DeepSeek V4 vs Llama 4: Which Open Frontier Model Should You Run?
DeepSeek V4 just topped the open leaderboard. Should you switch from Llama 4 405B? Side-by-side on benchmarks, license, hardware, and ecosystem.
On the leaderboards, it's not close: DeepSeek V4 685B beats Llama 4 405B on MMLU (89.4 vs 87.1), HumanEval (92.1 vs 84.5), and chatbot arena Elo (1342 vs 1289). But "is it better" and "should you run it" are different questions. Here's the honest comparison.
Where DeepSeek V4 wins **Reasoning and math**: GSM8K 95.2 vs 93.0 doesn't sound like a lot, but on harder benchmarks (MATH, ARC-Challenge) the gap widens. If your workload is math-heavy or chain-of-thought-heavy, DeepSeek wins.
Code generation: 92.1 vs 84.5 on HumanEval is a real gap. For coding agents, IDE assistants, or anything where the LLM writes code, DeepSeek V4 (or its Coder V3 sibling) is the choice.
License: DeepSeek's license has no MAU cap. Llama's MAU cap of 700M doesn't matter to most companies, but if you're at scale or have an exit in your sights, it can matter to acquirers.
Where Llama 4 wins **Ecosystem**: Llama has more fine-tunes, more quantizations, more LoRAs, more docs, more tutorials. If anything goes wrong with Llama, the answer is on Stack Overflow within 24 hours. With DeepSeek, you may be the first person to hit a given bug.
Hardware match: Llama 4 comes in 8B, 70B, and 405B. DeepSeek V4 only comes in 67B and 685B (the 236B is a discontinued size). Llama 8B fits a 16GB GPU; DeepSeek 67B doesn't. If you don't have an H100, this is the deciding factor.
Vision: Llama 4 90B+ supports vision input. DeepSeek V4 is text-only. For multi-modal pipelines, you'd be running DeepSeek alongside a separate vision model.
Hardware reality check - DeepSeek V4 685B (FP16): needs 8x H100 80GB at minimum. Realistically, $250K of hardware or $40+/hour cloud. - DeepSeek V4 67B (FP16): 2x A100 80GB or 1x H100. ~$8/hour cloud. - Llama 4 405B (FP16): 8x H100. Same as DeepSeek 685B in capex. - Llama 4 70B (FP16): 2x A100 80GB. Same as DeepSeek 67B. - Llama 4 8B (FP16): 1x 16GB GPU. ~$0.50/hour cloud, or your laptop.
For most teams, the comparison is really DeepSeek 67B vs Llama 70B. At that tier, DeepSeek wins on benchmarks but loses on ecosystem. Pick based on what your team will actually maintain.
What about cost via API? If you're not self-hosting, see the [LLM Pricing Calculator](https://llm-pricing-7mc.pages.dev) for current per-token costs across hosted DeepSeek and Llama providers (Together, Groq, Fireworks, etc.). At time of writing DeepSeek V4 67B and Llama 4 70B price within ~10% of each other on most hosts.
Our recommendation **Pick DeepSeek V4 67B if**: You're benchmark-driven, you have GPU access, and you're comfortable being on the bleeding edge.
Pick Llama 4 70B if: You want predictability, your team is small, you need vision, or you're going to fine-tune extensively.
Pick Llama 4 8B if: You want the model to fit on your laptop, you're prototyping, or you need an embedded LLM.