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

Qwen 2.5 72B vs Qwen3.6 Flash

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 Qwen 2.5 72B and Qwen3.6 Flash.

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
Alibaba

Qwen3.6 Flash

Qwen3.6 Flash is a fast, efficient language model from Alibaba's Qwen 3.6 series. It supports text, image, and video input with a 1M token context window. Tiered pricing kicks in...

View Full Specs

Technical Specifications

SpecificationQwen 2.5 72BQwen3.6 Flash
ProviderAlibabaAlibaba
Context Window131,072 tokens1,000,000 tokens
Agent Suitability88/100N/A
Time to First Token (TTFT)280 msN/A
Deployment Modelself hostableself hostable
Production Stabilitystablebeta
API AvailableYesYes
Released Date2025-09-192026-04-27

API Pricing Comparison

Input Price per Million Tokens

Qwen 2.5 72B

$0.40

Qwen3.6 Flash

$0.19

Output Price per Million Tokens

Qwen 2.5 72B

$0.80

Qwen3.6 Flash

$1.13

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.

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

Qwen3.6 Flash Quirks & Gotchas

No developer gotchas reported.