GPT-OSS 20b and 120b

GPT-OSS Reasoning Control

GPT-OSS models support explicit reasoning control via two parameters:

• thinking_budget – limits how many internal thinking tokens the model can use

• reasoning_effort – qualitative control over how hard the model reasons

These are especially useful for trading off depth vs latency/cost.

---

Parameters

thinking_budget

• Integer

• Upper bound on internal reasoning tokens

• Higher = deeper reasoning, slower & more expensive

reasoning_effort

• "low" | "medium" | "high"

• Controls how deliberate the reasoning style is

• Typically paired with a thinking budget

---

Python (OpenAI client)

extra_params = {
    "extra_body": {
        "custom_params": {"thinking_budget": 40},
        "chat_template_kwargs": {"reasoning_effort": "high"},
    }
}

resp = client.chat.completions.create(
    model="openai/gpt-oss-20b",
    messages=[
        {"role": "user", "content": "Explain why the sky is blue."}
    ],
    **extra_params,
)

---

Direct HTTP (curl)

curl https://api.openai.com/v1/chat/completions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-oss-20b",
    "messages": [
      { "role": "user", "content": "Explain why the sky is blue." }
    ],
    "custom_params": {
      "thinking_budget": 40
    },
    "chat_template_kwargs": {
      "reasoning_effort": "high"
    }
  }'

---

Practical guidance

• Fast / cheap: thinking_budget: 10, reasoning_effort: "low"

• Balanced default: thinking_budget: 25, reasoning_effort: "medium"

• Deep reasoning: thinking_budget: 40+, reasoning_effort: "high"

These controls affect internal reasoning only — the final response stays concise unless you ask otherwise.

Structured outputs (JSON Schema) with GPT-OSS

Goal: force the model to return only valid JSON that matches your schema. On Parasail, you do this by sending response_format.type = "json_schema" (same shape as OpenAI), and—optionally—adding GPT-OSS reasoning knobs via extra_body.

---

Minimal example (Parasail + GPT-OSS)

import os, json
from openai import OpenAI

MODEL = "parasail-gpt-oss-20b-fast"
client = OpenAI(api_key=os.environ["PARASAIL_API_KEY"], base_url="https://api.parasail.io/v1")

PRODUCT_REVIEW_SCHEMA = {
  "type": "object",
  "properties": {
    "rating": {"type": "integer", "minimum": 1, "maximum": 5},
    "pros":   {"type": "array", "items": {"type": "string"}},
    "cons":   {"type": "array", "items": {"type": "string"}},
    "summary":{"type": "string"},
  },
  "required": ["rating", "pros", "cons", "summary"],
  "additionalProperties": False,
}

resp = client.chat.completions.create(
  model=MODEL,
  messages=[{"role":"user","content":"Write a short review of a flagship smartphone."}],
  response_format={
    "type": "json_schema",
    "json_schema": {"name": "product_review", "strict": True, "schema": PRODUCT_REVIEW_SCHEMA},
  },
  # GPT-OSS / Parasail knobs (optional)
  extra_body={
    "custom_params": {"thinking_budget": 50},
    "chat_template_kwargs": {"reasoning_effort": "medium"},
  },
)

data = json.loads(resp.choices[0].message.content)
print(data)

Notes

• strict: True + additionalProperties: False is the combo that keeps output tight.

• Always json.loads(...) the returned message.content and treat that as the source of truth.

• If you stream, you’ll still receive JSON text in delta.content—just concatenate and parse at the end (like your stream_completion() does).