gpt-oss-20b (self-hosted)
OpenAI gpt-oss-20b · gpt-oss
OpenAI's open-weight 20B reasoning model under Apache-2.0, served on our own hardware - strong general reasoning, coding and agentic tool use without data leaving the deployment.
- Price per 1M tokens
- $0.40
- $0.0004 per 1k
- Requests · 30d
- 1
- new
- Latency
- 29.4 ms
- measured · declared 1800 ms
- Success rate
- 100.0%
- quality prior 0.78
Underlying model
Published by the vendor; the platform bills at the target's declared price.
gpt-oss-20b
reasoning open weights toolsOpenAI openai/gpt-oss-20b
gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for...
- Input $/1M
- $0.03
- Output $/1M
- $0.13
- Context
- 131K
- Max output
- 118K
- Released
- Aug 5, 2025
- Modalities
- text
Cached input reads $0.03 per 1M · weights on Hugging Face as openai/gpt-oss-20b
Traffic over 30 days
Requests per day this target answered on this deployment, with the cost line.
- requests (peak 1)
- failed
- Tokens
- 0
- Spend
- $0
- Previous window
- 0 req
Routed for
Domains the signal layer detected on requests that ended here.
- General1 · 100%
Capabilities
Policy constraints
Hard stops enforced before scoring.
Representative prompts
Examples the similarity strategy matches against.
- Explain the trade-offs between speculative decoding and batching for a 20B model on one GPU.
- Draft a technical blog post comparing three open-weight models for on-prem agents.
- Plan the steps an agent needs to migrate a Flask app to FastAPI.
Call it
Pin this target with model="gpt-oss", or send a candidate list and let the router choose and fall back.
curl https://api.opensmartroute.ai/v1/chat/completions \
-H "Authorization: Bearer $OSR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model": "gpt-oss", "messages": [{"role": "user", "content": "Explain the trade-offs between speculative decoding and batching for a 20B model on one GPU."}]}'from openai import OpenAI
client = OpenAI(base_url="https://api.opensmartroute.ai/v1", api_key="osr_live_...")
resp = client.chat.completions.create(
model="gpt-oss", # pin this target, or "auto" to let the router choose
messages=[{"role": "user", "content": "Explain the trade-offs between speculative decoding and batching for a 20B model on one GPU."}],
extra_body={"models": ["gpt-oss", "auto"]}, # fall back to the router's pick if it fails
)
print(resp.model, resp.choices[0].message.content)