DeepSeek V4.1 Flash 1017

DeepSeek V4.1 Flash 1017 is a general-purpose open-weight model served on Sarvam infrastructure. It accepts text and image input and has 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="deepseekv4.1-flash-1017",
    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)

Supported parameters

model: "deepseekv4.1-flash-1017" on Chat Completion V2 and Responses. Full request/response schemas live on those API reference pages; this table covers what’s actually accepted for this model.

400 is invalid_request_error.

Reasoning

DeepSeek V4.1 Flash 1017 has three reasoning modes, low, high, and max, and accepts all three directly on both APIs. It reasons at high when the field is omitted. minimal is served as low, and medium and xhigh are served as high.

This is different from GLM-5.3, where the Responses API rejects the literal string "max", DeepSeek V4.1 Flash 1017 has no such restriction.

Read the reasoning trace from choices[].message.reasoning_content on chat, or from the reasoning item’s content[0].text in output on Responses (summary is filled only when you set reasoning.summary). Reasoning tokens are billed as output tokens either way, and count against max_tokens on chat or max_output_tokens on Responses. Neither has a small default, so set them explicitly.

To turn reasoning off, set reasoning_effort: "none" on chat or reasoning.effort: "none" on Responses. On chat, thinking: {"type": "disabled"} and chat_template_kwargs: {"enable_thinking": false} (through extra_body in the SDKs) also work. Integer effort values from 1 to 100 are accepted as well.

Image input

Send images inline as base64 data URIs only. Remote image URLs are rejected:

import base64
with open("chart.png", "rb") as f:
    data_uri = "data:image/png;base64," + base64.b64encode(f.read()).decode()
response = client.chat.completions_v2(
    model="deepseekv4.1-flash-1017",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Summarise this chart."},
                {"type": "image_url", "image_url": {"url": data_uri}},
            ],
        }
    ],
    max_tokens=2000,
)

Known limitations

• Reasoning and the final answer share the max_tokens / max_output_tokens budget. Raise the cap, or turn thinking off with reasoning_effort: "none", when needed.

Limits and errors