Llama 4 vs. Qwen 3.5 vs. DeepSeek V4: Which Open-Source Model for Local Enterprise AI?

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2026 is the year of open-source LLMs. Almost every flagship model is a Mixture of Experts (MoE): massive parameter counts but efficient inference because only a fraction is activated per token. For enterprises, this means: powerful AI on your own hardware — without OpenAI API dependency.

But which model? Llama 4 from Meta, Qwen 3.5 from Alibaba, or DeepSeek V4? This comparison shows the differences — focused on local enterprise deployment.

Table of Contents

• Models at a glance
• Benchmarks: Who leads where?
• Hardware requirements
• Context windows and RAG
• Licensing
• Recommendation by use case

Models at a glance

All five are MoE models. This means: the total parameter count is misleading — what matters are the active parameters per token and the hardware required.

Benchmarks: Who leads where?

DeepSeek V4 Pro dominates code and reasoning — by a wide margin. But it's also the largest model (49B active parameters) and needs correspondingly more hardware.

Qwen 3.5 leads in GPQA Diamond (scientific reasoning) and MMLU (general knowledge). With 200+ languages, it's the best choice for multilingual applications.

Llama 4 Maverick trails in benchmarks — but has the second-largest context window and is well integrated into Western toolchains through Meta.

Hardware requirements

Important with mixture of experts: VRAM demand follows the total parameters, not the active ones. Only the compute per token is sparse - all experts have to sit in memory. Rule of thumb for the weights: quantized (Q4) roughly 0.5 GB per billion parameters, in FP16 about four times that. Context and KV cache add on top depending on context length and concurrency.

For most enterprise use cases you do not need this class at all. A dense model with 27B to 70B parameters covers chat, RAG, summaries and code generation and fits on a single card when quantized - Qwen3 32B takes roughly 20 GB in Q4, Llama 3.3 70B roughly 48 GB. The MoE giants above pay off once you hit the quality ceiling of the dense models and can provide the hardware.

Context windows and RAG

For RAG pipelines (Retrieval Augmented Generation), the context window is decisive:

• Llama 4 Scout: 10M tokens — theoretically massive, but limited by hardware in practice. 10M tokens require enormous memory for the KV cache.
• DeepSeek V4: 1M tokens — practical for large document collections.
• Qwen 3.5: 256K tokens — more than sufficient for most RAG pipelines. More realistic than 10M you rarely need.

Recommendation: For enterprise RAG on internal documents, Qwen 3.5 with 256K context is the most pragmatic compromise. Those who need to process single very large documents (contracts, technical manuals) benefit from DeepSeek's 1M.

Licensing

DeepSeek V4 under MIT is the most permissive option — no restrictions, no notification requirements, no MAU limits. For enterprises needing legal clarity, this is a strong argument.

Qwen 3.5 under Apache 2.0 is also straightforward — patent grant included.

Llama 4 uses the Llama Community License — not a true open-source license in the OSI sense. Commercial use is permitted, but with restrictions:

• 700M MAU limit: Above 700 million monthly active users, a separate Meta license is required
• EU restriction for multimodal: The vision/multimodal capabilities of Llama 4 are not licensed for companies headquartered in the EU (https://www.llama.com/llama4/license/). This affects image analysis, OCR and multimodal RAG pipelines.
• Attribution required: "Built with Llama" must be displayed on derivatives
• Acceptable Use Policy: Meta can restrict usage — e.g., for legal or medical advice
• Not OSI-standard: Meta retains rights and can change terms

For EU enterprises wanting to deploy multimodal AI, Llama 4 is a non-starter. For text-only inference it's usable, but Qwen 3.5 (Apache 2.0) or DeepSeek V4 (MIT) offer more legal certainty.

Recommendation by use case

For the majority of enterprise applications, we recommend Qwen 3.5 or DeepSeek V4 Flash. Both run on a single RTX 6000, both have open licenses, both deliver excellent results in German and English.

Which model fits your enterprise? We advise on model selection and deploy the model on your AI Cube or GPU server — pre-configured with Open WebUI or your preferred chat interface. Schedule a consultation | Configure AI Cube

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