DeepSeek V4 vs Llama 4: The Two Biggest Open-Source AI Families Compared (2026)
Open-source AI in 2026 is a two-horse race. On one side sits DeepSeek, the Chinese lab that shook the industry with its V4 family. On the other, Meta’s Llama 4, the latest generation of the most widely deployed open-weight model series in the West. Both families lean on Mixture-of-Experts (MoE) architectures, both push past the million-token context mark, and both are free to download.
But the details diverge in ways that matter for developers, startups, and enterprises choosing a foundation model. This guide breaks down architecture, benchmarks, licensing, and ecosystem support so you can pick the right family for your workload.
For deeper dives into individual models, see our DeepSeek V4 Pro complete guide, Llama 4 complete guide, and Llama 4 Scout vs Maverick comparison.
The two families at a glance
DeepSeek, based in Hangzhou, released V4 as a successor to the wildly popular V3 line. The family includes V4 Pro (the flagship) and V4 Flash (the lightweight variant). Meta countered with Llama 4, shipping Maverick as the high-end model and Scout as the efficient alternative.
All four models use MoE, meaning only a fraction of total parameters activate per token. This keeps inference costs lower than dense models of comparable quality.
Architecture comparison
| Spec | DeepSeek V4 Pro | DeepSeek V4 Flash | Llama 4 Maverick | Llama 4 Scout |
|---|---|---|---|---|
| Total parameters | 1.6T | 284B | 400B | 109B |
| Active parameters | 49B | 13B | 17B | 17B |
| Architecture | MoE | MoE | MoE | MoE |
| Max context | 1M tokens | 1M tokens | 1M tokens | 10M tokens |
| Training data cutoff | Early 2026 | Early 2026 | Early 2026 | Early 2026 |
| Origin | DeepSeek (China) | DeepSeek (China) | Meta (US) | Meta (US) |
A few things stand out. V4 Pro is by far the largest model in total parameter count at 1.6 trillion, but it activates 49B per forward pass. Llama 4 Maverick is smaller overall (400B) and activates just 17B, making it cheaper to serve per token. V4 Flash and Scout occupy the lightweight tier, with Flash activating 13B and Scout activating 17B.
Scout’s 10M token context window is the standout spec in the entire table. No other open-weight model in either family comes close to that length, making Scout the go-to choice for massive document ingestion and repository-scale code analysis.
Benchmark comparison
| Benchmark | DeepSeek V4 Pro | Llama 4 Maverick | DeepSeek V4 Flash | Llama 4 Scout |
|---|---|---|---|---|
| MMLU-Pro | 82.1 | 76.3 | 73.5 | 70.8 |
| HumanEval (pass@1) | 91.4 | 78.6 | 82.1 | 74.2 |
| MBPP+ | 88.7 | 76.1 | 80.3 | 72.5 |
| MATH-500 | 85.2 | 79.4 | 77.8 | 74.1 |
| GPQA Diamond | 68.9 | 61.2 | 58.4 | 55.7 |
| Arena Elo (approx.) | 1310 | 1250 | 1220 | 1190 |
V4 Pro pulls ahead significantly on coding benchmarks. The gap on HumanEval (91.4 vs 78.6) and MBPP+ (88.7 vs 76.1) is substantial, reflecting DeepSeek’s continued investment in code-focused training. V4 Pro also leads on reasoning tasks like GPQA Diamond and MATH-500, though the margins are narrower.
In the lightweight tier, V4 Flash outperforms Scout across the board, but Scout compensates with that enormous 10M context window. If your workload is context-heavy rather than reasoning-heavy, Scout may still be the better pick.
Context window
Both V4 Pro and Llama 4 Maverick support 1M token contexts. In practice, this is enough for most enterprise use cases: long documents, multi-file code review, and extended conversations.
Scout breaks the mold with 10M tokens. This opens up workflows that were previously impossible with open-weight models: ingesting entire codebases, processing book-length legal documents, or running multi-hour agentic sessions without losing earlier context. The tradeoff is that Scout’s reasoning ceiling is lower than Maverick’s or V4 Pro’s.
V4 Flash also supports 1M tokens, giving DeepSeek a consistent context story across its lineup.
Licensing
This is where the families diverge sharply.
DeepSeek V4: MIT License. The entire V4 family ships under MIT. You can use, modify, and redistribute the weights for any purpose, including commercial products, with no restrictions on company size or user count.
Llama 4: Meta’s custom license. Llama 4 uses a bespoke license that is more permissive than Llama 2’s original terms but still more restrictive than MIT. Key constraints include:
- Companies with over 700 million monthly active users must request a separate license from Meta.
- You must include Meta’s attribution notice in derivative works.
- The license includes acceptable use policies that restrict certain applications.
For most startups and mid-size companies, the Llama 4 license is fine in practice. But if you want maximum legal simplicity or you are building a product that redistributes weights at scale, DeepSeek’s MIT license removes friction entirely.
Ecosystem and hardware support
Llama 4 benefits from Meta’s partnerships across the hardware landscape. Day-one support exists for NVIDIA GPUs, AMD Instinct, Intel Gaudi, and major cloud providers (AWS, Azure, GCP). Quantized variants are widely available through communities like TheBloke and Unsloth. Llama models also enjoy first-class integration in frameworks like vLLM, TGI, and llama.cpp.
DeepSeek V4 has strong support on NVIDIA hardware and has notably expanded to Huawei Ascend NPUs, making it one of the few top-tier models optimized for non-Western silicon. Cloud availability is growing, with DeepSeek’s own API platform offering competitive pricing. Community quantizations exist but lag slightly behind Llama’s ecosystem breadth.
If you are deploying on Huawei infrastructure or in regions where Ascend hardware is prevalent, DeepSeek is the clear choice. For maximum hardware flexibility across Western cloud providers, Llama 4 has the edge.
Which family should you pick?
Choose DeepSeek V4 if:
- Coding performance is your top priority.
- You want the most permissive license (MIT).
- You are deploying on Huawei Ascend hardware.
- You need the strongest overall reasoning at the flagship tier.
Choose Llama 4 if:
- You need the broadest hardware and cloud provider support.
- Scout’s 10M context window fits your use case.
- You prefer Meta’s established ecosystem and community tooling.
- You want the lowest possible inference cost (Maverick’s 17B active params).
Final thoughts
The open-source AI landscape has never been this competitive. DeepSeek V4 and Llama 4 are both production-ready families that rival proprietary models on many tasks. The choice between them comes down to your specific constraints: licensing philosophy, hardware stack, context requirements, and whether coding benchmarks or ecosystem breadth matters more to your team.
Both families will continue to evolve rapidly. Keeping an eye on community benchmarks and fine-tuned variants is worth the effort as the year progresses.
FAQ
Is DeepSeek V4 Pro better than Llama 4 Maverick for coding?
Yes. V4 Pro scores significantly higher on HumanEval (91.4 vs 78.6) and MBPP+ (88.7 vs 76.1). If code generation and code reasoning are central to your workflow, V4 Pro is the stronger model. See our V4 Pro guide for setup instructions.
Can I use Llama 4 commercially?
Yes, with conditions. Meta’s custom license permits commercial use for most companies. However, organizations with more than 700 million monthly active users need a separate agreement. Check our Llama 4 guide for full licensing details.
Which lightweight model is better, V4 Flash or Llama 4 Scout?
It depends on your priority. V4 Flash wins on raw benchmark scores across coding, math, and reasoning tasks. Scout wins on context length (10M vs 1M tokens). If you need to process very long inputs, Scout is unmatched. For shorter-context tasks where quality matters most, V4 Flash is the better option. We compare Scout and Maverick in more detail in our Scout vs Maverick breakdown.