Reasoning

Control extended thinking for reasoning-capable models

Some hosted models support extended thinking. The model generates reasoning tokens before its final answer and returns them in reasoning_content, separate from the final content.

Supported models

Model	Slug	Reasoning
DeepSeek V4 Pro 0813	deepseek-ai/DeepSeek-V4-Pro-0813	Enabled by default
DeepSeek V4 Flash 0731	deepseek-ai/DeepSeek-V4-Flash-0731	Enabled by default
DeepSeek V4.1 Flash	deepseek-ai/DeepSeek-V4.1-Flash	Enabled by default
DeepSeek V4.1 Flash Fast	deepseek-ai/DeepSeek-V4.1-Flash-Fast	Enabled by default
OpenAI GPT 120B	openai/gpt-oss-120b	Enabled by default
Kimi K3	moonshotai/Kimi-K3	Enabled by default
GLM 5.2	zai-org/GLM-5.2	Opt-in through chat_template_args
GLM 5.2 Fast	zai-org/GLM-5.2-Fast	Enabled by default
GLM 5.3	zai-org/GLM-5.3	Enabled by default
GLM 5.3 Fast	zai-org/GLM-5.3-Fast	Enabled by default
GLM 5.3 Flash	zai-org/GLM-5.3-Flash	Enabled by default
Nemotron 3.5 Super VL	nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B	Opt-in through reasoning_effort
Nemotron Ultra	nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B	Opt-in through chat_template_args

Models not listed here don’t support reasoning.

Enable thinking

For GLM 5.2 and Nemotron Ultra, enable thinking by passing chat_template_args. For Nemotron 3.5 Super VL, set reasoning_effort to high.

Python

Pass chat_template_args through extra_body since it extends the standard OpenAI API:

enable_thinking.py

response = client.chat.completions.create(
    model="zai-org/GLM-5.2",
    messages=[{"role": "user", "content": "What is the sum of the first 100 prime numbers?"}],
    extra_body={"chat_template_args": {"enable_thinking": True}},
    max_tokens=4096,
    stream=True,
)

JavaScript

Include chat_template_args directly in the request options:

enable_thinking.js

const response = await client.chat.completions.create({
    model: "zai-org/GLM-5.2",
    messages: [{ role: "user", content: "What is the sum of the first 100 prime numbers?" }],
    chat_template_args: { enable_thinking: true },
    max_tokens: 4096,
    stream: true,
});

cURL

Include chat_template_args in the JSON request body:

Request

curl https://inference.baseten.co/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -d '{
    "model": "zai-org/GLM-5.2",
    "messages": [{"role": "user", "content": "What is the sum of the first 100 prime numbers?"}],
    "chat_template_args": {"enable_thinking": true},
    "max_tokens": 4096,
    "stream": false
  }'

Control reasoning depth

Set the top-level reasoning_effort parameter to control how much reasoning a supported model performs. Only the models in this table apply the parameter.

Model	Supported values
DeepSeek V4 Pro 0813	none, low, high, max
DeepSeek V4 Flash 0731	none, minimal, low, medium, high (default), xhigh, max
DeepSeek V4.1 Flash	none, high (default), max
DeepSeek V4.1 Flash Fast	none, high (default), max
Kimi K3	none, low, high, max (default)
OpenAI GPT 120B	none, minimal, low, medium (default), high, xhigh, max
GLM 5.2	none, low, high, max
GLM 5.2 Fast	none, low, high, max
GLM 5.3	none, low, high (default), max
GLM 5.3 Fast	none, low, high (default), max
GLM 5.3 Flash	none, low, high (default), max
Nemotron 3.5 Super VL	none (default), minimal, low, medium, high, xhigh, max
Nemotron Ultra	none, high

Nemotron 3.5 Super VL defaults to non-thinking responses. Set reasoning_effort to high to enable reasoning, or none to turn it off. The other accepted values also enable reasoning. Setting only chat_template_args.enable_thinking: true returns HTTP 400; use reasoning_effort instead.

Lower reasoning depths reduce reasoning-token usage and latency. Higher depths use more output tokens for reasoning. Accepted values can map to the same depth. Set reasoning_effort to none to request a direct answer.

OpenAI GPT 120B can still return reasoning_content when you set reasoning_effort to none. On Nemotron Ultra, none disables reasoning and high enables it. DeepSeek V4.1 Flash and DeepSeek V4.1 Flash Fast do not support low; a request with low still reasons at the default depth, so use high, max, or none.

Baseten treats reasoning_effort: "none" as low for GLM 5.3, GLM 5.3 Fast, and GLM 5.3 Flash. Setting none doesn’t disable reasoning. These models also accept chat_template_args.enable_thinking: false. Responses can include reasoning_content with either setting.

Python

Pass reasoning_effort through extra_body since it extends the standard OpenAI API:

reasoning_effort.py

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://inference.baseten.co/v1",
    api_key=os.environ["BASETEN_API_KEY"],
)

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4-Flash-0731",
    messages=[{"role": "user", "content": "What is the sum of the first 100 prime numbers?"}],
    extra_body={"reasoning_effort": "high"},
)

print(response.choices[0].message.content)

JavaScript

Include reasoning_effort directly in the request options:

reasoning_effort.js

import OpenAI from "openai";

const client = new OpenAI({
    baseURL: "https://inference.baseten.co/v1",
    apiKey: process.env.BASETEN_API_KEY,
});

const response = await client.chat.completions.create({
    model: "deepseek-ai/DeepSeek-V4-Flash-0731",
    messages: [{ role: "user", content: "What is the sum of the first 100 prime numbers?" }],
    reasoning_effort: "high",
});

console.log(response.choices[0].message.content);

cURL

Include reasoning_effort in the JSON request body:

Request

curl https://inference.baseten.co/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -d '{
    "model": "deepseek-ai/DeepSeek-V4-Flash-0731",
    "messages": [{"role": "user", "content": "What is the sum of the first 100 prime numbers?"}],
    "reasoning_effort": "high"
  }'

Use reasoning for mathematical calculations, multi-step logic, code generation with complex requirements, and analysis that requires several intermediate steps.

For simple questions or text generation, reasoning adds latency and output tokens. Use a model without reasoning support or set reasoning_effort to low.

Parse the response

The message object returns reasoning in reasoning_content and the final answer in content.

Python

Read reasoning_content and content directly off the message object:

parse_reasoning.py

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://inference.baseten.co/v1",
    api_key=os.environ["BASETEN_API_KEY"],
)

response = client.chat.completions.create(
    model="zai-org/GLM-5.2",
    messages=[{"role": "user", "content": "Is 91 a prime number? Answer in one sentence."}],
    extra_body={"chat_template_args": {"enable_thinking": True}},
)

message = response.choices[0].message
print("Reasoning:", message.reasoning_content)
print("Answer:", message.content)

JavaScript

Read reasoning_content and content from the returned message:

parse_reasoning.js

import OpenAI from "openai";

const client = new OpenAI({
    baseURL: "https://inference.baseten.co/v1",
    apiKey: process.env.BASETEN_API_KEY,
});

const response = await client.chat.completions.create({
    model: "zai-org/GLM-5.2",
    messages: [{ role: "user", content: "Is 91 a prime number? Answer in one sentence." }],
    chat_template_args: { enable_thinking: true },
});

const message = response.choices[0].message;
console.log("Reasoning:", message.reasoning_content);
console.log("Answer:", message.content);

cURL

Pipe the response through jq to extract each field:

Request

curl https://inference.baseten.co/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -d '{
    "model": "zai-org/GLM-5.2",
    "messages": [{"role": "user", "content": "Is 91 a prime number? Answer in one sentence."}],
    "chat_template_args": {"enable_thinking": true}
  }' | jq '.choices[0].message | {reasoning: .reasoning_content, answer: .content}'

The response body contains both fields on the assistant message:

Response

{
  "choices": [
    {
      "message": {
        "role": "assistant",
        "reasoning_content": "The user is asking whether 91 is a prime number... 91 = 7 × 13, so it is not prime...",
        "content": "No, 91 is not a prime number because it can be factored as $7 \\times 13$."
      }
    }
  ],
  "usage": {
    "prompt_tokens": 21,
    "completion_tokens": 203,
    "total_tokens": 224
  }
}

Baseten includes reasoning tokens in completion_tokens and counts them toward your total usage and billing.

Set a reasoning token budget

max_tokens caps the whole completion, reasoning tokens included. reasoning.max_tokens caps reasoning tokens alone: once the model has generated that many reasoning tokens, it stops thinking and spends the rest of max_tokens on its final answer. Set max_tokens above the reasoning budget so the model has room to answer.

DeepSeek V4 Flash 0731 enforces reasoning.max_tokens; other reasoning models ignore it. On DeepSeek V4 Flash 0731, reasoning stops at 4,096 tokens by default, including at reasoning_effort: max. Raise the budget when a hard problem needs more thinking room:

Python

Pass reasoning through extra_body since it extends the standard OpenAI API:

reasoning_budget.py

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://inference.baseten.co/v1",
    api_key=os.environ["BASETEN_API_KEY"],
)

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4-Flash-0731",
    messages=[{"role": "user", "content": "What is the sum of the first 100 prime numbers?"}],
    extra_body={"reasoning": {"max_tokens": 20000}},
    max_tokens=24000,
)

print(response.choices[0].message.content)

JavaScript

Include reasoning directly in the request options:

reasoning_budget.js

import OpenAI from "openai";

const client = new OpenAI({
    baseURL: "https://inference.baseten.co/v1",
    apiKey: process.env.BASETEN_API_KEY,
});

const response = await client.chat.completions.create({
    model: "deepseek-ai/DeepSeek-V4-Flash-0731",
    messages: [{ role: "user", content: "What is the sum of the first 100 prime numbers?" }],
    reasoning: { max_tokens: 20000 },
    max_tokens: 24000,
});

console.log(response.choices[0].message.content);

cURL

Include reasoning in the JSON request body:

Request

curl https://inference.baseten.co/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -d '{
    "model": "deepseek-ai/DeepSeek-V4-Flash-0731",
    "messages": [{"role": "user", "content": "What is the sum of the first 100 prime numbers?"}],
    "reasoning": {"max_tokens": 20000},
    "max_tokens": 24000
  }'

Next steps

Model APIs overview

Review supported models, pricing, and features.

Structured outputs

Constrain reasoning model output to a JSON schema.