Open Weight Models
Home›Compare›Family comparison
Family comparison · source-first decision page

Qwen vs Llama Open-Weight Models

Compare Qwen and Llama open-weight families by licensing, size, multimodality, context, hardware class and self-hosted deployment.

Updated 1 Oct 20268 referenced modelsNo universal ranking
Direct answer

The largest structural difference is licensing and family composition. Many Qwen checkpoints in the OWM registry use Apache 2.0, while Llama releases use Meta’s community licenses with model-specific conditions. Both families span multiple sizes and include multimodal options. Evaluate the exact checkpoint for quality, runtime support and hardware rather than treating either family as one model.

01Read checkpoint-specific license terms before commercial deployment or redistribution.
02Compare text-only and multimodal checkpoints separately.
03Account for dense versus MoE architecture in memory and serving plans.
04Use provider origin only as provenance metadata, not as a residency claim.

Models to evaluate

ModelArchitecture / sizeLicenseHardware classProvider
Qwen3 8BQwen / Alibaba8B · local general-purpose modelApache 2.0≤8B · consumer/local🇨🇳 China
Qwen3-32BQwen / Alibaba32B · denseApache 2.017–32B · high-memory workstation🇨🇳 China
Qwen3-235B-A22BQwen / Alibaba235B / 22B active · MoEApache 2.0Model-specific · large / specialized🇨🇳 China
Qwen3-VL-30B-A3B-InstructQwen / Alibaba30B-class sparse vision-language modelApache 2.017–32B · high-memory workstation🇨🇳 China
Llama 3.2 3B InstructMeta3B · compact text instructLlama 3.2 Community License≤8B · consumer/local🇺🇸 United States
Llama 3.2 11B Vision InstructMeta11B · vision-language instructLlama 3.2 Community License9–16B · workstation/local🇺🇸 United States
Llama 3.3 70B InstructMeta70B · text instructLlama 3.3 Community License33–80B · large workstation / multi-GPU🇺🇸 United States
Llama 4 ScoutMeta17B active / 16 expertsLlama 4 Community LicenseModel-specific · large / specialized🇺🇸 United States
Shortlist, not ranking: these candidates span different capability and hardware classes. Remove incompatible models first, then benchmark the remainder on the exact workload.

Decision criteria

Read checkpoint-specific license terms before commercial deployment or redistribution.

Compare text-only and multimodal checkpoints separately.

Account for dense versus MoE architecture in memory and serving plans.

Use provider origin only as provenance metadata, not as a residency claim.

Deployment reality

Validate the exact checkpoint, precision or quantization, runtime, context length and concurrency target. Weight memory alone does not capture KV cache, runtime workspaces, multimodal encoders or distributed-serving overhead.

OWM keeps license, provider origin and data residency separate. A provider-country label is provenance metadata; the deployer determines where inference and connected services run.

Primary model sources

Qwen3 8B — official model source ↗Qwen3-32B — official model source ↗Qwen3-235B-A22B — official model source ↗Qwen3-VL-30B-A3B-Instruct — official model source ↗Llama 3.2 3B Instruct — official model source ↗Llama 3.2 11B Vision Instruct — official model source ↗Llama 3.3 70B Instruct — official model source ↗Llama 4 Scout — official model source ↗OWM model-selection framework →