Alibaba shipped Qwen 3 in October 2025 — frontier-quality, MoE, open-weights, with industry-leading multilingual performance. Meta's Llama 4 followed in late 2025. The two are now the dominant open-weights families in 2026. Here is the head-to-head that actually matters.
What changed in 2026
- Both went MoE. Qwen 3 235B-A22B and Llama 4 Maverick 400B-A17B both use mixture-of-experts. Quality scaled, inference cost stayed reasonable.
- Multilingual got serious. Qwen 3 trained heavily on CJK plus 100+ languages. Llama 4 expanded to 50+. The gap is meaningful for non-English deployments.
- Licensing diverges. Qwen 3 is Apache 2.0 (truly permissive). Llama 4 is community license (>700M MAU restriction).
Multilingual benchmarks
| Language |
Qwen 3 (235B-A22B) |
Llama 4 Maverick |
| English (MMLU) |
87.0 |
89.0 |
| Chinese (CMMLU) |
85.5 |
73.5 |
| Japanese (JMMLU) |
78.0 |
68.0 |
| Korean (KMMLU) |
73.5 |
64.0 |
| French (MMLU-FR) |
79.5 |
81.0 |
| German (MMLU-DE) |
78.0 |
80.5 |
| Arabic (MMLU-AR) |
71.0 |
69.5 |
For CJK markets, Qwen is decisive. For European languages, they're roughly even. For English-only, Llama 4 Maverick has the edge.
Coding and math
Llama 4 Maverick wins on English coding (HumanEval 86.5, SWE-bench Verified 41%) over Qwen 3 (HumanEval 82.0, SWE-bench Verified 36%). On math (AIME 2024), they're within 2 points. For agentic coding pipelines in English, Llama 4 is the safer pick.
Architecture differences
| Aspect |
Qwen 3 235B |
Llama 4 Maverick |
| Active params |
22B |
17B |
| Total params |
235B |
400B |
| Experts |
128 (8 active) |
16 (2 active) |
| Context |
128K (1M with rope scaling) |
1M (4M with extra training) |
| License |
Apache 2.0 |
Llama Community |
Qwen's many-fine-grained-expert design tends to specialize better; Llama 4's fewer-larger-expert design has cleaner inference economics at scale.
Fine-tuning ecosystem
Hugging Face has 18K+ Llama 4 fine-tunes vs 6K+ Qwen 3 fine-tunes (as of April 2026). Llama 4 leads in tooling maturity (Axolotl, TRL, Unsloth all first-class). Qwen 3 has Alibaba's own DashScope tuning, which is excellent if you're deploying on Aliyun, plus growing Hugging Face support.
Deployment cost
Both are competitive on Together / Fireworks at $0.50-0.90/M input. Self-hosted on 8xH100, Qwen 3 has slightly better throughput per active param due to its expert routing. For 5M+ tokens/day, self-hosting either is below $1/M tokens; below 5M, hosted is more economical.
When to pick which
Pick Qwen 3 when: you serve users in CJK languages, you want a truly permissive Apache 2.0 license, you operate in or sell into Asia where Aliyun integration matters, or you need an Apache-2.0 model for a downstream redistribution play.
Pick Llama 4 when: your workload is English-dominant, you need the strongest open-weights coding model, you're using AWS Bedrock heavily (Llama is more deeply integrated), or you need >1M context.
FAQ
Can I deploy Qwen 3 in the West?
Yes. Together, Fireworks, Hugging Face inference, and Replicate all host Qwen 3. For data sovereignty, self-hosting on AWS/GCP/Azure works fine — the weights are open.
What about Qwen 3 Coder vs Codestral?
Qwen 3 Coder is excellent on Python and JavaScript, competitive with Codestral 3. For multilingual codebases, Qwen 3 Coder leads.
Does Qwen 3 support thinking mode?
Yes — <think> tag mechanism similar to DeepSeek R1/R2 and o1. Quality on math and reasoning improves substantially with thinking enabled.
Where to go next
For related comparisons see Llama 4 vs Llama 3 in 2026, DeepSeek R2 review, and Local LLM setup guide for 2026.