Mercury 3
- Released
- —
- Modality
- —
- Context
- 1M tokens
- Price in / cached input / out
- $0.10 / $0.01 / $0.35per 1M tokens
Quickstart
OpenAI-compatible: change the base URL, and the setup is complete.
from openai import OpenAI
import os
client = OpenAI(
base_url="https://api.you.com/v1",
api_key=os.environ["YOU_API_KEY"],
)
response = client.chat.completions.create(
model="mercury-3",
messages=[{"role": "user", "content": "Summarize this support ticket in two sentences."}],
)
print(response.choices[0].message.content)Specifications
Technical details, capabilities, pricing, and data handling.
Model info
- Input Modality
- —
- Output Modality
- —
- Context Window
- 1M
Capabilities
- Reasoning
- Not supported
- Function calling
- Not supported
- Structured outputs
- Not supported
- Web search grounding
- Not supported
Pricing
- Input
- $0.10
- Cached input
- $0.01
- Output
- $0.35
Data handling
- Zero data retention
- Supported
- No training on customer data
- Supported
- Regions
- US
Providers
Access models through multiple providers with automatic routing and built-in failover.
| Provider | ZDR | No training | Region |
|---|---|---|---|
| You Inference | Yes | Yes | US |
| Inception | Yes | Yes | US |
When to use Mercury 3
Guidance based on the specifications and benchmarks on this page.
Best for
- Latency-sensitive real-time appsRuns at 1,068 tokens per second on NVIDIA GPUs, which Inception says is 4–8x faster than comparable speed-optimized models.
- Fast agent loopsInception positions it for browser-use agents, real-time search agents, and log analysis, where per-step delays compound.
- Voice, chat, and tutoring supportEarly partners use it for customer support and interactive education, with tutoring hints returned in about a second.
Consider alternatives for
- You need image understandingInception's API docs list text as the only supported format for Mercury 3.
- You need self-hosted open weightsMercury 3 is offered through Inception's API and partners; no open weights are published.
- You need maximum frontier intelligenceInception benchmarks it against speed-optimized models, not frontier flagship models.
Benchmarks
Mercury 3 results alongside comparable models
| Benchmark | Mercury 3Inception | Kimi K3Moonshot AI | Qwen3.8 MaxAlibaba | DeepSeek V4 ProDeepSeek | DeepSeek V4.1 FlashDeepSeek | GPT-OSS 120BOpenAI |
|---|---|---|---|---|---|---|
| GPQA Diamond | 89.2% | 93.5% | 92.6% | 90.1% | 90.9% | 80.1% |
| IFBench | 76.8% | Not evaluated | 82.8% | Not evaluated | Not evaluated | Not evaluated |
Scores are self-reported by each model’s developer, so settings may vary between models. Sources: Mercury 3 (release post), Kimi K3 (Hugging Face), Qwen3.8 Max (Hugging Face), Qwen3.8 Max (release post), DeepSeek V4 Pro (Hugging Face), DeepSeek V4.1 Flash (Hugging Face), GPT-OSS 120B (model card).
Give Mercury 3 access to the live web
Ground responses with fresh web information and cited sources using You.com Search, without changing models or adding another integration.
What error do I get installing psycopg2 on Python 3.13?
LLM + You.com Web Search
psycopg2 on Python 3.13
Short answer:
psycopg2 installs fine today. 2.9.10 added 3.13 support in Oct 2024. 12
The error you hit is almost certainly missing build step:
pg_config executable not found
Fix:
pip install -U psycopg2-binary
LLM (LLM only)
When trying to install psycopg2 on Python 3.13, you will most likely hit one of these errors:
1. No pre-built wheel available
pip cannot find a binary wheel because the psycopg2 project hasn’t yet published wheels for Python 3.13.
What you should do
Downgrade Python — use Python 3.12 or earlier until psycopg2 officially supports 3.13.
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