New Models Added: GLM-4.7-Flash, GLM-4.7-FlashX from Z.AI and GPT-5.2-Codex from OpenAI
Date: 2026-01-29 / (1404-11-10)
Summary
AvalAI adds three new models: GLM-4.7-Flash and GLM-4.7-FlashX from Z.AI offering faster and more cost-effective versions of the flagship GLM-4.7, plus GPT-5.2-Codex from OpenAI optimized for long-horizon agentic coding tasks with 400K context window and reasoning token support.
Details
Z.AI - GLM-4.7-Flash Series
The GLM-4.7-Flash series brings the power of GLM-4.7 in smaller, faster, and more affordable packages. These models achieve open-source SOTA scores among models of comparable size on mainstream benchmarks like SWE-bench Verified and τ²-Bench. Documentation
glm-4.7-flashx
The fastest model in the GLM-4.7 family, optimized for speed-critical applications while maintaining excellent programming capabilities.
| Feature | Details |
|---|---|
| Model ID | glm-4.7-flashx |
| Context Window | 200,000 tokens |
| Maximum Output | 128,000 tokens |
| Input Modalities | Text |
| Output Modalities | Text |
| Supported Endpoints | v1/chat/completions, v1/responses (partial), v1/messages (partial) |
| Capabilities | Chat, Function Calling, Structured Outputs |
| Best for | High-throughput applications, fast responses, cost-sensitive deployments |
Pricing:
| Token Type | Price per 1M Tokens |
|---|---|
| Input | $0.077 |
| Cached Input | $0.011 |
| Output | $0.44 |
glm-4.7-flash
A balanced model offering excellent performance with improved efficiency over the full GLM-4.7 model.
| Feature | Details |
|---|---|
| Model ID | glm-4.7-flash |
| Context Window | 200,000 tokens |
| Maximum Output | 128,000 tokens |
| Input Modalities | Text |
| Output Modalities | Text |
| Supported Endpoints | v1/chat/completions, v1/responses (partial), v1/messages (partial) |
| Capabilities | Chat, Function Calling, Structured Outputs |
| Best for | Frontend/backend development, Chinese writing, translation, long-form text processing |
Pricing:
| Token Type | Price per 1M Tokens |
|---|---|
| Input | $0.07 |
| Cached Input | $0.01 |
| Output | $0.40 |
Key Features of GLM-4.7-Flash Series:
- Open-source SOTA: Achieves state-of-the-art scores among similarly sized models on SWE-bench Verified and τ²-Bench
- Superior Development Capabilities: Excels at both frontend and backend development tasks
- Cost-Effective: Significantly lower pricing than the full GLM-4.7 model while maintaining quality
- Fast Inference: Optimized for speed with lower latency responses
- Versatile Applications: Excellent for Chinese writing, translation, long-form text processing, and role-playing interactions
OpenAI - GPT-5.2-Codex
GPT-5.2-Codex is OpenAI's most intelligent coding model, optimized for long-horizon, agentic coding tasks in Codex or similar environments. Documentation
| Feature | Details |
|---|---|
| Model ID | gpt-5.2-codex |
| Context Window | 400,000 tokens |
| Maximum Output | 128,000 tokens |
| Knowledge Cutoff | August 31, 2025 |
| Input Modalities | Text, Image |
| Output Modalities | Text |
| Supported Endpoints | v1/chat/completions, v1/responses, v1/realtime |
| Capabilities | Streaming, Function Calling, Structured Outputs, Reasoning Tokens |
| Best for | Agentic coding, long-horizon tasks, complex software development |
Pricing:
| Token Type | Price per 1M Tokens |
|---|---|
| Input | $1.75 |
| Cached Input | $0.175 |
| Output | $14.00 |
Key Features:
- Reasoning Token Support: Supports low, medium, high, and xhigh reasoning effort settings
- Extended Context: 400K token context window for handling large codebases
- High Output Capacity: Up to 128K tokens output for comprehensive code generation
- Vision Support: Can process images as input for visual code understanding
- Agentic Workflows: Optimized for autonomous coding agents and iterative development
API Usage Examples
GLM-4.7-FlashX Example
curl https://api.avalai.ir/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $AVALAI_API_KEY" \
-d '{
"model": "glm-4.7-flashx",
"messages": [
{
"role": "user",
"content": "Write a TypeScript function that implements a debounce utility with proper typing."
}
],
"max_tokens": 2048,
"temperature": 0.7
}'GPT-5.2-Codex Example
curl https://api.avalai.ir/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $AVALAI_API_KEY" \
-d '{
"model": "gpt-5.2-codex",
"messages": [
{
"role": "user",
"content": "Refactor this codebase to use dependency injection and implement a comprehensive test suite."
}
],
"max_tokens": 8192,
"temperature": 0.6
}'Pricing Comparison
| Model | Input | Cached Input | Output | Best For |
|---|---|---|---|---|
glm-4.7-flashx | $0.077 | $0.011 | $0.44 | Fast responses, high throughput |
glm-4.7-flash | $0.07 | $0.01 | $0.40 | Balanced performance |
glm-4.7 | $0.60 | $0.11 | $2.20 | Complex reasoning, full capability |
gpt-5.2-codex | $1.75 | $0.175 | $14.00 | Agentic coding, long-horizon tasks |