Llama 5 vs DeepSeek V4 Pro
Side-by-side comparison of pricing, context window, modalities, licensing, and strengths — with practical guidance on which model fits which workload.
| Spec | Llama 5 | DeepSeek V4 Pro |
|---|---|---|
| Provider | Meta | DeepSeek |
| Context window | 5M tokens | 1M tokens |
| Input price (per 1M tokens) | Free / Self-host | $0.44 |
| Output price (per 1M tokens) | Free / Self-host | $0.87 |
| Example workload (10M in + 2M out) | Free (self-hosted — infra costs only) | $6.14 / month |
| Modalities | text, image, code | text, code |
| Knowledge cutoff | Not disclosed | Not disclosed |
| Release date | Apr 2026 | Apr 2026 |
| License | Llama Community License | MIT |
| Strengths | 5M context, Open weights, 600B params, Fine-tune ecosystem | 1.6T MoE (49B active), Code & math, Ultra-low cost, Open weights |
| Open weights | Yes | Yes |
Verdict
The heavyweight open-weights bout of 2026 — both released in April. Meta’s Llama 5 (600B parameters) headlines with a 5-million-token context window, the largest of any widely available model, plus the deepest fine-tuning and tooling ecosystem in open-source AI. DeepSeek V4 Pro counters with a larger 1.6T MoE (49B active), stronger published code and math results, a fully permissive MIT license (versus Meta’s community license), and one of the cheapest hosted APIs anywhere if you’d rather not run it yourself.
When to choose Llama 5
Pick Llama 5 for extreme-context workloads (5M tokens), vision input, and the biggest community fine-tune ecosystem.
When to choose DeepSeek V4 Pro
Pick DeepSeek V4 Pro for maximum open-model capability on code/math, MIT-license freedom, and its ultra-cheap official API.
Prices and specs reflect published provider information and change frequently — always confirm on the provider's pricing page before committing to a workload.
Frequently asked questions
Which is cheaper: Llama 5 or DeepSeek V4 Pro?
One of these models has open weights, so API pricing isn't directly comparable — self-hosting shifts the cost to infrastructure. For the managed model, see the per-token prices in the table above.
Which has the larger context window?
Llama 5 supports 5M tokens versus 1M for DeepSeek V4 Pro — roughly 5.0× more room for documents, code, and conversation history.
Can I self-host either model?
Llama 5 has downloadable weights (Llama Community License), so you can run it on your own hardware. Both models are open-weights.
How should I test which model is better for my use case?
Benchmarks are a starting point, not an answer. Run both models on 20–50 examples of your real task and compare outputs blind. Use our token counter to estimate prompt sizes and the cost calculator to project monthly spend before committing.