DeepSeek V4 Flash

A general-purpose open-source reasoning model. Its distinguishing feature on Sarvam is a 1M token context window — the largest of the open-source models Sarvam serves — paired with visible chain-of-thought and tool calling, at a lower cost per token than GLM-5.2.

At a Glance

Why you might use it

Model Specifications

DeepSeek V4 Flash vs GLM-5.2

Key Capabilities

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from sarvamai import SarvamAI
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client = SarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")
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response = client.open_source_models.chat_completions_v2(
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    model="deepseekv4-flash",
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    messages=[
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        {"role": "user", "content": "Explain the difference between a B-tree and a B+ tree index."}
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    ],
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    temperature=0.2,
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    top_p=1,
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    max_tokens=2000,
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)
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print(response.choices[0].message.content)

Reasoning

DeepSeek V4 Flash thinks before every answer — by default, not only when reasoning_effort is set. The chain-of-thought arrives in a separate reasoning_content field on the message (delta.reasoning_content when streaming); content carries only the final answer. Two practical consequences:

• Reasoning tokens are billed and count against max_tokens. A small budget can be consumed entirely by reasoning, returning content: null with finish_reason: "length". If your answers come back empty, raise max_tokens or turn thinking off.
• Turn thinking off when you set response_format. With reasoning active, a JSON-constrained request returns content: null instead of JSON.

The off-switch goes through extra_body:

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{
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  "model": "deepseekv4-flash",
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  "messages": [{ "role": "user", "content": "..." }],
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  "extra_body": {
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    "chat_template_kwargs": { "enable_thinking": false }
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  }
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}

With thinking off, responses are faster, the whole max_tokens budget goes to the answer, and Structured Outputs work reliably.

Parameters

DeepSeek V4 Flash accepts the standard OpenAI-compatible parameter set — temperature, top_p, max_tokens, stream, stop, n, seed, frequency_penalty, presence_penalty, reasoning_effort, response_format (Structured Outputs), tools, tool_choice, and extra_body. See the full parameter table for types, defaults, and ranges.

Model-specific exceptions:

• image_url content parts are rejected (400) — DeepSeek V4 Flash is text only.
• response_format requires thinking off — see Reasoning above.
• seed is not currently reproducible on this model — identical seeds return different completions. Use temperature: 0 where you need stability.

Limits and errors

Common errors for every model on /v2/chat/completions are documented on the Open-Source Models overview. DeepSeek V4 Flash-specific cases:

Support

Sarvam supports the serving layer — auth, billing, rate limits, availability. Sarvam does not tune or evaluate this model, so its output quality, reasoning, and language coverage are properties of the model itself. See Open-Source Models for the full support boundary, and Talk to us for serving issues.