AI Model Release Tracker

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>1T
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.
128k256k
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—
Overview
CompanyMetaQwen
Release dateSep 25 2024Sep 24 2025
AccessOpen WeightClosed
Model detailsView modelView model

Other comparisons

LLaMA 3.2vsClaude Haiku 5.5Qwen3-MaxvsClaude Haiku 5.5LLaMA 3.2vsGPT-6.1 TerraQwen3-MaxvsGPT-6.1 TerraLLaMA 3.2vsGemini 4 CarbonQwen3-MaxvsGemini 4 CarbonLLaMA 3.2vsGrok 4.7Qwen3-MaxvsGrok 4.7LLaMA 3.2vsDeepSeek-V4.1-Flash-1017Qwen3-MaxvsDeepSeek-V4.1-Flash-1017LLaMA 3.2vsMistral Large 4Qwen3-MaxvsMistral Large 4

Frequently asked questions

LLaMA 3.2 and Qwen3-Max don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 3.2 shipped 364 days before Qwen3-Max, so benchmark comparisons should account for the intervening progress.

LLaMA 3.2 has 1B/3B/11B/90B parameters, while Qwen3-Max has >1T. Context windows are 128k (LLaMA 3.2) vs 256k (Qwen3-Max). LLaMA 3.2 is open weight, while Qwen3-Max is closed.

Direct benchmark comparisons are unavailable — LLaMA 3.2 and Qwen3-Max don't publish scores on any of the same benchmarks.