Type
Spec
Rate
Input
per 1M prompt tokens
Free
Output
per 1M generated tokens
Free
Implicit cache read
per 1M cached input tokens
Free
Web search
per request when enabled
$0.033
GLM 4.7 Flash and GLM 4.5 Flash side by side: specs, capabilities, parameters, and live pricing from the EmpirioLabs catalog.
Z.ai Text GenerationGLM 4.7 Flash and GLM 4.5 Flash are both Z.ai models in the Text Generation category, available through the EmpirioLabs API.
| Spec | GLM 4.7 Flash | GLM 4.5 Flash |
|---|---|---|
| Model ID | glm-4-7-flash | glm-4-5-flash |
| Author | Z.ai | Z.ai |
| Released | Jan 19, 2026 | Jul 28, 2025 |
| Context window | 202.8K tokens | 128K tokens |
| Max output | 131,072 tokens | 98,304 tokens |
| Input | Text | Text |
| Output | Text | Text |
| Structured output | JSON Mode | JSON Mode |
| Region | Singapore | Singapore |
| Endpoints | POST /v1/chat/completions POST /v1/responses POST /v1/messages POST /v1beta/models/glm-4-7-flash:generateContent | POST /v1/chat/completions POST /v1/responses POST /v1/messages POST /v1beta/models/glm-4-5-flash: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 4.7 Flash | GLM 4.5 Flash |
|---|---|---|
| temperature | 0 to 1 Default: 1 | 0 to 1 Default: 0.6 |
| top_p | 0.01 to 1 Default: 0.95 | 0.01 to 1 Default: 0.95 |
| max_tokens | 1 to 131072 Default: 4096 | 1 to 98304 Default: 4096 |
| stop | array | array |
| do_sample | boolean Default: true | boolean Default: true |
| enable_thinking | boolean Default: true | boolean Default: true |
| thinking | object | object |
| preserve_thinking | boolean Default: false | - |
| tools | array | array |
| tool_choice | auto Default: auto | auto Default: auto |
| tool_stream | boolean Default: false | - |
| tool_web_search | boolean Default: false | boolean Default: false |
| search_result | boolean Default: true | boolean Default: true |
| search_prompt | string | string |
| count | 1 to 50 Default: 10 | 1 to 50 Default: 10 |
| search_domain_filter | string | string |
Base token use is free. Built-in web search is optional through tool_web_search and adds $0.033 per request when enabled.
Base token use is free. Built-in web search is optional through tool_web_search and adds $0.033 per request when enabled.
GLM 4.7 Flash 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-4-7-flash. Both models are called through the same endpoint. Set model to glm-4-7-flash or glm-4-5-flash. 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-4-7-flash",
"messages": [
{"role": "user", "content": "Write a haiku about the ocean."}
]
}'GLM 4.7 Flash is the newer release (Jan 19, 2026); GLM 4.5 Flash was released on Jul 28, 2025. GLM 4.7 Flash has the larger context window: 202.8K tokens, against 128K for GLM 4.5 Flash. GLM 4.7 Flash returns up to 131,072 output tokens per response, GLM 4.5 Flash up to 98,304. The specs, rate cards, and parameters on this page come from the live EmpirioLabs catalog.
GLM 4.7 Flash is the newer release (Jan 19, 2026); GLM 4.5 Flash was released on Jul 28, 2025.
Both models list the same rate for Web search (per request when enabled). 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.