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
- 0
- no traffic
- Latency
- 1800 ms
- declared
- Success rate
- n/a
- 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.
- Tokens
- 0
- Spend
- $0
- Previous window
- 0 req
Routed for
Domains the signal layer detected on requests that ended here.
No routed requests yet. Declared domains: General, Marketing, Finance.
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.
Route to it
This llm is not executed by the platform. Ask the router for a decision restricted to kinds=["llm"] and dispatch the winner in your own stack; send feedback afterwards so the learners improve.
curl https://api.opensmartroute.ai/api/v1/route \
-H "Authorization: Bearer $OSR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"text": "Explain the trade-offs between speculative decoding and batching for a 20B model on one GPU.", "kinds": ["llm"], "top_k": 3}'
# Returns the chosen target, confidence, the ranked alternatives and the
# per-strategy trace. Execution of llm targets happens in your stack.import httpx
r = httpx.post(
"https://api.opensmartroute.ai/api/v1/route",
headers={"Authorization": "Bearer osr_live_..."},
json={"text": "Explain the trade-offs between speculative decoding and batching for a 20B model on one GPU.", "kinds": ["llm"]},
)
d = r.json()
print(d["target"]["id"], d["confidence"])
print([t["id"] for t in d["ranked"]]) # e.g. gpt-oss first when it fits