DeepSeek V4 vs Llama 4
2026 - Pricing, benchmarks, and use case comparison
Quick take
- •Llama 4 has a 10M context window - 78x larger than DeepSeek V4's 128K. Better for long documents and large codebases.
Specs comparison
| DeepSeek V4 | Llama 4 | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Type | Open source | Open source |
| Context window | 128K | ✓10M |
| Input / 1M tokens | Free (self-host) | Free (self-host) |
| Output / 1M tokens | Free (self-host) | Free (self-host) |
| Release date | 2025-12 | 2025-04 |
Benchmarks
| Benchmark | DeepSeek V4 | Llama 4 |
|---|---|---|
| MMLU | - | ~85% |
Scores sourced from official provider release posts.
Strengths
DeepSeek V4
- ✓Mixture-of-Experts architecture - high capability, low activation cost
- ✓Open-source weights freely available
- ✓Strong coding and reasoning benchmarks
- ✓Flash variant offers low-latency inference
- ✓Significantly cheaper to run than US frontier models
Llama 4
- ✓Fully open weights - no usage restrictions
- ✓10M context in Llama 4 Scout variant
- ✓Native multimodal support
- ✓Strong performance relative to size
- ✓Enormous ecosystem of community tools and fine-tunes
Which should you choose?
Choose DeepSeek V4 if you need...
- →Self-hosted deployments needing frontier performance
- →Cost-sensitive high-volume inference
- →Coding and technical tasks
- →Researchers studying MoE architectures
Choose Llama 4 if you need...
- →Self-hosted and on-premise deployments
- →Privacy-sensitive workloads
- →Custom fine-tuning
- →Researchers and open-source builders