Sarvam AI
DeepSeek

DeepSeek V4.1 Flash 1017

Combine a million-token context window with reasoning and tool calling for document-intensive applications and agent workflows. Call it with model ID deepseekv4.1-flash-1017.

  • Reasoning
  • Coding
  • Tool calling
  • Long context
  • Vision

Best suited for

  1. 01

    Document-intensive agents

    Reason over extensive source material while keeping the tool definitions and conversation history in the same request.

  2. 02

    Visual + text workflows

    Send inline images alongside instructions for extraction, layout understanding and multimodal tool use.

  3. 03

    Multi-step automation

    Connect reasoning to application tools for tasks that require decisions, external results and follow-up actions.

Build with the Sarvam SDK

pip install --upgrade sarvamaiV2 · Sarvam SDK · Streaming
import osfrom sarvamai import SarvamAI​client = SarvamAI(api_subscription_key=os.environ["SARVAM_API_KEY"])​stream = client.chat.completions_v2(    model="deepseekv4.1-flash-1017",    messages=[{"role": "user", "content": "Explain why time to first token matters for a chat application briefly."}],    max_tokens=8000,    stream=True,)​for chunk in stream:    if not chunk.choices:        continue    text = chunk.choices[0].delta.content    if text:        print(text, end="", flush=True)
  1. 01 Install the Sarvam SDK

    Install sarvamai for Python or JavaScript. Store your key in SARVAM_API_KEY.

  2. 02 Choose your model

    Use model ID deepseekv4.1-flash-1017. The SDK calls https://api.sarvam.ai/v2 for this example.

    V2 Chat Completions. High reasoning is the default. Reasoning tokens count toward the 8,000-token output budget.

  3. 03 Validate your application

    Check output quality, supported parameters and tool behaviour on representative requests before moving production traffic.

Model API Reference

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From model choice to production. Bring your workload, traffic expectations and latency targets. Build on Sarvam’s serving stack in India.

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