Muse Wonder
Muse Wonder is a frontier multimodal reasoning model from Meta for personal and professional agents, long-horizon coding, and computer use. It combines Active Memory, which carries relevant context across conversations, with Active Focus, which keeps it working through blockers toward a goal.
- Released
- Oct 12 2026
- Modality
- Text + Image + Document + Audio + Video
- Context
- 1M tokens
- Price in / cached input / out
- $4.00 / $0.20 / $20.00per 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="muse-wonder-1.0",
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
- Text, Image, Document, Audio, Video
- Output Modality
- Text
- Context Window
- 1M
Capabilities
- Reasoning
- Not supported
- Function calling
- Supported
- Structured outputs
- Not supported
- Web search grounding
- Not supported
Pricing
- Input
- $4.00
- Cached input
- $0.20
- Output
- $20.00
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 |
When to use Muse Wonder
Guidance based on the specifications and benchmarks on this page.
Best for
- Long-running personal and professional agentsMeta designed it for agentic work across personal tasks and domain knowledge work in law, finance, and health.
- Remembering context across conversationsActive Memory carries context across conversations, so long-running assistants keep track of earlier work.
- Long-horizon software engineeringScores 78.8% on DeepSWE v1.1 and 53.1% on SWE-Marathon at max reasoning, per Meta.
Consider alternatives for
- You need precise 3D spatial reasoningMeta's model card lists visual and spatial reasoning, especially 3D tasks, as a known limitation.
- You need image or audio outputIt accepts text, image, video, and audio input but outputs text only.
- You need from-scratch program buildingOn ProgramBench and Terminal-Bench 4.0, Meta's own table shows Claude Opus 5.5 ahead.
Benchmarks
Muse Wonder results alongside comparable models
| Benchmark | Muse WonderMeta | DeepSeek V4.1 FlashDeepSeek | GLM 5.3Z.ai | Kimi K3Moonshot AI | Muse Spark 1.3Meta | Qwen3.8 MaxAlibaba |
|---|---|---|---|---|---|---|
| DeepSWE v1.1 | 78.8% | 74.2% | 66.9% | 67.5% | 75.4% | 56.6% |
Scores are self-reported by each model’s developer, so settings may vary between models. Sources: Muse Wonder (release post), DeepSeek V4.1 Flash (Hugging Face), GLM 5.3 (Hugging Face), Kimi K3 (Hugging Face), Muse Spark 1.3 (release post), Qwen3.8 Max (Hugging Face), Qwen3.8 Max (release post).
Give Muse Wonder 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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