Migrating Chat Completion from OpenRouter to Sarvam

Last updated: October 23, 2026

This guide covers moving OpenRouter Chat Completions and Responses calls for open-weight models to Sarvam’s /v2 API.

Migration checklist

1. Create an API key in the Sarvam dashboard.
2. Change base_url to https://api.sarvam.ai/v2 and api_key to your Sarvam key.
3. Replace each model ID using the model ID table.
4. Replace the reasoning object with reasoning_effort (Reasoning).
5. Remove OpenRouter-only fields: provider, models, plugins, usage, session_id, and others (list).
6. Read reasoning from reasoning_content instead of reasoning.
7. Replace models fallbacks with retries on 429 and 503 (full example).
8. Set max_tokens explicitly, then run your tests and watch for 400 responses that name a field.

Quick start

from openai import OpenAI
client = OpenAI(
    api_key="YOUR_OPENROUTER_API_KEY",
    base_url="https://openrouter.ai/api/v1",
)
response = client.chat.completions.create(
    model="moonshotai/kimi-k3",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Suggest a name for a coffee shop in Bengaluru."},
    ],
    max_tokens=2000,
    extra_body={
        "reasoning": {"effort": "low"},
        "provider": {"sort": "throughput"},
    },
)
print(response.choices[0].message.content)

Reasoning

With the openai Python package, read the field with getattr, since it isn’t part of the package’s typed response:

reasoning = getattr(response.choices[0].message, "reasoning_content", None)

OpenRouter-only fields to remove

Sarvam validates request fields and returns 400 invalid_request_error with Unsupported parameter(s) for fields it doesn’t support, so leftover OpenRouter options fail loudly instead of being ignored.

Parameter concept map

Responses API

Both platforms’ Responses APIs are stateless. For base URL and field changes, reasoning items, function tools, and server-side tools, see Migrating the Responses API from OpenRouter.

What’s different by design

• One provider, no routing layer. Sarvam runs each model on its own infrastructure, so there are no upstream providers to rank, pin, or fail over between, and a model’s behaviour doesn’t change from request to request.
• Strict request validation. Unsupported fields return 400 with the field named, instead of being dropped silently. This catches stale options early. gemma4 is less strict: it accepts some fields, such as prediction and logit_bias, and ignores them.
• Reasoning in reasoning_content. Sarvam uses the same field name as many open-model servers, rather than OpenRouter’s reasoning and reasoning_details.
• Billing in your Sarvam credits. Usage is metered per token against your Sarvam account. See Pricing.

Full example

A wrapper that replaces OpenRouter’s models fallback list with retries and a fallback model in your own code:

from openai import OpenAI
client = OpenAI(
    api_key="YOUR_OPENROUTER_API_KEY",
    base_url="https://openrouter.ai/api/v1",
)
def ask(prompt: str) -> str:
    response = client.chat.completions.create(
        model="moonshotai/kimi-k3",
        messages=[{"role": "user", "content": prompt}],
        max_tokens=2000,
        extra_body={
            "models": ["moonshotai/kimi-k3", "z-ai/glm-5.3"],
            "reasoning": {"effort": "low"},
        },
    )
    return response.choices[0].message.content

Common mistakes

See also

• Migration overview: base URL, auth, and model IDs
• Chat Completion overview
• Responses API guide
• Open-Weight Models
• Errors & Troubleshooting