DeepSeek V4 vs Llama 4
Pricing, benchmarks, and use case comparison
Verdict
Our pick: DeepSeek V4Pick DeepSeek V4 for the strongest open-weight reasoning and agentic coding; pick Llama 4 for record-breaking context length, native multimodality, and single-GPU deployability.
Specs comparison
| DeepSeek V4 | 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 | Llama 4 |
|---|---|---|
| SWE-bench Verified | 80.6% | - |
| Math / STEM / Coding (open-model comparison) | Best among open models (per DeepSeek) | - |
| 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.
Capability and benchmarks
Both are open-weight MoE models but aim at different strengths. DeepSeek V4 (Pro) is the reasoning and coding leader: 80.6% SWE-bench Verified (Pro-Max config) and, per DeepSeek, best-among-open-models on math, STEM, and coding, with capability scores of coding 93, reasoning 92, and math 93. Llama 4 is built around multimodality and context: it is natively multimodal via early fusion, but its reasoning and coding scores are lower (coding 72, reasoning 74). For pure problem-solving, DeepSeek V4 is well ahead.
Context, multimodality, and licensing
Llama 4 wins on two axes. Its Scout variant has an industry-leading 10M-token context (Maverick ~1M) and it accepts image input natively, whereas DeepSeek V4 is text-only (multimodal score 10) with a 1M-token context. Licensing differs too: DeepSeek V4 ships under the permissive MIT license, while Llama 4 uses the Llama 4 Community License, which requires a separate license for organizations above 700M monthly active users. Scout also fits a single H100 with int4 quantization, easing self-hosting.
Which to pick
- Pick DeepSeek V4 for frontier-class open reasoning, agentic coding, and math, and if you want a fully permissive MIT license (hosted API ~$0.435/$0.87 per 1M).
- Pick Llama 4 for extremely long context, native image understanding, or single-GPU self-hosting via Scout.
Which should you choose?
Choose DeepSeek V4 if...
- →You need a frontier-class open model you can self-host for data control
- →Your workload involves very long documents, codebases, or agent trajectories (up to 1M tokens)
- →You want top-tier agentic coding at a fraction of closed-model cost
- →You need to fine-tune or customize a strong base model
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