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Llama 4 Scout vs Yi-Lightning

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 Llama 4 Scout and Yi-Lightning.

Meta

Llama 4 Scout

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...

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01.AI

Yi-Lightning

Yi-Lightning is 01.AI's (離一万物) fastest and most cost-efficient model, purpose-built for high-throughput production workloads. It delivers competitive performance against GPT-4o-mini and Claude Haiku at a fraction of the cost, with exceptional bilingual Chinese-English capabilities. Yi-Lightning excels at classification, entity extraction, summarization, and high-frequency API tasks where latency and cost-per-call are critical constraints.

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Technical Specifications

SpecificationLlama 4 ScoutYi-Lightning
ProviderMeta01.AI
Context Window10,000,000 tokens131,072 tokens
Agent Suitability82/10082/100
Time to First Token (TTFT)350 ms120 ms
Deployment Modelself hostablemanaged api
Production Stabilitybetastable
API AvailableYesYes
Released Date2025-04-052025-10-01

API Pricing Comparison

Input Price per Million Tokens

Llama 4 Scout

$0.10

Yi-Lightning

$0.15

Output Price per Million Tokens

Llama 4 Scout

$0.30

Yi-Lightning

$0.30

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Benchmark Performance Metrics

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

MMLUGeneral knowledge & multi-task understanding
8720.0%vsN/A
Llama 4 Scout
Yi-Lightning
HumanEvalPython coding & logic synthesis
8950.0%vsN/A
Llama 4 Scout
Yi-Lightning
MATHComplex mathematical problem solving
8100.0%vsN/A
Llama 4 Scout
Yi-Lightning
GPQAGraduate-level expert reasoning
6680.0%vsN/A
Llama 4 Scout
Yi-Lightning
HellaSwagCommonsense reasoning and inference
9450.0%vsN/A
Llama 4 Scout
Yi-Lightning
MT-BenchMulti-turn conversation flow quality
910.0%vsN/A
Llama 4 Scout
Yi-Lightning

Llama 4 Scout Quirks & Gotchas

  • β–Έ10M context causes significant VRAM pressure β€” recommend 4-bit quantization
  • β–ΈPrimarily designed for RAG, not agentic tool calling

Yi-Lightning Quirks & Gotchas

  • β–ΈBest cost-efficiency for high-volume bilingual applications
  • β–ΈSelf-hostable via Ollama β€” excellent open-weight option for Asian-language pipelines