GLM 5.2 - Kyma API

Overview

GLM 5.2 is the flagship of Zhipu’s GLM line on Kyma and where the glm-flagship alias points. Released in June 2026 under the permissive MIT license, it is a mixture-of-experts model with roughly 744B total parameters and ~40B active per token, positioned squarely at coding and agentic workloads. On the independent Intelligence Index v4.1 it scores 51 — the highest of any open-weight model, ahead of DeepSeek V4 Pro, MiniMax-M3, and Kimi K2.6, and ahead of several leading closed models. Through Kyma every call routes with automatic failover across multiple serving paths, so a degraded provider never surfaces as an error, and you reach it with the same OpenAI-compatible key as every other model. The headline change over GLM 5.1 is context: the window grows from ~200K to a full 1M tokens, with output up to 131K. That holds an entire codebase plus an agent’s working history in a single request. Function calling, structured outputs, and extended reasoning are all supported, and implicit prompt caching bills repeated prefixes at a steep discount — which matters for agents that resend a long system prompt on every step.

Specs

Field	Value
Model ID	`glm-5.2`
Best for	Agentic coding, SWE tasks, long-horizon, 1M context
Context window	1M
Max output tokens	131K
Input modalities	Text
Output modalities	Text
Tool calling	Yes
Structured outputs	Yes
Prompt caching	Yes
Speed	Medium
Cost band	Premium
Release stage	Stable

Pricing

	Per 1M tokens
Input	$1.89
Output	$5.94

Use this when

Long-horizon coding agents — Its core design target — multi-step SWE tasks where the agent reads, edits, tests, and iterates across a repository over a long session.
Whole-repository context — The 1M-token window lets an agent load a large codebase and its own history at once, instead of paging context in and out.
Complex reasoning — Extended reasoning mode works through debugging, architecture decisions, and multi-constraint planning before committing to an answer.
Tool-calling pipelines — Function calling plus structured outputs keep multi-step agent loops parseable and on schema, step after step.

Not ideal for

Image inputs (it’s text-only) or latency-sensitive chat UX — it’s a medium-speed, premium-priced model that earns its cost on deep agentic work, not quick interactive replies.

Example

from openai import OpenAI

client = OpenAI(base_url="https://kymaapi.com/v1", api_key="ky-...")

response = client.chat.completions.create(
    model="glm-5.2",
    messages=[{"role": "user", "content": "..."}],
    tools=[...],  # function calling supported
)

FAQ

How is GLM 5.2 different from GLM 5.1? GLM 5.2 expands the context window from ~200K to 1M tokens, raises max output to 131K, and tops the open-weight Intelligence Index. It’s the newer flagship, so the glm-flagship alias now resolves to it; GLM 5.1 stays available by its own model ID. What is the glm-flagship alias? Kyma aliases let you write integrations that don’t hardcode a model ID. Sending model glm-flagship currently resolves to GLM 5.2, and the X-Kyma-Model header on every response tells you exactly which model ran. Why run GLM 5.2 through Kyma? One API key and one OpenAI-compatible endpoint cover this and every other model on the platform. You get automatic failover when a serving path degrades, prompt caching discounts on repeated prefixes — a big deal for agents resending long system prompts — exact per-request cost in usage.cost, and $0.50 free credit to try it with no card.

Alias	Resolves to
`glm-flagship`	`glm-5.2`

​Overview

​Specs

​Pricing

​Use this when

​Not ideal for

​Example

​FAQ

​Related aliases