Llama 4 vs Qwen 3
Pricing, benchmarks, and use case comparison
Verdict
Our pick: Qwen 3Pick Llama 4 for record-length context and native multimodality in an open model; pick Qwen 3 for stronger text reasoning, switchable thinking, and a fully permissive Apache-2.0 license.
Specs comparison
| Llama 4 | Qwen 3 | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen Team) |
| Type | Open source | Open source |
| Context window | ✓Up to 10M tokens (Scout); ~1M tokens (Maverick) | 128K tokens (32K for 0.6B/1.7B/4B dense variants) |
| Input / 1M tokens | Free (self-host) | Free (self-host) |
| Output / 1M tokens | Free (self-host) | Free (self-host) |
| Release date | 2025-04 | 2025-04 |
Benchmarks
| Benchmark | Llama 4 | Qwen 3 |
|---|---|---|
| Scout context window | 10M tokens | - |
| Scout size | 17B active / 109B total (16 experts) | - |
| Maverick size | 17B active / 400B total (128 experts) | - |
| Qwen3-235B-A22B | - | 235B total / 22B active |
| Qwen3-30B-A3B | - | 30B total / 3B active |
Scores sourced from official provider release posts and independent benchmark aggregators.
Capability and benchmarks
These open MoE families optimize for different things. Qwen 3 is the stronger text reasoner and coder (reasoning 80, coding 78, math 80) and offers switchable thinking and non-thinking modes per request. Llama 4 is weaker on pure reasoning (reasoning 74, coding 72) but is natively multimodal via early fusion (multimodal 82 vs Qwen's 30). If your work is text-and-code reasoning, Qwen 3 leads; if it involves images, Llama 4 leads.
Context, licensing, and hardware
Llama 4 dominates context length: Scout reaches an industry-leading 10M tokens (Maverick ~1M), while Qwen 3 tops out at 128K (32K on the smallest dense variants). Licensing favors Qwen 3's fully permissive Apache 2.0 versus the Llama 4 Community License, which requires a separate license for organizations above 700M monthly active users. Both offer efficient MoE variants; Qwen's 30B-A3B activates just 3B parameters, and Llama 4 Scout fits a single H100 at int4.
Which to pick
- Pick Llama 4 for extremely long context, native image understanding, and single-GPU deployment via Scout.
- Pick Qwen 3 for stronger text reasoning and coding, per-request control over reasoning depth, broad multilingual support (119 languages), and a no-strings Apache 2.0 license.
Which should you choose?
Choose Llama 4 if...
- →You need extremely long context in an open model (Scout's 10M window)
- →Self-hosted or on-prem multimodal deployment
- →You want an efficient MoE that activates few parameters per token
- →Fine-tuning or full control over the model
Choose Qwen 3 if...
- →You need an open, self-hostable model with a permissive license
- →You want to toggle deep reasoning on or off per request
- →Multilingual applications
- →Efficient inference via MoE with few active parameters