Llama 4 vs DeepSeek V4 - trillion scale MoE meets long context open weight AI

It depends on the variant and your workload. DeepSeek V4 Pro costs $3.48 per million output tokens through the API, which is roughly 7x cheaper than comparable closed source models. However, Llama 4 Maverick activates only 17B parameters per token compared to DeepSeek V4 Pro's 49B, so self hosted inference on Llama 4 can be more cost effective if you already have GPU infrastructure. DeepSeek V4 Flash at under $1 per million output tokens is the cheapest option for API based workloads.

DeepSeek V4 Pro is the clear leader for coding tasks in this comparison. It scores 80.6% on SWE Bench Verified, placing it within 0.2 points of Claude Opus 4.6. Llama 4 Maverick is a strong general purpose model with 80.5% on MMLU Pro, but it does not match DeepSeek V4 Pro on specialized coding benchmarks. If your primary workload is automated code generation or agentic software engineering, DeepSeek V4 Pro is the better choice.

Yes, both model families are available as open weights for self hosted deployment. Llama 4 ships under the Llama 3.1 Community License, which allows commercial use with some conditions for very large scale deployments. DeepSeek V4 uses the MIT license, which has no usage restrictions at all. Both can be downloaded and run on your own infrastructure using standard serving frameworks like vLLM, TGI, or SGLang.

The MIT license on DeepSeek V4 is one of the most permissive open source licenses available. It allows unrestricted commercial use, modification, and redistribution with no reporting requirements. The Llama 3.1 Community License also permits commercial use but includes conditions around monthly active user thresholds and requires attribution. For most teams, both licenses work fine, but enterprises with strict legal requirements often prefer the simplicity of MIT.

Llama 4 has stronger demonstrated multimodal capabilities in this comparison. Maverick scores 73.4% on MMMU, which tests understanding of images, charts, diagrams, and visual content. DeepSeek V4 is primarily optimized for text and code tasks, with its standout benchmark being SWE Bench Verified at 80.6%. If your workflow involves processing visual content alongside text, Llama 4 Maverick is the better fit.

DeepSeek V4 Pro is significantly more demanding due to its 1.6 trillion total parameters and 49B active per token. Even with quantization, it typically requires a multi node setup with several hundred gigabytes of combined VRAM. Llama 4 Maverick at 400B total and 17B active is much more manageable and can run on a single high end server with 4 to 8 GPUs depending on quantization level. DeepSeek V4 Flash at 13B active is the lightest option and can run on smaller GPU configurations.

They serve different purposes. DeepSeek V4 Flash is optimized for cost efficient inference with 13B active parameters and sub dollar API pricing, making it great for high volume production workloads. Llama 4 Scout is built around its 10M token context window, which is 10x longer than Flash's 1M limit. Choose Flash when you need affordable throughput on standard length tasks, and choose Scout when your workload requires processing very long documents or maintaining extended conversation history.

The best choice in the Llama 4 vs DeepSeek V4 comparison depends on your primary use case. For coding and software engineering automation, DeepSeek V4 Pro's 80.6% SWE Bench score and MIT license make it the top pick. For long document processing, retrieval augmented generation over large corpora, or applications needing extended memory, Llama 4 Scout's 10M context window is unmatched. For general purpose enterprise AI with strong multimodal support, Llama 4 Maverick offers the best balance of quality and efficiency.