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/34B80B
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.
16k256k
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.
—$0.072DeepInfra
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input.
—$0.88DeepInfra
—$73.00DeepInfra · fp8
Overview
CompanyMetaQwen
Release dateAug 24 2023Sep 11 2025
AccessOpen WeightOpen Weight
Model detailsView modelView model

Other comparisons

Code LlamavsClaude Haiku 5.5Qwen3-NextvsClaude Haiku 5.5Code LlamavsGPT-6.1 SolQwen3-NextvsGPT-6.1 SolCode LlamavsGemini 4 ArgonQwen3-NextvsGemini 4 ArgonCode LlamavsGrok 4.7Qwen3-NextvsGrok 4.7Code LlamavsDeepSeek-V4.1-Flash-1017Qwen3-NextvsDeepSeek-V4.1-Flash-1017Code LlamavsMistral Large 4Qwen3-NextvsMistral Large 4

Frequently asked questions

Code Llama and Qwen3-Next don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama shipped 749 days before Qwen3-Next, so benchmark comparisons should account for the intervening progress.

Code Llama has 7B/13B/34B parameters, while Qwen3-Next has 80B. Context windows are 16k (Code Llama) vs 256k (Qwen3-Next).

Direct benchmark comparisons are unavailable — Code Llama and Qwen3-Next don't publish scores on any of the same benchmarks.