| Signal | Llama 4 Scout | Delta | Kimi K2.6 |
|---|---|---|---|
Capabilities | 67 | -17 | |
Benchmarks | 52 | +1 | |
Pricing | 100 | +3 | |
Context window size | 88 | +2 | |
Recency | 63 | -37 | |
Output Capacity | 70 | -20 | |
| Overall Result | 3 wins | of 6 | 3 wins |
Score History
54.2
current score
Llama 4 Scout
right now
54.1
current score
Meta
Moonshot AI
Llama 4 Scout saves you $232.00/month
That's $2784.00/year compared to Kimi K2.6 at your current usage level of 100K calls/month.
| Metric | Llama 4 Scout | Kimi K2.6 | Winner |
|---|---|---|---|
| Overall Score | 54 | 54 | Llama 4 Scout |
| Rank | #151 | #152 | Llama 4 Scout |
| Quality Rank | #151 | #152 | Llama 4 Scout |
| Adoption Rank | #151 | #152 | Llama 4 Scout |
| Parameters | -- | -- | -- |
| Context Window | 328K | 262K | Llama 4 Scout |
| Pricing | $0.08/$0.30/M | $0.80/$3.50/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 83 | Kimi K2.6 |
| Benchmarks | 52 | 51 | Llama 4 Scout |
| Pricing | 100 | 97 | Llama 4 Scout |
| Context window size | 88 | 86 | Llama 4 Scout |
| Recency | 63 | 100 | Kimi K2.6 |
| Output Capacity | 70 | 90 | Kimi K2.6 |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 54/100 (rank #151), placing it in the top 48% of all 290 models tracked.
Scores 54/100 (rank #152), placing it in the top 48% of all 290 models tracked.
With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
Llama 4 Scout offers 91% better value per quality point. At 1M tokens/day, you'd spend $5.70/month with Llama 4 Scout vs $64.50/month with Kimi K2.6 - a $58.80 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. Llama 4 Scout also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (328K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.30/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (54/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Llama 4 Scout and Kimi K2.6 are extremely close in overall performance (only 0.10000000000000142 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Llama 4 Scout
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 4 Scout
91% lower pricing; better value at scale
Best for Reliability
Llama 4 Scout
Higher uptime and faster response speeds
Best for Prototyping
Llama 4 Scout
Stronger community support and better developer experience
Best for Production
Llama 4 Scout
Wider enterprise adoption and proven at scale
by Meta
| Capability | Llama 4 Scout | Kimi K2.6 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Meta
Moonshot AI
Llama 4 Scout saves you $5.14/month
That's 91% cheaper than Kimi K2.6 at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Llama 4 Scout | Kimi K2.6 |
|---|---|---|
| Context Window | 328K | 262K |
| Max Output Tokens | 16,384 | 262,144 |
| Open Source | Yes | Yes |
| Created | Apr 5, 2025 | Apr 20, 2026 |