Configure reasoning tiers and continue tool calls

One-sentence takeaway

Agent tasks with DeepSeek-V4-Flash should explicitly select the low/high/max thinking level and pass reasoning_content back unchanged across tool-call turns; otherwise, the API may return a 400 error or lose the continuity of its reasoning state.

Use cases

• Suitable tasks: code agents; retrieval/execution loops that require function calls; and long-running tasks that need to balance cost and latency.

• Unsuitable tasks: workflows that require image or audio input; both the official update and empirical materials describe V4-Flash as a text model.

• Applicable model version: deepseek-v4-flash, especially for the 2026-07-31 0731 API update.

• Applicable client, agent, or API: DeepSeek OpenAI-compatible Chat Completions; tool-call loops.

• Recommended reasoning levels and parameters: low for simple tasks; high for everyday agents; and max for complex tasks. In thinking mode, do not rely on temperature, top_p, presence_penalty, or frequency_penalty; the official documentation says these parameters have no effect.

Ready-to-use content

The following is a minimal configuration template adapted for V4-Flash from the official parameter table. Field names and status mappings follow the official definitions; the model name has been changed from Pro in the documentation example to the model used in this directory:

from openai import OpenAI

client = OpenAI(
    api_key="<DeepSeek API Key>",
    base_url="https://api.deepseek.com",
)

response = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[
        {"role": "user", "content": "Check the input first, call any necessary tools, and finally provide a verifiable conclusion."}
    ],
    reasoning_effort="high",  # low / high / max
    extra_body={"thinking": {"type": "enabled"}},
)
print(response.choices[0].message.content)

The corresponding approach in the official Responses API is reasoning.effort="none" | "low" | "high" | "max"; none disables thinking. In the OpenAI format, the thinking switch must be placed in extra_body.

Test/workflow steps

1. Start a request with model="deepseek-v4-flash", choosing reasoning_effort according to the task's complexity.

2. When tools are needed, retain the complete assistant message from the previous turn in subsequent requests instead of retaining only content.

3. Read tool_calls, execute the local tools, and append each result to messages with role="tool" and the corresponding tool_call_id.

4. Continue requesting until there are no new tool_calls; in the end, return only content as the user's answer.

5. Validate external data separately for important tasks; the model level controls reasoning investment but does not replace verification of tool results.

Raw evidence and data

• The official update page says that V4-Flash 0731 is a public beta with the same model architecture and size as V4-Flash-Preview, but with post-training performed again; the official Agent results include Terminal Bench 2.1 82.7, DeepSWE 54.4, Toolathlon Verified 70.3, and Automation Bench Public 25.1.

• The same update page specifies the official harness conditions: DeepSeek Harness minimal mode, max effort, top_p=0.95, and temperature=1.0; these are official test conditions and should not be treated directly as default values for arbitrary clients.

• The thinking-mode documentation explicitly states that the requested-effort mappings for V4-Flash and V4-Pro are low→low, medium→high, high→high, xhigh→high, and max→max; thinking is enabled by default, with the default effective level set to high.

• During tool calls, the official documentation warns that if the complete reasoning_content is not passed back, the API will return 400; in ordinary multi-turn conversations without tools, the previous turn's reasoning_content does not need to be appended back.

Applicability boundaries

• medium and xhigh do not produce separate effective levels with the same names on V4-Flash; both map to high. Use max when the greatest reasoning investment is needed.

• Thinking mode does not support tuning randomness with sampling parameters; the documentation says these parameters have no effect even if they are accepted.

• The documentation examples include visible reasoning_content; here, it is treated only as an API state field and should not be treated as a reliable fact about the model's internal reasoning or shown directly to end users.

• The 0731 benchmarks use the official harness; different agent orchestration, tool descriptions, and context-compression strategies may change the results.

Source excerpts or observations (compliance short quotes only)

The key original wording on the official update page is “DeepSeek-V4-Flash-0731 keeps the same model architecture and size ... and was only re-post-trained”; the thinking-mode page also requires that after a tool request, “reasoning_content must be fully passed back”.