GLM-5.3

GLM-5.3 is a large-scale reasoning model built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1,048,576-token context window.

At a glance

Key capabilities

Quickstart

from sarvamai import SarvamAI
client = SarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")
response = client.chat.completions_v2(
    model="glm5.3",
    messages=[
        {
            "role": "user",
            "content": "Explain the difference between a B-tree and a B+ tree index.",
        }
    ],
    temperature=1,
    top_p=0.95,
    max_tokens=2000,
)
print(response.choices[0].message.content)

API reference

Call POST /v2/chat/completions or POST /v2/responses with model: "glm5.3". Request fields (n, logprobs, parallel_tool_calls, stream, and the rest) are defined in those API references, not duplicated on this model page.

Reasoning

GLM-5.3 has three reasoning modes, low, high, and max, but which values you can pass depends on the API:

Read the reasoning trace from choices[].message.reasoning_content on chat, or from the reasoning item’s summary[0].text in output on Responses (its content field is null there). Reasoning tokens are billed as output tokens either way, and count against max_tokens on chat (defaults to 2048 when omitted) or max_output_tokens on Responses (no default, omitting it means unbounded generation).

Response fields (chat)

Full response shape: Chat Completion V2.

Known limitations

• Output token budget. max_tokens on chat defaults to 2048 when omitted; max_output_tokens on Responses has no default and allows unbounded generation. Reasoning counts against whichever budget applies, so a small max_tokens on chat can leave little or no room for the actual answer.
• Parallel tool calls. GLM-5.3 only respects parallel_tool_calls: true (or omitting the field). Passing false is accepted without error, but the model can still return more than one tool call in a turn.
• Context-length checks are approximate, and more conservative for Latin-script text than for Indic scripts. Treat the model’s own limit as authoritative.

Parameter errors (n, logprobs, stop, and similar) are documented on Chat Completion V2.

Limits and errors