Simulating realistic Translation parameters (5,000 in / 5,500 out with 15% cache reuse). DeepSeek Coder V2.5 delivers a 91% cost reduction over GLM 4.7 (Zhipu).
| Traffic Volume Tier | DeepSeek Coder V2.5 Monthly | GLM 4.7 (Zhipu) Monthly | Monthly Savings by picking DeepSeek Coder V2.5 |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $2.146 | $24.856 | Save $22.711 / mo |
| 10,000 reqs/mo (Small App) | $21.455 | $248.563 | Save $227.108 / mo |
| 100,000 reqs/mo (Growth Production) | $214.55 | $2,485.625 | Save $2,271.075 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $2,145.50 | $24,856.25 | Save $22,710.75 / mo |
DeepSeek Coder V2.5 is 91% cheaper for Translation workloads. At standard Translation parameter ratios (5,000 input tokens, 5,500 output tokens, 15% cache hit), DeepSeek Coder V2.5 costs $0.002146 per request compared to $0.0249 on GLM 4.7 (Zhipu).
DeepSeek Coder V2.5 offers a context window of 128,000 tokens (max output: 8,192), while GLM 4.7 (Zhipu) offers 128,000 tokens (max output: 16,384).
At 100,000 requests per month, using DeepSeek Coder V2.5 saves $2,271.075 every month (or $27,252.90 annually) compared to GLM 4.7 (Zhipu).
Output length ≈ input length; budget both sides of the request. Japanese and Chinese text typically costs more per word than English due to tokenization. Cache translation memories and glossaries embedded in the prompt.