DeepSeek V3 vs Llama 4
2026 - Pricing, benchmarks, and use case comparison
Quick take
- •Llama 4 is open-weights - free to self-host with no API costs. DeepSeek V3 requires paid API access.
- •Llama 4 has a 10M context window - 78x larger than DeepSeek V3's 128K. Better for long documents and large codebases.
Specs comparison
| DeepSeek V3 | Llama 4 | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Type | Open source | Open source |
| Context window | 128K | ✓10M |
| Input / 1M tokens | $0.27 | ✓Free (self-host) |
| Output / 1M tokens | $1.10 | Free (self-host) |
| Release date | 2024-12 | 2025-04 |
Benchmarks
| Benchmark | DeepSeek V3 | Llama 4 |
|---|---|---|
| HumanEval | 90.2% | - |
| MMLU | 88.5% | ~85% |
| Aider Polyglot | 55.0% | - |
Scores sourced from official provider release posts.
Strengths
DeepSeek V3
- ✓Near-GPT-4o quality at a fraction of the price
- ✓Open weights - self-host or fine-tune freely
- ✓Efficient MoE architecture reduces inference cost
- ✓Strong coding (Aider polyglot, HumanEval)
- ✓Good instruction following and structured output
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 V3 if you need...
- →Cost-sensitive high-volume inference
- →Self-hosted deployments
- →Fine-tuning for specialized domains
- →Coding assistants
Choose Llama 4 if you need...
- →Self-hosted and on-premise deployments
- →Privacy-sensitive workloads
- →Custom fine-tuning
- →Researchers and open-source builders