The two great open-weight ecosystems. Qwen3.5 is a year fresher and it shows: better efficiency, stronger benchmarks, cleaner license (Apache 2.0 vs Llama's community terms). Llama 4 retains the deepest tooling integration, Scout's unmatched single-GPU long context, and US origin. Greenfield projects mostly choose Qwen now; Llama-committed stacks and US-vendor-required buyers stay put.
Llama 4 vs Qwen3.5 at a glance
| Llama 4 | Qwen3.5 | |
|---|---|---|
| Vendor | Meta (United States) | Alibaba (Qwen team) (China) |
| Open weights | Yes — downloadable | Yes — downloadable |
| License | Llama 4 Community License — free for most commercial use; requires a separate license above 700M MAU and carries branding/acceptable-use conditions | Apache 2.0 — permissive, patent-granting, free for commercial use |
| Parameters | Scout 109B / Maverick 400B (17B (both) active) | 397B (17B active) |
| Context window | Maverick 1M tokens; Scout up to 10M (claimed) | 262K tokens (native) |
| Modalities | text, image | text |
| Pricing | Varies by hosting provider (open weights) | Varies by hosting provider (open weights) |
| Released | 2025-04-05 | 2026-02-16 |
Specs and pricing verified July 2026.
About Llama 4
Llama 4 is Meta's mixture-of-experts generation: Scout (109B total) and Maverick (400B total), both activating 17B parameters per token, both natively multimodal on text and images. Maverick still posts the highest MMLU among open models in mid-2026 roundups (85.5%), and Scout's headline 10M-token context claim remains the largest advertised window in open weights, with ~1M tokens the practically-validated range.
Llama's superpower is its ecosystem: every inference framework, fine-tuning library, and cloud platform supports it on day one, and the Llama 4 Community License — while not OSI-open (700M-MAU clause, branding requirements) — is free for virtually every company that isn't a hyperscaler. Amid the 2026 wave of Chinese frontier releases, Llama 4 remains the flagship American open-weight family and the safe institutional default.
Full specs, benchmarks, and hardware guidance: the Llama 4 page.
About Qwen3.5
Qwen3.5 is Alibaba's February 2026 generation and the efficiency benchmark of the open-weight field: the 397B-A17B flagship activates just 17B parameters per token yet beats Alibaba's own trillion-parameter Qwen3-Max — at 8.6× the throughput at 32K context and 19× at 256K. All open-weight variants ship under Apache 2.0, the most enterprise-friendly license in common use.
The release is a full family, not one model: from the 397B flagship down to a 9B variant with a native 256K context window that runs on consumer hardware. That spread — one architecture, Apache 2.0 everywhere, sizes from phone to datacenter — is why Qwen has become the default base-model family for fine-tuners and the most-adopted open-weight line by download count.
Full specs, benchmarks, and hardware guidance: the Qwen3.5 page.
Choosing between them
Choose Llama 4 for:
- The broadest tooling and ecosystem support in open weights
- Single-GPU long-context deployment (Scout on one H100)
- Organizations that prefer a US-based vendor for governance reasons
Choose Qwen3.5 for:
- Enterprise deployments that require Apache 2.0 licensing
- Efficiency-critical serving (17B active parameters, extreme throughput)
- Fine-tuning projects — the broadest size ladder in open weights
Frequently asked questions
Is Llama 4 better than Qwen3.5?
The two great open-weight ecosystems. Qwen3.5 is a year fresher and it shows: better efficiency, stronger benchmarks, cleaner license (Apache 2.0 vs Llama's community terms). Llama 4 retains the deepest tooling integration, Scout's unmatched single-GPU long context, and US origin. Greenfield projects mostly choose Qwen now; Llama-committed stacks and US-vendor-required buyers stay put.
Which is cheaper: Llama 4 or Qwen3.5?
Llama 4: Varies by hosting provider (open weights). Qwen3.5: Varies by hosting provider (open weights). Both are open-weight, so self-hosting costs depend on your hardware and utilization.
Can I self-host Llama 4 and Qwen3.5?
Llama 4: yes — weights are published under Llama Community. Qwen3.5: yes — weights are published under Apache 2.0.