

GLM 5.2 and DeepSeek V4 Pro side by side: specs, capabilities, parameters, and live pricing from the EmpirioLabs catalog.
GLM 5.2 from Z.ai and DeepSeek V4 Pro from DeepSeek are both in the Text Generation category, available through the EmpirioLabs API.
| Spec | GLM 5.2 | DeepSeek V4 Pro |
|---|---|---|
| Model ID | glm-5-2 | deepseek-v4-pro |
| Author | Z.ai | DeepSeek |
| Released | Jun 16, 2026 | Apr 24, 2026 |
| Context window | 1M tokens | 1M tokens |
| Max output | 131,072 tokens | 393,216 tokens |
| Input | Text | Text |
| Output | Text | Text |
| Structured output | JSON Mode | JSON Schema |
| Region | Singapore | Germany |
| Endpoints | POST /v1/chat/completions POST /v1/responses POST /v1/messages POST /v1beta/models/glm-5-2:generateContent | POST /v1/chat/completions POST /v1/responses POST /v1/messages POST /v1beta/models/deepseek-v4-pro:generateContent |
| Capabilities |
Live rates from the EmpirioLabs catalog for both models. Each rate card always matches what the API charges.
Request parameters each model lists on EmpirioLabs, with the allowed values or range where one applies. A dash marks a parameter the model does not list.
| Parameter | GLM 5.2 | DeepSeek V4 Pro |
|---|---|---|
| max_tokens | 1 to 131072 Default: 65536 | 1 to 393216 |
| temperature | 0 to 1 Default: 1 | 0 to 2 Default: 0.7 |
| top_p | 0.01 to 1 Default: 0.95 | 0 to 1 Default: 0.9 |
| reasoning_effort | none, minimal, low, medium, high, xhigh, max Default: max | none, low, medium, high, max Default: medium |
| enable_thinking | boolean Default: true | boolean Default: true |
| preserve_thinking | boolean Default: false | - |
| do_sample | boolean Default: true | - |
| tool_web_search | boolean Default: false | - |
| search_recency_filter | oneDay, oneWeek, oneMonth, oneYear, noLimit Default: noLimit | - |
| count | 1 to 50 Default: 10 | - |
| search_domain_filter | string | - |
| search_prompt | string | - |
| search_result | boolean Default: true | - |
| tool_stream | boolean Default: false | - |
| tools | array Default: [] | - |
| tool_choice | object | - |
GLM 5.2 serves the OpenAI-compatible Chat Completions API. Point any OpenAI SDK at https://api.empiriolabs.ai/v1 with your EmpirioLabs API key and use the model id glm-5-2. Both models are called through the same endpoint. Set model to glm-5-2 or deepseek-v4-pro. Get an API key from the EmpirioLabs dashboard.
curl https://api.empiriolabs.ai/v1/chat/completions \
-H "Authorization: Bearer $EMPIRIOLABS_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-5-2",
"messages": [
{"role": "user", "content": "Write a haiku about the ocean."}
]
}'Prompt tests and comparison runs that cover both models.
Comparisons Kimi K3 vs GLM 5.2 vs DeepSeek V4 Pro Compare Kimi K3, GLM 5.2, and DeepSeek V4 Pro on EmpirioLabs: three 1M token reasoning models, side by side on context,...
Guides Kimi vs DeepSeek vs Qwen vs GLM: AI Coding Models Compared Kimi K2.7 Code vs DeepSeek V4 Pro vs Qwen3.7 Max vs GLM 5.2, same three prompts, one shot each. Watch all four build...GLM 5.2 is the newer release (Jun 16, 2026); DeepSeek V4 Pro was released on Apr 24, 2026. Both models have a context window of 1M tokens. DeepSeek V4 Pro returns up to 393,216 output tokens per response, GLM 5.2 up to 131,072. The specs, rate cards, and parameters on this page come from the live EmpirioLabs catalog.
GLM 5.2 is the newer release (Jun 16, 2026); DeepSeek V4 Pro was released on Apr 24, 2026.
GLM 5.2 lists the lower rate for Input (per 1M prompt tokens). DeepSeek V4 Pro lists the lower rate for Output (per 1M generated tokens). Both live rate cards are on this page and always match what the API charges.
Yes. Both models use POST /v1/chat/completions with the same EmpirioLabs API key, so most integrations switch by changing the model value. Check the parameters table on this page first if you rely on a model-specific parameter.
Yes. Open each model in the EmpirioLabs playground and run the same prompt on both, with the parameters the API exposes.
Check out our pricing or reach out if you want your own model deployed on our stack.