Model Comparison
Llama 4 vs Kimi K2.6 - open-weight versatility meets agentic powerhouse
Meta's Llama 4 family (Scout 109B / Maverick 400B) brings the longest context window in open models and full open-weight access. Moonshot's Kimi K2.6 (1T total, 32B active, 384 experts) pushes the frontier on agentic coding and multimodal benchmarks. Two very different design philosophies - here's how they compare.
Performance
Head-to-head benchmark comparison
Llama 4 Maverick leads on context length and open accessibility, while Kimi K2.6 dominates agentic coding and several frontier benchmarks. Scout adds an unmatched 10M token context window.
Llama 4 and Kimi K2.6 target different strengths. Maverick is a strong all-rounder with open weights and 1M context. Kimi K2.6 is a 1T-parameter specialist built for agentic tasks, with native multimodal support via MoonViT. Scout's 10M context window remains unmatched by any model in this comparison.
Kimi K2.6: SWE-Bench Pro 58.6%, HLE-Full 54.0%, BrowseComp 83.2%
Maverick: MMLU Pro 80.5%, GPQA Diamond 69.8%, MMMU 73.4%
Scout: 10M token context - 39x longer than Kimi K2.6's 256K
Kimi K2.6: native multimodal via MoonViT 400M (text + image + video)
Both families use MoE architecture with different scale tradeoffs
Full comparison
Llama 4 Maverick vs Kimi K2.6 vs Llama 4 Scout
Complete benchmark results across reasoning, coding, multimodal, and architecture metrics.
| Benchmark | Llama 4 Maverick 400B / 17B active Open Weight | Kimi K2.6 1T / 32B active Agentic | Llama 4 Scout 109B / 17B active Long Context |
|---|---|---|---|
MMLU Pro Knowledge & reasoning | 80.5% | - | 74.3% |
GPQA Diamond Scientific knowledge | 69.8% | - | 57.2% |
MMMU Multimodal understanding | 73.4% | - | 69.4% |
SWE-Bench Pro Agentic coding | - | 58.6% | - |
HLE-Full Hard language eval | - | 54.0% | - |
BrowseComp Web browsing tasks | - | 83.2% | - |
Context Window Max tokens | 1M | 256K | 10M |
Total Parameters Model size | 400B | 1T | 109B |
Active Parameters Per token | 17B | 32B | 17B |
Number of Experts MoE routing | 128 | 384 (8+1 shared) | 16 |
Multimodal Input modalities | Text + Image | Text + Image + Video (MoonViT 400M) | Text + Image |
Data from Meta's official model card, Moonshot's technical report, and independent evaluations.
Choose Llama 4
When to choose Llama 4 over Kimi K2.6
Llama 4 is the better choice when you need massive context windows, open-weight flexibility, or a proven ecosystem. Scout's 10M token context is 39x longer than Kimi K2.6's 256K, and both Llama 4 models are fully open-weight for self-hosted deployment.
- 10M token context (Scout) - process entire codebases in one call
- Fully open-weight under Llama 3.1 compatible license
- Lower active parameter cost (17B vs 32B per token)
- Stronger general knowledge benchmarks (MMLU Pro 80.5%)
- Broad ecosystem support across cloud providers and frameworks
Choose Kimi K2.6
When Kimi K2.6 has the edge
Kimi K2.6 excels at agentic coding tasks and web browsing. Its 1T parameter scale with 384 experts and native video understanding via MoonViT 400M make it a strong choice for complex autonomous workflows.
- SWE-Bench Pro 58.6% - frontier agentic coding performance
- BrowseComp 83.2% - excellent web browsing and navigation
- HLE-Full 54.0% - strong on hard language evaluation
- Native video understanding via MoonViT 400M encoder
- 384 experts (8 selected + 1 shared) for deep specialization
Llama 4 Family
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