Why Chinese Developers Pay More for GPT-5.6 than DeepSeek
Chinese developers are climbing the firewall to access OpenAI’s GPT-5.6 models, despite their significantly higher cost compared to alternatives like DeepSeek and Zhipu’s offerings.
The Cost Comparison
OpenAI’s new GPT-5.6 models are considerably pricier than their Chinese counterparts:
- GPT-5.6 Sol: $5 per million input tokens and $30 per million output tokens
- GPT-5.6 Terra: $2.50 per million input tokens and $15 per million output tokens
- GPT-5.6 Luna: $1 per million input tokens and $6 per million output tokens
In contrast:
- Zhipu’s GLM-5.2: $1.40 per million input tokens and $4.40 per million output tokens
- DeepSeek V4: Up to $0.44 per million input tokens and $0.87 per million output tokens
At first glance, DeepSeek appears significantly cheaper than OpenAI’s offerings. However, a different metric reveals a different picture.
Efficiency Over Price
The per-token pricing metric is not the most relevant factor when evaluating these models. What matters more is how many tokens a model requires to complete a task.
Research from Artificial Analysis found that GPT-5.6 uses roughly one ninth of the output tokens DeepSeek V4 needs while achieving higher overall performance in coding-agent tasks.
This efficiency translates to:
- Faster execution: Fewer tokens mean quicker processing times.
- Lower inference cost: Models that use fewer tokens are less expensive to run.
This logic is becoming increasingly important for businesses, where falling token prices have coincided with rising overall costs as organizations prioritize efficiency over raw price per token.
User Perspectives
Users in China praise GPT-5.6 for its:
- Methodical problem solving: Li Yitao, co-founder of Quotaflow, highlights ChatGPT’s ability to tackle large projects systematically.
- Improved multi-turn conversation consistency: Vincent Liu observes that the model displays less drift during prolonged conversations.
This preference for efficiency and accuracy over raw cost per token underscores a shift in the AI landscape, where enterprise customers demand models capable of sophisticated reasoning and nuanced decision-making.