Glm 4 9b Chat API

What is GLM-4-9B-Chat?

GLM-4-9B-Chat is a 9-billion parameter large language model developed by Zhipu AI as part of the GLM-4 model family. It's designed as a bilingual (Chinese and English) conversational model with strong performance in chat, reasoning, and function calling tasks. The model uses the ChatGLM architecture, which combines bidirectional and autoregressive objectives to achieve efficient inference while maintaining high-quality outputs.

This model sits in the "efficient mid-size" category—larger than compact 7B models but significantly more resource-friendly than 70B+ flagship models. GLM-4-9B-Chat is particularly well-suited for applications requiring Chinese language support, cost-conscious deployments, or scenarios where latency matters more than absolute state-of-the-art performance.

When accessed through Aiduct, GLM-4-9B-Chat becomes available via OpenAI-compatible endpoints at https://api.aiduct.ai/v1, eliminating the need to manage provider-specific SDKs or authentication schemes.

Who Should Use GLM-4-9B-Chat?

GLM-4-9B-Chat is ideal for engineering teams building:

Bilingual applications: If your product serves Chinese-speaking users or requires Chinese-English translation, code-switching, or cross-lingual understanding, GLM-4-9B-Chat offers native bilingual training rather than retrofitted multilingual support.

Cost-optimized chat interfaces: The 9B parameter count delivers a favorable cost-performance ratio for conversational AI, customer support bots, or internal tooling where you need coherent multi-turn dialogue without the expense of frontier models.

Low-latency inference: Smaller parameter count translates to faster token generation. If your application requires sub-second response times or handles high request volumes, GLM-4-9B-Chat's efficiency becomes a practical advantage.

Function calling and tool use: The model supports structured output and function calling, making it suitable for agentic workflows, API orchestration, or applications that need to invoke external tools based on user input.

Teams already using OpenAI or Anthropic models can evaluate GLM-4-9B-Chat as a drop-in alternative by changing only the model identifier in their existing code.

How to Call GLM-4-9B-Chat via Aiduct

Aiduct exposes GLM-4-9B-Chat through the standard OpenAI ChatCompletion interface. You'll use the model identifier glm-4-9b-chat in your requests.

Python Example (OpenAI SDK)

from openai import OpenAI

client = OpenAI(
    api_key="your-aiduct-api-key",
    base_url="https://api.aiduct.ai/v1"
)

response = client.chat.completions.create(
    model="glm-4-9b-chat",
    messages=[
        {"role": "system", "content": "You are a helpful assistant fluent in Chinese and English."},
        {"role": "user", "content": "Explain the difference between async and sync Python code."}
    ],
    temperature=0.7,
    max_tokens=512
)

print(response.choices[0].message.content)

cURL Example

curl https://api.aiduct.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-aiduct-api-key" \
  -d '{
    "model": "glm-4-9b-chat",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What are the trade-offs of using a 9B parameter model?"}
    ],
    "temperature": 0.7,
    "max_tokens": 500
  }'

The response follows the standard OpenAI ChatCompletion schema, so existing parsing logic, logging, and error handling work without modification.

Key Features and Capabilities

Bilingual proficiency: GLM-4-9B-Chat is trained on balanced Chinese and English corpora, making it effective for tasks like translating technical documentation, answering questions in either language, or handling mixed-language inputs common in global SaaS products.

Function calling: The model supports OpenAI-style function calling, allowing you to define tools or APIs the model can invoke. This is useful for building agents that query databases, trigger workflows, or integrate with third-party services.

Multi-turn conversation: GLM-4-9B-Chat maintains context across multiple turns, making it suitable for interactive chat applications, troubleshooting wizards, or any scenario requiring stateful dialogue.

Efficient inference: At 9 billion parameters, the model offers a practical balance between capability and speed. You'll see lower latency and higher throughput compared to 30B+ models, which matters for real-time applications or high-concurrency deployments.

Standard tokenization: The model uses a SentencePiece-based tokenizer optimized for Chinese and English. When accessed via Aiduct, token counting and billing align with the upstream provider's tokenization, so you can predict costs accurately.

Pricing and Cost Considerations

GLM-4-9B-Chat pricing through Aiduct is determined by input and output token volume. Because the model is smaller than flagship alternatives, per-token costs are typically lower, and the reduced parameter count means faster generation—so you pay for fewer output tokens per second of user wait time.

For current per-token rates, visit the Aiduct pricing page. Pricing is subject to change based on upstream provider costs and Aiduct's routing optimizations.

When evaluating cost, consider:

• Prompt length: Longer system prompts or few-shot examples increase input token usage. GLM-4-9B-Chat's context window accommodates typical chat applications, but extremely long contexts may require truncation.
• Output verbosity: Tune max_tokens and temperature to control response length. Lower temperature often produces more concise answers.
• Caching opportunities: If you're sending repeated system prompts or prefixes, upstream caching (when available) can reduce effective input token costs.

Aiduct vs. Direct Provider Access

Unified API surface: Aiduct normalizes GLM-4-9B-Chat behind the same OpenAI-compatible endpoint you use for GPT, Claude, or other models. This means one SDK, one authentication flow, and one set of request/response types.

Model switching: Changing "model": "glm-4-9b-chat" to "model": "gpt-4" in your code is all it takes to A/B test models. You don't need separate API keys, billing accounts, or client libraries.

Observability: Aiduct provides centralized logging, latency metrics, and error tracking across all models. You can compare GLM-4-9B-Chat's performance against alternatives without stitching together multiple monitoring systems.

Fallback and routing: Aiduct can route requests to alternative providers if the primary endpoint is unavailable or rate-limited, reducing downtime risk.

Billing consolidation: One invoice covers usage across all models, simplifying procurement and cost allocation.

Direct provider access may offer marginally lower latency (one fewer network hop) and access to provider-specific features not exposed through the unified API. For most production applications, Aiduct's operational benefits outweigh these trade-offs.

Common Gotchas and Best Practices

Language mixing: While GLM-4-9B-Chat handles bilingual input well, be explicit in your system prompt about the desired output language. If you want answers in English only, state that upfront to avoid code-switching mid-response.

Function calling format: Ensure your function definitions follow the OpenAI schema exactly. The model expects name, description, and parameters fields with JSON Schema types. Malformed tool definitions will result in poor or failed invocations.

Context window management: GLM-4-9B-Chat has a finite context window. For long conversations, implement sliding window logic or summarization to keep total token count within limits. Monitor the usage field in API responses to track consumption.

Temperature tuning: For factual or structured tasks, use temperature=0.3 or lower. For creative or conversational use cases, 0.7 to 0.9 works better. The model's behavior is sensitive to this parameter.

Rate limits: Aiduct enforces per-key rate limits. If you're building high-throughput systems, contact support to discuss limit increases or implement client-side queueing and exponential backoff.

Error handling: Treat 5xx responses as transient and retry with backoff. Treat 4xx responses (especially 400 and 401) as permanent—log the request payload and fix the issue before retrying.

Comparison to Other Models

GLM-4-9B-Chat occupies a similar niche to models like Mistral 7B, Llama 2 13B, or Qwen 7B—mid-size, efficient, and suitable for production chat applications. Its standout feature is bilingual Chinese-English performance, which is stronger than most Western-trained models of comparable size.

Compared to GPT-3.5 Turbo, GLM-4-9B-Chat offers lower cost and faster inference but may lag in complex reasoning or nuanced English-language tasks. Compared to GPT-4 or Claude 3, it's significantly cheaper and faster but less capable on hard problems.

For Chinese-language tasks, GLM-4-9B-Chat often outperforms general-purpose models that treat Chinese as a secondary language. If your workload is Chinese-heavy, this model is worth benchmarking against your current provider.

Getting Started

1. Sign up for an Aiduct API key at aiduct.ai.
2. Install the OpenAI Python SDK: pip install openai.
3. Set your base URL to https://api.aiduct.ai/v1 and your API key in the client constructor.
4. Use "model": "glm-4-9b-chat" in your chat.completions.create() calls.
5. Monitor token usage in the usage field of responses and compare cost/performance against your existing models.

For detailed API documentation, rate limits, and integration guides, visit the Aiduct documentation.