Skip to content
Free Tool Arena

Head-to-head · Open-weight LLMs

Llama 3.3 vs Qwen 3.5

Llama 3.3 70B vs Qwen 3.5 72B compared: coding benchmarks, license, multilingual, long context, and which open-weight model to self-host.

Updated May 2026 · 7 min read
100% in-browserNo downloadsNo sign-upMalware-freeHow we keep this safe →

Llama 3.3 70B (Meta) and Qwen 3.5 72B (Alibaba) are the two most-used English-friendly open-weight models in the 70B class. Both are free to self-host, both have permissive-ish licenses, both run on a Hyperspace pod or rented cloud GPU. The key difference: Qwen 3.5 leads on most coding and reasoning benchmarks; Llama 3.3 has the larger ecosystem and the longer track record in production.

Advertisement

Option 1

Llama 3.3 70B

Meta's 70B flagship — broadest ecosystem.

Best for

Production deployments, broad community + tools support, multi-language work.

Pros

  • Llama community license — permissive for most uses.
  • Largest ecosystem in 2026 (vLLM, llama.cpp, every framework).
  • Battle-tested in production.
  • Strong multi-language support.
  • 128k context window.

Cons

  • Behind Qwen 3.5 on most code + reasoning benchmarks.
  • No native long-context (128k vs 128k+ on Qwen).
  • Slightly slower inference than Qwen at similar size.

Option 2

Qwen 3.5 72B

Alibaba's 72B flagship — top open-weight on coding.

Best for

Coding-heavy self-host, anyone who needs the best open-weight quality below 100B params.

Pros

  • Top SWE-bench Verified among open-weight 70B-class.
  • Strong reasoning + math.
  • 128k native context.
  • Permissive license (Apache 2.0).
  • Excellent in Chinese AND English.

Cons

  • Smaller English-language community than Llama.
  • Some Western tooling has rougher Qwen integration.
  • Less battle-tested in non-Chinese production deployments.

The verdict

Pick Qwen 3.5 if coding quality is your priority and you want the best open-weight 70B-class model. Pick Llama 3.3 for production stability, ecosystem maturity, and broadest tooling. Both run identically on Ollama, vLLM, and Hyperspace pods — switching is a one-line change.

Run the numbers yourself

Plug your own inputs into the free tools below — no signup, works in your browser, nothing sent to a server.

Free toolFrontier AI Model TrackerLive tracker of every frontier AI model: Claude 4.x, GPT-5, Gemini 3 Pro, DeepSeek R1/V3.2, Kimi K2, Grok 4, Llama 4, Qwen 3.5, Mistral Large 3.Open tool →Free toolLocal vs API Break-even CalculatorHow many months until self-hosting pays back vs using API? Compare Mac Studio, RTX 4090/5090, and Hyperspace pods at your usage level.Open tool →

Frequently asked questions

Which is the best open-weight LLM in 2026?

For coding, Qwen 3.5 72B leads at the 70B class. DeepSeek V3.2 (671B MoE) is the absolute open-weight quality leader if you can host it.

Can I self-host either on a single GPU?

Both fit on a single H100 80GB at FP8 or Q4 quantization. For RTX 4090 (24GB) you need offloading or a multi-machine pod — see the home-AI-cluster guide.

Are these models really free?

Free to download and run. Llama 3.3 has acceptable-use license restrictions; Qwen 3.5 is Apache 2.0 (more permissive). Read the license if you're shipping a commercial product.

More head-to-head comparisons