DeepSeek V4 Flash

DeepSeek V4 Flash is a general-purpose open-weight model served on Sarvam infrastructure. 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="deepseekv4-flash",
    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: "deepseekv4-flash". Request fields are defined in those API references, not duplicated on this model page.

Reasoning

DeepSeek V4 Flash has three reasoning modes, low, high, and max, and accepts all three directly on both APIs:

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

Read the reasoning trace from choices[].message.reasoning_content on chat, or from the reasoning item’s summary[0].text in output on Responses when one is returned. 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).

To turn reasoning off on chat, set extra_body.chat_template_kwargs.enable_thinking to false.

Known limitations

• Reasoning and the final answer share the max_tokens / max_output_tokens budget. Raise the cap or disable thinking via extra_body.chat_template_kwargs.enable_thinking: false when needed.
• Structured output with reasoning enabled can return content: null, disable thinking or raise the token cap.

Limits and errors