DeepSeek V3 vs Llama 4
Pricing, benchmarks, and use case comparison
Quick take
- •DeepSeek V3 is meaningfully stronger at cost efficiency (92 vs 82 on our capability index).
- •Llama 4 is meaningfully stronger at multimodal (82 vs 10).
- •Llama 4 has a Up to 10M tokens (Scout); ~1M tokens (Maverick) context window vs 128K tokens - better for whole-repo or long-document work.
Specs comparison
| DeepSeek V3 | Llama 4 | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Type | Open source | Open source |
| Context window | 128K tokens | ✓Up to 10M tokens (Scout); ~1M tokens (Maverick) |
| Input / 1M tokens | Free (self-host) | Free (self-host) |
| Output / 1M tokens | Free (self-host) | Free (self-host) |
| Release date | 2024-12 | 2025-04 |
Benchmarks
| Benchmark | DeepSeek V3 | Llama 4 |
|---|---|---|
| Pre-training scale | ~15T tokens | - |
| Scout context window | - | 10M tokens |
| Scout size | - | 17B active / 109B total (16 experts) |
| Maverick size | - | 17B active / 400B total (128 experts) |
Scores sourced from official provider release posts and independent benchmark aggregators.
Which should you choose?
Choose DeepSeek V3 if...
- →You want a proven, stable open model with broad ecosystem support
- →You need to self-host or fine-tune without licensing friction
- →Cost is critical and you don't need V4's 1M context or top scores
- →You want reproducible open-weight behavior pinned to a known version
Choose Llama 4 if...
- →You need extremely long context in an open model (Scout's 10M window)
- →Self-hosted or on-prem multimodal deployment
- →You want an efficient MoE that activates few parameters per token
- →Fine-tuning or full control over the model