Qwen 2.5 from Alibaba DAMO Academy has established itself as the premier open-weight model family for multilingual enterprise deployments in Asia-Pacific — with best-in-class Chinese, Japanese, and Korean language performance that Western-trained models cannot match. Apache 2.0 licensing enables full commercial deployment and fine-tuning without royalty constraints, making Qwen 2.5 the default choice for enterprises operating across Asian markets. This comparison covers Qwen's model family, benchmark performance, and enterprise deployment scenarios where it outperforms frontier alternatives.
Qwen 2.5 Model Family
| Model | Parameters | Context | Specialisation | Licence |
|---|
| Qwen 2.5 72B | 72B | 128K tokens | General purpose — top open-weight general model | Qwen (non-commercial for 72B) |
| Qwen 2.5 32B | 32B | 128K tokens | Balance of capability and deployability | Apache 2.0 |
| Qwen 2.5 14B / 7B | 14B / 7B | 128K tokens | Edge and cost-efficient inference | Apache 2.0 |
| Qwen 2.5 Coder 32B | 32B | 128K tokens | Code generation — competitive with GPT-4o on coding benchmarks | Apache 2.0 |
| Qwen 2.5 Math 72B | 72B | 4K tokens | Mathematical reasoning — outperforms GPT-4o on MATH benchmark | Qwen (non-commercial for 72B) |
| QwQ-32B | 32B | 128K tokens | Extended reasoning — chain-of-thought, competitive with o1 mini | Apache 2.0 |
CJK Language Performance
Why Qwen Dominates CJK Language Tasks
Western frontier models (GPT-4, Claude, Llama 4) were pre-trained primarily on English and European language corpora — their CJK language capabilities are added via multilingual training but remain secondary. Qwen 2.5 was trained on 18 trillion tokens with a significant proportion of high-quality Chinese, Japanese, and Korean text — the model's tokenizer, vocabulary, and pre-training were optimised for CJK from the ground up. The result: 15–25% better performance on CJK benchmarks vs GPT-4o, with particularly large gaps in Chinese culture, literature, and domain-specific knowledge.
#1
Qwen 2.5 72B ranking on C-Eval (Chinese language understanding benchmark) and CMMLU (Chinese multitask language understanding) — highest-scoring model on both authoritative Chinese LLM benchmarks
Apache 2.0
Licence for all Qwen 2.5 models up to 32B — full commercial use, fine-tuning, distribution, and modification permitted without royalties. The most permissive commercial licence of any frontier-quality multilingual model
18T
Training tokens for Qwen 2.5 — including a large proportion of Chinese, Japanese, and Korean high-quality text. Data quality and CJK representation drive the multilingual performance advantage
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Pan-Asian Customer Service
Deploy Qwen 2.5 32B (Apache 2.0, self-hostable) for customer service automation serving Chinese, Japanese, and Korean customers — document Q&A, complaint handling, product enquiries. 15–25% better response quality vs GPT-4o on CJK tasks, at significantly lower cost when self-hosted. Our
ML development team deploys fine-tuned Qwen for enterprise customer service.
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Code Generation for Asian Dev Teams
Qwen 2.5 Coder 32B (Apache 2.0) — the best open-weight coding model in 2026 per HumanEval, matching GPT-4o at self-hosted inference cost. For development teams where Chinese-language code comments, documentation, and requirements are standard, Qwen Coder's bilingual capability is a significant advantage vs Western coding models.
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Financial Analysis in CJK Markets
Qwen 2.5 72B for financial document analysis, earnings report summarisation, and regulatory filing processing in Chinese, Japanese, and Korean — tasks where deep language understanding of the specific idioms, regulatory terminology, and business culture of each market matters significantly. Outperforms GPT-4o specifically on Chinese financial text benchmarks.
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Mathematical Reasoning
QwQ-32B (Apache 2.0) for complex mathematical and logical reasoning — competitive with o1-mini on AIME and MATH benchmarks, deployable self-hosted. Best open-weight option for: financial modelling, quantitative analysis, engineering calculations, and any enterprise workflow requiring extended chain-of-thought mathematical reasoning without frontier API cost.
Self-Hosting Qwen 2.5
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Hardware
GPU Requirements by Model Size
Qwen 2.5 7B: single RTX 4090 (24GB) in FP16. Qwen 2.5 14B: 2× RTX 4090 or single A100 80GB. Qwen 2.5 32B (including Coder 32B and QwQ-32B): 2× A100 80GB in FP16; single A100 with AWQ INT4 quantisation. Deploy with vLLM or Ollama for local development. All models available on Hugging Face — pull and serve via vllm serve Qwen/Qwen2.5-32B-Instruct. Our DevOps and ML teams manage GPU infrastructure for Qwen deployments.
vLLM servingA100 80GB for 32BAWQ INT4 quantisation
Deploying Qwen 2.5 for Enterprise?
Our ML development and DevOps teams deploy Qwen 2.5 models for enterprise production — GPU infrastructure, vLLM serving, fine-tuning on domain data, and CJK-optimised evaluation frameworks. Book a free advisory session.