DeepSeek V4 vs Llama 4

Compare DeepSeek V4 and Llama 4 — the two largest open-weight model families of 2025-2026. Architecture, context window, licensing, real-world performance, and deployment trade-offs.

Overview

DeepSeek V4 and Llama 4 represent the two highest-profile attempts at frontier-scale open-weight models in 2025-2026. They were released roughly a year apart — Llama 4 in April 2025, DeepSeek V4 in April 2026 — and the year between them produced substantially different reception. Llama 4's launch was widely seen as underwhelming relative to expectations, and Meta has paused the Llama 4 Behemoth release. DeepSeek V4 launched at the top of the open-weight leaderboards and is broadly viewed as a meaningful step toward closed-model parity.

Architecturally, the two families share the mixture-of-experts pattern but make different design choices. DeepSeek V4 uses a relatively narrow MoE topology (DSA sparse attention, 49B active out of 1.6T), while Llama 4 uses fine-grained expert routing (17B active out of 109B-400B). Both support long context, but DeepSeek's 1M is matched by Llama 4 Scout's 10M — the largest context window of any open-weight model. The licensing posture also differs significantly: DeepSeek's MIT-style license is more permissive than Llama 4's Community License, which includes usage caps and attribution requirements.

Feature Comparison

Feature	DeepSeek V4	Llama 4
Total Parameters (Flagship)	1.6T (V4 Pro)	400B (Llama 4 Maverick)
Active Parameters	49B (Pro) / 13B (Flash)	17B (both Scout and Maverick)
Context Window	1M tokens	10M (Scout) / 1M (Maverick)
License	DeepSeek License (MIT-style)	Llama Community License
Commercial Restrictions	None significant	Usage caps, attribution requirements
Thinking Mode
Native Multimodal
Composite Intelligence Score	87 (BenchLM, leader)	~78 (Maverick)
Behemoth Status	N/A	Paused — not publicly released
Hugging Face Path	deepseek-ai/DeepSeek-V4-Pro	meta-llama/Llama-4-Maverick

Strengths

DeepSeek V4

Currently leads the BenchLM composite intelligence index at 87, ahead of all other open-weight models
DeepSeek Sparse Attention (DSA) makes long-context inference dramatically more efficient than naive attention
Unified thinking mode in a single checkpoint — no separate reasoning model deployment needed
DeepSeek License is permissive enough for nearly all commercial use cases without attribution overhead
Strong coding benchmarks including SWE-Bench Verified ~73% (V3.2 lineage continues in V4)

Llama 4

Llama 4 Scout's 10 million token context window is the largest in any publicly released open-weight model
Native multimodal support across the family — image input is built into the base architecture
Lower active parameter count (17B) gives Llama 4 better inference economics for high-throughput serving
Mature deployment ecosystem — llama.cpp, vLLM, TensorRT-LLM, and Ollama all have first-class Llama 4 support
Meta's brand reputation and ongoing model investment provide long-term ecosystem confidence

Which Should You Choose?

You're choosing the absolute best open-weight model for reasoning qualityDeepSeek V4

DeepSeek V4 leads the open-weight intelligence leaderboard at the time of release. Llama 4's reception was widely viewed as underwhelming relative to expectations, and Meta paused the Behemoth flagship.

You need ultra-long context (>1M tokens) for very large document or codebase analysisLlama 4

Llama 4 Scout's 10M token context is unique among publicly released models. While effective context is shorter than the advertised limit on any model, Scout's headroom is unmatched.

Your application requires native multimodal input (images alongside text)Llama 4

Llama 4 has multimodal capability built into the base architecture. DeepSeek V4 is text-only — multimodal use cases need a separate vision-language model alongside it.

Licensing simplicity for commercial deployment is a priorityDeepSeek V4

DeepSeek License is closer to MIT — minimal commercial restrictions. Llama Community License includes usage caps and attribution requirements that complicate some commercial use cases.

Verdict

DeepSeek V4 is the more capable model in nearly every dimension that production teams care about: reasoning quality, coding performance, licensing permissiveness, and operational simplicity via the unified thinking mode. Llama 4 retains advantages in two specific areas — multimodal capability (native image input) and ultra-long context (Scout's 10M tokens) — but those advantages don't compensate for DeepSeek V4's lead on the core reasoning capability axis.

For most teams choosing between these two flagships in 2026, DeepSeek V4 is the recommended default. Llama 4 remains relevant for use cases that specifically need its multimodal or 10M-context advantages, and for teams already deeply invested in the Meta ecosystem. But the year between the two releases significantly shifted the open-weight quality leaderboard, and DeepSeek V4 captured that lead.

How Ertas Fits In

Both models are at the upper end of practical fine-tuning. DeepSeek V4 Flash QLoRA in Ertas Studio needs approximately 280-340GB of total VRAM across a multi-GPU server (8x A100 80GB or equivalent). Llama 4 Maverick QLoRA needs approximately 200-260GB given the lower active parameter count. V4 Pro and Llama 4 Behemoth are both impractical for most teams to fine-tune directly.

For teams without multi-GPU server access, Ertas Studio's recommended pattern is teacher-student distillation: use either V4 or Llama 4 to generate synthetic training data, then fine-tune a smaller base model (Qwen 32B, Llama 70B, or DeepSeek-R1 distilled variants) on that data. This produces a domain-specialized model at single-GPU deployment cost. Llama 4's mature deployment ecosystem makes this distillation pattern particularly smooth — the resulting fine-tuned model exports to GGUF and deploys via Ollama or llama.cpp without any additional integration work.