Responses API

The Responses API provides an OpenAI-compatible interface for text generation, reasoning, structured output, and tool calling. Streaming is optional: the same pattern as Chat Completion V2: omit stream or set it to false for one JSON response (default).

Create a response

Use POST /v2/responses. The endpoint is stateless: send the complete conversation in input on every request. Omit store or set it to false (true returns 400).

Nothing is stored for later retrieval, GET /v2/responses/{id} returns 404. GET /v2/responses and DELETE /v2/responses/{id} return 405 (documented in the API Reference alongside POST /v2/responses).

from sarvamai import SarvamAI
client = SarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")
response = client.responses.create(
    model="glm5.3",
    input="Explain database indexing in three short points.",
    store=False,
)
print(response.output)

Stream a response (optional)

Set stream to true when you want named server-sent events instead of one JSON body. Streams end with response.completed, response.incomplete, or response.failed; they do not emit a [DONE] sentinel. On glm5.3, streamed items can nest: pair events by item_id. Usage appears on the terminal event (there is no stream_options).

from sarvamai import SarvamAI
client = SarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")
stream = client.responses.create(
    model="glm5.3",
    input="Explain database indexing in three short points.",
    store=False,
    stream=True,
)
for event in stream:
    print(event.type)

Continue a conversation

The endpoint does not store response state. To continue a conversation, send the prior messages and outputs again in input. Do not use previous_response_id or conversation , both return 400.

from sarvamai import SarvamAI
client = SarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")
response = client.responses.create(
    model="gemma4",
    input=[
        {"role": "user", "content": "Name one advantage of a B-tree index."},
        {"role": "assistant", "content": "It keeps lookups efficient as data grows."},
        {"role": "user", "content": "When should I avoid one?"},
    ],
    store=False,
)

For message parts in input, text uses {"type": "input_text", "text": "…"} (not chat completions’ {"type": "text"}).

Reasoning (glm5.3, deepseekv4-flash)

Both reasoning models support three modes, low, high, and max, and Chat Completion V2 accepts all three directly for both. On this route (Responses), the two models differ:

Chat trace: choices[].message.reasoning_content.

Responses trace: on glm5.3, a reasoning item is always in output. Read summary[0].text (content is null). Optional reasoning.summary: auto or detailed.

Reasoning tokens count toward max_output_tokens, which has no default cap on this route, omitting it means unbounded generation. On chat, the equivalent budget is max_tokens, which does default to 2048 when omitted. See the Chat Completion V2 API Reference.

Defaults when omitted (glm5.3)

Parameters

Refused or unsupported on this route

These return 400 (the error names the field): store: true, background: true, previous_response_id, conversation, max_tool_calls, prompt, item_reference.

On Responses, logprobs, top_logprobs, min_p, and repetition_penalty may be accepted and echoed without changing output. On Chat Completion V2, logprobs and top_logprobs are supported on sarvam-105b and gemma4. They aren’t supported on glm5.3, which returns a clean 400, or on deepseekv4-flash, which returns a 503 model_overloaded instead of a clean rejection, omit the field for that model. See Chat Completion V2 for details.

See the Responses API Reference for the complete request, response, error, and streaming-event schemas. Model-specific limits for glm5.3 are also summarized on the GLM-5.3 model page.