Zai Glm
At a glance
- Price per 1M tokens (input)
- $1.40
- Price per 1M tokens (output)
- $4.40
- Context
- 1,048,576 tokens
- Free access
- yes (see below)
Refreshed daily; data verified September 18, 2026. Published September 18, 2026.
Zai Glm by Mistral AI is designed for developers and technical teams building applications that require reliable interaction with external systems through tool calling, enabling the model to invoke functions or APIs as part of its reasoning process. It excels in multi-step workflows where logical decomposition and sequential decision-making are needed, such as automating data retrieval, coordinating service calls, or executing conditional logic based on dynamic inputs. Mistral AI’s approach emphasizes open-weight models with strong reasoning alignment, allowing Zai Glm to balance general language understanding with precise, controllable behavior in tool-augmented environments.
Specifications & pricing
| Input (per 1M tokens) | $1.40 |
|---|---|
| Output (per 1M tokens) | $4.40 |
| Cache read (per 1M tokens) | $0.14 |
| Context window | 1,048,576 tokens |
| Max output | 131,072 tokens |
| Capabilities | tool calling, reasoning |
LiteLLM community dataset (MIT), verified September 18, 2026. Official Mistral AI pricing.
What Zai Glm would cost on your workload — run it through the cost calculator →
Where to try Zai Glm for free
- Mistral AI offers a free chat — Le Chat (free plan). A vendor's free chat may run a different model from the same family — the exact model is not guaranteed.
Frequently asked questions
What is tool calling and why is it useful in Zai Glm?+
Tool calling allows the model to request and use external functions or APIs during generation, enabling it to access real-time data or perform actions beyond its training knowledge. In Zai Glm, this capability is integrated with step-by-step reasoning to support complex task execution.
How does Zai Glm differ from other models in the Mistral AI lineup?+
While sharing architectural foundations with other Mistral models, Zai Glm is specifically tuned for improved function invocation accuracy and logical chaining, making it more suitable for agent-like workflows that depend on structured tool use.
What are the main limitations to consider when using Zai Glm?+
The model’s effectiveness depends on the quality and clarity of the provided tool definitions; ambiguous or overly complex functions may lead to incorrect invocations. It also requires careful prompt design to maintain reasoning coherence across multiple steps.
How can a team get started with Zai Glm for internal applications?+
Teams can access Zai Glm through Mistral AI’s platform or compatible inference endpoints, then define their available tools using a standard schema. Begin with simple functions to validate behavior before scaling to multi-step processes.
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