What this is
Three Mixture-of-Experts (MoE) flagships dominate open-weight AI in 2026: DeepSeek V4 (Chinese, MIT, strongest coder), Qwen 3.5 (Chinese / Alibaba, strongest reasoner), Llama 4 (Meta, 10M context, custom license). All three are competitive with the closed-frontier on most tasks. This page is a 2026-05-15 head-to-head focused on the model-choice decision.
Side-by-Side Matrix
| Dimension | DeepSeek V4-Pro | Qwen 3.5 | Llama 4 Maverick |
|---|---|---|---|
| Architecture | Sparse MoE | Sparse MoE | Sparse MoE |
| Total params | 1.6T | 397B | 400B |
| Active params | 49B | 17B | 17B |
| SWE-Bench Verified | 83.7% (leader) | ~75% | ~70% |
| HumanEval | 90% | ~85% | ~82% |
| GPQA Diamond | ~85% | 88.4% (leader) | ~80% |
| MMLU-Pro | ~82% | ~84% | 80.5% |
| Context window | 1M | ~256K | 1M (Maverick) / 10M (Scout) |
| License | MIT | Apache 2.0 (Qwen-specific) | Meta custom (700M MAU clause) |
| Best at | Coding + reasoning | Scientific reasoning | Long context + broad knowledge |
| Inference cost (open hosters, $/M input) | ~$0.14 | ~$0.20 | ~$0.20 |
Best-Use Scenarios
| Use case | Pick |
|---|---|
| Coding agents / SWE-Bench-style tasks | DeepSeek V4 Pro |
| Scientific reasoning, research workloads | Qwen 3.5 |
| Long-context (millions of tokens) workloads | Llama 4 Scout (10M context) |
| Commercial deployment under 700M MAU | Llama 4 (license permits) |
| Commercial deployment regardless of MAU | DeepSeek V4 (MIT) or Qwen 3.5 (Apache 2.0) |
| Cheapest competitive open model | DeepSeek V4 (~$0.14/M input) |
| Strict export-control / non-Chinese-origin requirement | Llama 4 |
| Multilingual workloads (esp. Chinese, Asian languages) | Qwen 3.5 |
Six Things the Data Tells You
- DeepSeek V4 is the strongest open coder. 83.7% SWE-Bench Verified closes in on Claude Opus 4.6 and beats most proprietary models on the same benchmark.
- Qwen 3.5 leads scientific reasoning. 88.4% GPQA Diamond is the best-in-class for open weights and competitive with all but the top frontier models.
- Llama 4 Scout's 10M context window is unmatched. The longest open-weight context window in production.
- License differences matter for commercial deployment. DeepSeek MIT and Qwen Apache are unrestricted; Llama 4 has the Meta custom license with the 700M MAU clause that excludes hyperscale consumer products.
- Active-parameter efficiency converged. All three target 17-49B active parameters per token for inference efficiency.
- Open hosters serve all three at $0.14-$0.20/M input. Open-weight pricing is now significantly below proprietary commodity pricing.
How to Pick
Coding-heavy workloads: DeepSeek V4 Pro. Scientific reasoning: Qwen 3.5. Long-context document workloads: Llama 4 Scout. Consumer-product deployment above 700M MAU: avoid Llama 4 for licensing reasons. Strict origin requirement: Llama 4.
Methodology
Benchmark and architecture data combine Codersera's open-source LLM landscape 2026, Spheron's DeepSeek vs Llama 4 vs Qwen 3 production comparison, AkitaOnRails LLM coding benchmark May 2026, and AI Magicx open-source AI takeover analysis.