DeepSeek V4 Flash vs Llama 4
Pricing, benchmarks, and use case comparison
Quick take
- •DeepSeek V4 Flash is meaningfully stronger at math (86 vs 70 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 1M tokens - better for whole-repo or long-document work.
Specs comparison
| DeepSeek V4 Flash | Llama 4 | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Type | Open source | Open source |
| Context window | 1M 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 | 2026-04 | 2025-04 |
Benchmarks
| Benchmark | DeepSeek V4 Flash | Llama 4 |
|---|---|---|
| Reasoning (vs V4 Pro) | Closely approaches V4 Pro | - |
| 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 V4 Flash if...
- →You want most of V4 Pro's capability at a lower price and higher throughput
- →You need long context but on a tighter compute or cost budget
- →You are serving high request volumes where per-token cost dominates
- →You want an open model small enough to self-host on modest multi-GPU setups
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