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KAT-Coder-Pro V2 vs Llama 3.1 8B

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for KAT-Coder-Pro V2 and Llama 3.1 8B.

Kuaishou

KAT-Coder-Pro V2

KAT-Coder-Pro V2 is the latest high-performance model in KwaiKAT’s KAT-Coder series, designed for complex enterprise-grade software engineering and SaaS integration. It builds on the agentic coding strengths of earlier versions,...

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Meta

Llama 3.1 8B

Llama 3.1 8B is Meta's lightweight open-weight model from the Llama 3.1 generation, optimized for efficient deployment on consumer hardware and edge devices. Despite its compact 8-billion-parameter size, it delivers strong performance on instruction following, text summarization, and lightweight coding tasks. Lllama 3.1 8B is the most downloaded model in the Llama family and runs efficiently on laptops, single GPUs, and CPU via quantization — making it the default choice for on-device AI applications and local prototyping.

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Technical Specifications

SpecificationKAT-Coder-Pro V2Llama 3.1 8B
ProviderKuaishouMeta
Context Window256,000 tokens131,072 tokens
Agent SuitabilityN/A74/100
Time to First Token (TTFT)N/A80 ms
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-03-272024-07-23

API Pricing Comparison

Input Price per Million Tokens

KAT-Coder-Pro V2

$0.30

Llama 3.1 8B

$0.04

Output Price per Million Tokens

KAT-Coder-Pro V2

$1.20

Llama 3.1 8B

$0.04

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

KAT-Coder-Pro V2 Quirks & Gotchas

No developer gotchas reported.

Llama 3.1 8B Quirks & Gotchas

  • Perfect for CPU/edge deployment — runs on Raspberry Pi with quantization
  • Limited tool calling vs larger models — best for simple classification and chat