Mid-tier model
OpenAI GPT-4.1 Mini · llm-mid
Balanced model for moderate reasoning, drafting, data analysis and marketing copy.
- Price per 1M tokens
- $3.00
- $0.0030 per 1k
- Requests · 30d
- 7
- new
- Latency
- 50.4 ms
- measured · declared 900 ms
- Success rate
- 100.0%
- quality prior 0.75
Underlying model
Published by the vendor; the platform bills at the target's declared price.
GPT-4.1 Mini
toolsOpenAI openai/gpt-4.1-mini
GPT-4.1 Mini is a mid-sized model delivering performance competitive with GPT-4o at substantially lower latency and cost. It retains a 1 million token context window and scores 45.1% on hard...
- Input $/1M
- $0.40
- Output $/1M
- $1.60
- Context
- 1.0M
- Max output
- 33K
- Released
- Apr 14, 2025
- Modalities
- imagetextfile
Cached input reads $0.10 per 1M
Traffic over 30 days
Requests per day this target answered on this deployment, with the cost line.
- requests (peak 7)
- failed
- Tokens
- 0
- Spend
- $0
- Previous window
- 0 req
Routed for
Domains the signal layer detected on requests that ended here.
- Marketing3 · 60%
- General2 · 40%
Capabilities
Policy constraints
Hard stops enforced before scoring.
Representative prompts
Examples the similarity strategy matches against.
- Draft a newsletter announcing our new pricing tiers.
- Analyze this CSV of monthly sales and describe the trend.
- Write a short story about a lighthouse keeper.
Call it
Pin this target with model="llm-mid", 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": "llm-mid", "messages": [{"role": "user", "content": "Draft a newsletter announcing our new pricing tiers."}]}'from openai import OpenAI
client = OpenAI(base_url="https://api.opensmartroute.ai/v1", api_key="osr_live_...")
resp = client.chat.completions.create(
model="llm-mid", # pin this target, or "auto" to let the router choose
messages=[{"role": "user", "content": "Draft a newsletter announcing our new pricing tiers."}],
extra_body={"models": ["llm-mid", "auto"]}, # fall back to the router's pick if it fails
)
print(resp.model, resp.choices[0].message.content)