning — accuracy on the original competition mathematics test set | 68% | — |
MMLUGeneral knowledge — A 57-subject multiple-choice exam — history, law, medicine, maths — that was the standard measure of how much a model knows from 2020 until roughly 2024, when frontier scores crowded into the high 80s and labs moved on to harder tests. The scores here were published years apart under different testing setups, so read them as a historical record rather than a like-for-like ranking. Higher is better. | 86% | — |
MGSMMultilingual math — solving grade-school word problems translated into multiple languages | 86.9% | — |
MathVistaMultimodal reasoning — mathematical reasoning about visual information | 57.3% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 60.3% | — |
| Overview | ||
| Company | Meta | Qwen |
| Release date | Sep 25 2024 | Nov 11 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
LLaMA 3.2vsClaude Fable 5.2Qwen4-Max PreviewvsClaude Fable 5.2LLaMA 3.2vsGPT-6.1 TerraQwen4-Max PreviewvsGPT-6.1 TerraLLaMA 3.2vsGemini 4 CarbonQwen4-Max PreviewvsGemini 4 CarbonLLaMA 3.2vsGrok 4.7Qwen4-Max PreviewvsGrok 4.7LLaMA 3.2vsDeepSeek-V4.1-Flash-1017Qwen4-Max PreviewvsDeepSeek-V4.1-Flash-1017LLaMA 3.2vsMistral Large 4Qwen4-Max PreviewvsMistral Large 4Frequently asked questions
LLaMA 3.2 and Qwen4-Max Preview don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 3.2 shipped 777 days before Qwen4-Max Preview, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (LLaMA 3.2) vs 1M (Qwen4-Max Preview). LLaMA 3.2 is open weight, while Qwen4-Max Preview is closed.
Direct benchmark comparisons are unavailable — LLaMA 3.2 and Qwen4-Max Preview don't publish scores on any of the same benchmarks.