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.
7B/13B/70B0.5B/1.5B/3B/7B/14B/32B
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.
4k128k
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.
—$0.66Cloudflare
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input.
—$1.00Cloudflare
—$182Cloudflare
Overview
CompanyMetaQwen
Release dateJul 18 2023Nov 12 2024
AccessOpen WeightOpen Weight
Model detailsView modelView model

Other comparisons

LLaMA 2vsClaude Haiku 5.5Qwen2.5-CodervsClaude Haiku 5.5LLaMA 2vsGPT-6.1 SolQwen2.5-CodervsGPT-6.1 SolLLaMA 2vsGemini 4 ArgonQwen2.5-CodervsGemini 4 ArgonLLaMA 2vsGrok 4.7Qwen2.5-CodervsGrok 4.7LLaMA 2vsDeepSeek-V4.1-Flash-1017Qwen2.5-CodervsDeepSeek-V4.1-Flash-1017LLaMA 2vsMistral Large 4Qwen2.5-CodervsMistral Large 4

Frequently asked questions

LLaMA 2 and Qwen2.5-Coder don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 2 shipped 483 days before Qwen2.5-Coder, so benchmark comparisons should account for the intervening progress.

LLaMA 2 has 7B/13B/70B parameters, while Qwen2.5-Coder has 0.5B/1.5B/3B/7B/14B/32B. Context windows are 4k (LLaMA 2) vs 128k (Qwen2.5-Coder).

Direct benchmark comparisons are unavailable — LLaMA 2 and Qwen2.5-Coder don't publish scores on any of the same benchmarks.