As of May 2026, the open-source large-model battlefield has three families in clear technical differentiation: DeepSeek V3.2 Speciale (MoE, 685B total params, MIT license), Meta Llama 4 Scout/Maverick (MoE, 109B/400B total params, Llama 4 Community license), and Alibaba Qwen3-32B (Dense, 32.8B, Apache 2.0 license). A recent benchmark report from Spheron provides detailed cost and performance data for this competition.
Hardware thresholds differ significantly. The minimum deployment costs of the three models are starkly different. Both Qwen3-32B and Llama 4 Scout can run on a single H100 80GB, with Spot pricing around $0.87/hour per million tokens. DeepSeek V3.2 Speciale, using MoE and activating 37B parameters, needs at least 8×H100, with inference cost jumping to $13.89/hour per million tokens — 16× the former. This means if your business doesn't need DeepSeek's math-reasoning specialty, choosing the first two saves significant compute cost.
Benchmarks each have their wins and losses. In MMLU general knowledge, DeepSeek V3.2 Speciale leads with 88.5%, Llama 4 Maverick is at 85.5%, Qwen3-32B at 83.6%. But in code ability on HumanEval, Qwen3-32B instead takes the lead at 88.0%, surpassing DeepSeek V3.2 Speciale's 82.6% (note: the latter uses the HumanEval-Mul multilingual version, so they're not directly comparable). Llama 4 Scout's official evaluation data is unpublished, results are uncertain. On context window, Llama 4 Scout's 10M ultra-long context is the biggest highlight, suitable for RAG and long-document Q&A; DeepSeek V3.2 and Qwen3-32B are at 128K and 131K respectively.
Efficiency revolution: MoE vs Dense route debate. The three routes' efficiency philosophies are radically different. DeepSeek V3.2 takes a high-parameter + sparse-activation route, with 685B total but only 37B activated per pass, trading strong reasoning for high memory usage; Llama 4 Scout is a typical mid-parameter + ultra-sparse case, 109B total with only 17B activated, with 10M context relying on YaRN length extrapolation; Qwen3-32B holds the Dense line, simple and direct, a 33GB INT4 quantized file fits in one H100, winning on "good enough is enough."
Licenses determine where you can use them. Qwen3's Apache 2.0 is most friendly, with no user-count restrictions; Llama 4 Community License requires Meta authorization for monthly active users above 700M; DeepSeek V3.2's MIT license is similarly permissive, but less explicit than Apache 2.0 for deep customization scenarios.
Selection advice. A simple framework: code assistant pick Qwen3-32B for the best cost-performance; long-document RAG pick Llama 4 Scout for its irreplaceable 10M context; math/complex reasoning pick DeepSeek V3.2 Speciale, but first confirm the budget can cover 8×H100 deployment costs. There's no free lunch, but the open-source ecosystem gives developers unprecedented choice.