DeepSeek V4 outclasses Llama 4 on raw capability — the 2026 release cycle left Maverick behind on reasoning and coding. Llama 4 still wins on three practical axes: ecosystem depth (everything supports Llama first), deployability (Scout fits one H100), and vendor origin for organizations that require a US supplier. That last axis, not benchmarks, is why Llama remains the institutional default.
Llama 4 vs DeepSeek at a glance
| Llama 4 | DeepSeek V4 | |
|---|---|---|
| Vendor | Meta (United States) | DeepSeek (China) |
| Open weights | Yes — downloadable | Yes — downloadable |
| License | Llama 4 Community License — free for most commercial use; requires a separate license above 700M MAU and carries branding/acceptable-use conditions | MIT — weights free to download, self-host, fine-tune, and use commercially |
| Parameters | Scout 109B / Maverick 400B (17B (both) active) | 1.6T (49B active) |
| Context window | Maverick 1M tokens; Scout up to 10M (claimed) | 1M tokens |
| Modalities | text, image | text |
| Pricing | Varies by hosting provider (open weights) | $1.74 in / $3.48 out per 1M tokens |
| Released | 2025-04-05 | 2026-04-24 |
Specs and pricing verified July 2026.
About Llama 4
Llama 4 is Meta's mixture-of-experts generation: Scout (109B total) and Maverick (400B total), both activating 17B parameters per token, both natively multimodal on text and images. Maverick still posts the highest MMLU among open models in mid-2026 roundups (85.5%), and Scout's headline 10M-token context claim remains the largest advertised window in open weights, with ~1M tokens the practically-validated range.
Llama's superpower is its ecosystem: every inference framework, fine-tuning library, and cloud platform supports it on day one, and the Llama 4 Community License — while not OSI-open (700M-MAU clause, branding requirements) — is free for virtually every company that isn't a hyperscaler. Amid the 2026 wave of Chinese frontier releases, Llama 4 remains the flagship American open-weight family and the safe institutional default.
Full specs, benchmarks, and hardware guidance: the Llama 4 page.
About DeepSeek V4
DeepSeek V4 Pro is the flagship of DeepSeek's V4 family, released on April 24, 2026, and the strongest argument yet that open-weight models compete at the frontier. It is a 1.6-trillion-parameter mixture-of-experts model that activates just 49B parameters per token, pairs a 1M-token context window with up to 384K tokens of output, and ships under a plain MIT license — no usage thresholds, no acceptable-use gate.
The headline engineering story is the attention stack. V4 combines Compressed Sparse Attention (CSA), which compresses KV entries 4× along the sequence with softmax-gated pooling, with Heavily Compressed Attention (HCA) at 128× compression. The practical result: at 1M-token context, V4 Pro needs roughly 27% of the single-token inference FLOPs and 10% of the KV cache of DeepSeek-V3.2. Long context stopped being a luxury and became the default across DeepSeek's services.
Full specs, benchmarks, and hardware guidance: the DeepSeek V4 page.
Choosing between them
Choose Llama 4 for:
- The broadest tooling and ecosystem support in open weights
- Single-GPU long-context deployment (Scout on one H100)
- Organizations that prefer a US-based vendor for governance reasons
Choose DeepSeek V4 for:
- Frontier-level reasoning and coding on an open license
- Very long documents and repositories (1M-token context as the default)
- Teams that need Anthropic/OpenAI API compatibility with open-weight economics
Frequently asked questions
Is Llama 4 better than DeepSeek V4?
DeepSeek V4 outclasses Llama 4 on raw capability — the 2026 release cycle left Maverick behind on reasoning and coding. Llama 4 still wins on three practical axes: ecosystem depth (everything supports Llama first), deployability (Scout fits one H100), and vendor origin for organizations that require a US supplier. That last axis, not benchmarks, is why Llama remains the institutional default.
Which is cheaper: Llama 4 or DeepSeek V4?
Llama 4: Varies by hosting provider (open weights). DeepSeek V4: $1.74 in / $3.48 out per 1M tokens. Both are open-weight, so self-hosting costs depend on your hardware and utilization.
Can I self-host Llama 4 and DeepSeek V4?
Llama 4: yes — weights are published under Llama Community. DeepSeek V4: yes — weights are published under MIT.