โ† Back to Model Hub/SIDE-BY-SIDE REVIEW
SHARE THIS:

Llama 3 8B Instruct vs Qwen 2.5 72B

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 3 8B Instruct and Qwen 2.5 72B.

Meta

Llama 3 8B Instruct

Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases. It has demonstrated strong...

View Full Specs
Alibaba

Qwen 2.5 72B

Qwen 2.5 72B is Alibaba Cloud's flagship open-weight large language model from the Qwen 2.5 generation, delivering GPT-4-class performance across general reasoning, coding, mathematics, and multilingual tasks with strong Chinese-language superiority. It supports a 131,072-token context window and is available under a permissive Apache 2.0 license for both research and commercial use, making it one of the most popular open-weight alternatives to Llama for bilingual applications.

View Full Specs

Technical Specifications

SpecificationLlama 3 8B InstructQwen 2.5 72B
ProviderMetaAlibaba
Context Window8,192 tokens131,072 tokens
Agent SuitabilityN/A88/100
Time to First Token (TTFT)N/A280 ms
Deployment Modelself hostableself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-04-182025-09-19

API Pricing Comparison

Input Price per Million Tokens

Llama 3 8B Instruct

$0.14

Qwen 2.5 72B

$0.40

Output Price per Million Tokens

Llama 3 8B Instruct

$0.14

Qwen 2.5 72B

$0.80

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.

Llama 3 8B Instruct Quirks & Gotchas

No developer gotchas reported.

Qwen 2.5 72B Quirks & Gotchas

  • โ–ธStrong bilingual (ZH/EN) performance โ€” best open model for Chinese-language tasks
  • โ–ธSelf-hostable via vLLM or Ollama with 4-bit quantization