DeepSeek V4 Pro 0813 vs GLM-5.2
deepseek/deepseek-v4-pro-0813 · z-ai/glm-5.2
As of November 10, 2026, GLM-5.2 is the cheaper API on UberLLM: $0.0000 per million input tokens and $0.0170 per million output, versus $0.0023 / $0.0932 for DeepSeek V4 Pro 0813, about 82% less when output is 3 tokens for every input token. These are the best prices on the market right now. Both work with one OpenAI-compatible key and UberLLM adds no fee. Choose GLM-5.2 to cut cost; choose DeepSeek V4 Pro 0813 when its capabilities fit your task better.
DeepSeek V4 Pro 0813 vs GLM-5.2, side by side
| Spec | DeepSeek V4 Pro 0813 | GLM-5.2 |
|---|---|---|
| Price · input / 1M | $0.0023 | $0.0000 |
| Price · output / 1M | $0.0932 | $0.0170 |
| Context length | 1,048,576 | 1,048,576 |
| Max output tokens | 384,000 | 131,072 |
| Input modalities | text | text |
| Output modalities | text | text |
| Tools / functions | Yes | Yes |
| Reasoning | Yes | Yes |
| % off list | 97.9% off | 99.6% off |
| First token, median (7 days) | — | 4.5 s (1,273 requests) |
Best prices on UberLLM right now. No UberLLM fee on requests; adding credit by card costs 5.5% + $0.50. Updated November 10, 2026. A price more than 70% below list is what buyers were billed, not an error. Why so cheap?
DeepSeek V4 Pro 0813 vs GLM-5.2 — FAQ
Is DeepSeek V4 Pro 0813 or GLM-5.2 cheaper?
GLM-5.2 is cheaper on a blended token mix, by about 82%. A request costs what its provider charged, UberLLM adds no fee, and every request is listed with its cost in Activity.
What's the price difference between DeepSeek V4 Pro 0813 and GLM-5.2?
DeepSeek V4 Pro 0813: $0.0023 in / $0.0932 out per 1M tokens. GLM-5.2: $0.0000 in / $0.0170 out per 1M tokens. That's roughly a 82% blended difference. Prices are live; UberLLM adds no fee.
Can I use both DeepSeek V4 Pro 0813 and GLM-5.2 with one API?
Yes. Both are served through a single OpenAI-compatible endpoint — set the base URL to https://api.uberllm.dev/v1 and your UberLLM key, then switch between them by changing the model id. Anthropic SDKs work via https://api.uberllm.dev/v1/messages.