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Qwen3 30B A3B Instruct 2507 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 Qwen3 30B A3B Instruct 2507 and Yi-Lightning.

Alibaba

Qwen3 30B A3B Instruct 2507

Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model from Qwen, with 3.3B active parameters per inference. It operates in non-thinking mode and is designed for high-quality instruction following, multilingual understanding, and...

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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

SpecificationQwen3 30B A3B Instruct 2507Yi-Lightning
ProviderAlibaba01.AI
Context Window131,072 tokens131,072 tokens
Agent SuitabilityN/A82/100
Time to First Token (TTFT)N/A120 ms
Deployment Modelself hostablemanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2025-07-292025-10-01

API Pricing Comparison

Input Price per Million Tokens

Qwen3 30B A3B Instruct 2507

$0.05

Yi-Lightning

$0.15

Output Price per Million Tokens

Qwen3 30B A3B Instruct 2507

$0.19

Yi-Lightning

$0.30

Want to test both models live?

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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.

Qwen3 30B A3B Instruct 2507 Quirks & Gotchas

No developer gotchas reported.

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