GLM-5.2

A general-purpose open-source model. Its distinguishing feature on Sarvam is a very large 512K token context window — four times the 128K offered by Sarvam’s own chat models — which makes it a reasonable choice for reasoning over long documents or large codebases in a single request.

At a Glance

Why you might use it

Model Specifications

GLM-5.2 vs Sarvam’s chat model

Key Capabilities

1
from sarvamai import SarvamAI
2
3
client = SarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")
4
5
response = client.open_source_models.chat_completions_v2(
6
    model="glm5.2",
7
    messages=[
8
        {"role": "user", "content": "Explain the difference between a B-tree and a B+ tree index."}
9
    ],
10
    temperature=0.2,
11
    top_p=1,
12
    max_tokens=2000,
13
)
14
15
print(response.choices[0].message.content)

Reasoning

GLM-5.2 thinks before every answer — by default, not only when reasoning_effort is set. The chain-of-thought arrives in a separate reasoning_content field on the message (delta.reasoning_content when streaming); content carries only the final answer. Two practical consequences:

• Reasoning tokens are billed and count against max_tokens. A small budget can be consumed entirely by reasoning, returning content: null with finish_reason: "length". If your answers come back empty, raise max_tokens or turn thinking off.
• Turn thinking off when you set response_format. With reasoning active, a JSON-constrained request returns content: null instead of JSON.

The off-switch goes through extra_body:

1
{
2
  "model": "glm5.2",
3
  "messages": [{ "role": "user", "content": "..." }],
4
  "extra_body": {
5
    "chat_template_kwargs": { "enable_thinking": false }
6
  }
7
}

With thinking off, responses are faster, the whole max_tokens budget goes to the answer, and Structured Outputs work reliably.

Parameters

GLM-5.2 accepts the standard OpenAI-compatible parameter set — temperature, top_p, max_tokens, stream, stop, n, seed, frequency_penalty, presence_penalty, reasoning_effort, response_format (Structured Outputs), tools, tool_choice, and extra_body. See the full parameter table for types, defaults, and ranges.

Model-specific exceptions:

• image_url content parts are rejected (400) — GLM-5.2 is text only.
• response_format requires thinking off — see Reasoning above.
• seed is not currently reproducible on this model — identical seeds return different completions. Use temperature: 0 where you need stability.

Limits and errors

Common errors for every model on /v2/chat/completions are documented on the Open-Source Models overview. GLM-5.2-specific cases:

Support

Sarvam supports the serving layer — auth, billing, rate limits, availability. Sarvam does not tune or evaluate this model, so its output quality, reasoning, and language coverage are properties of the model itself. See Open-Source Models for the full support boundary, and Talk to us for serving issues.