Model Comparison
Llama 4 vs DeepSeek V4 - two MoE philosophies, different scale
Meta's Llama 4 family offers the longest context window in open models (10M tokens) and proven open-weight accessibility. DeepSeek V4 Pro (1.6T total, 49B active) pushes frontier coding performance with 80.6% SWE-Bench Verified, while V4 Flash (284B, 13B active) targets cost efficiency. Both families are MIT/open-weight licensed.
Performance
Head-to-head benchmark comparison
DeepSeek V4 Pro leads on raw coding benchmarks, while Llama 4 Scout offers an unmatched 10M token context window. Both families use MoE architecture at very different scales.
DeepSeek V4 launched April 2026 with two variants: Pro (1.6T total, 49B active) and Flash (284B, 13B active). Both offer 1M context windows. Llama 4 Maverick (400B, 17B active) competes on general benchmarks, while Scout's 10M context window remains unmatched. DeepSeek V4 Pro's 80.6% SWE-Bench Verified is within 0.2 points of Claude Opus 4.6.
DeepSeek V4 Pro: SWE-Bench Verified 80.6% - near Claude Opus 4.6 level
DeepSeek V4 Pro: 1.6T total parameters, 49B active - largest open-weight model
Maverick: MMLU Pro 80.5%, MMMU 73.4% - strong all-around performance
Scout: 10M token context - 10x longer than DeepSeek V4's 1M
DeepSeek V4 Flash: 284B total, 13B active - cost-efficient alternative
Full comparison
Llama 4 family vs DeepSeek V4 family
Complete benchmark results across reasoning, coding, and architecture metrics.
| Benchmark | Llama 4 Maverick 400B / 17B active Open Weight | Llama 4 Scout 109B / 17B active Long Context | DeepSeek V4 Pro 1.6T / 49B active Frontier | DeepSeek V4 Flash 284B / 13B active Efficient |
|---|---|---|---|---|
MMLU Pro Knowledge & reasoning | 80.5% | 74.3% | - | - |
SWE-Bench Verified Agentic coding | - | - | 80.6% | - |
MMMU Multimodal | 73.4% | 69.4% | - | - |
GPQA Diamond Scientific knowledge | 69.8% | 57.2% | - | - |
Context Window Max tokens | 1M | 10M | 1M | 1M |
Total Parameters Model size | 400B | 109B | 1.6T | 284B |
Active Parameters Per token | 17B | 17B | 49B | 13B |
License Commercial use | Llama 3.1 | Llama 3.1 | MIT | MIT |
API Cost Per million output tokens | Varies | Varies | $3.48 | <$1 |
Data from Meta's official model card, DeepSeek's technical report, and independent evaluations. April 2026.
Choose Llama 4
When to choose Llama 4 over DeepSeek V4
Llama 4 is the better choice when you need massive context windows, proven multimodal capabilities, or lower active parameter costs. Scout's 10M context is 10x longer than DeepSeek V4's 1M, and Maverick's 17B active parameters keep inference costs low.
- 10M token context (Scout) - 10x longer than DeepSeek V4
- 17B active parameters vs DeepSeek V4 Pro's 49B - lower inference cost
- MMMU 73.4% - proven multimodal understanding
- Broad ecosystem support across all major cloud providers
- Established open-weight community and tooling
Choose DeepSeek V4
When DeepSeek V4 has the edge
DeepSeek V4 Pro delivers near-Claude Opus 4.6 coding performance at a fraction of the cost. Its 80.6% SWE-Bench Verified score and MIT license make it compelling for coding-heavy production workloads.
- SWE-Bench Verified 80.6% - within 0.2 points of Claude Opus 4.6
- MIT license - more permissive than Llama 3.1 license
- $3.48 per million output tokens - 7x cheaper than Claude
- V4 Flash: 13B active parameters for ultra-efficient inference
- 1M context window on both Pro and Flash variants
Llama 4 Family
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