DeepSeek V4.1 Flash
DeepSeek V4.1 Flash model for reasoning and agentic coding
- Released
- Sep 10 2026
- Modality
- Text + Image
- Context
- 1M tokens
- Price in / cached input / out
- $0.30 / $0.007 / $1.20per 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="deepseek-v4.1-flash",
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
- Output Modality
- Text
- Context Window
- 1M
- Parameters
- 552B total / 8B active (prefill), 16B active (decode)
- License
- MIT
Capabilities
- Reasoning
- Not supported
- Function calling
- Supported
- Structured outputs
- Supported
- Web search grounding
- Available with You.com Search
Pricing
- Input
- $0.30
- Cached input
- $0.007
- Output
- $1.20
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 |
| Parasail | Yes | Yes | US |
| Wafer | Yes | Yes | US |
When to use DeepSeek V4.1 Flash
Guidance based on the specifications and benchmarks on this page.
Best for
- Cost-efficient agentic codingLeads DeepSeek's comparison table on Terminal-Bench 2.1 (90.6%) and DeepSWE v1.1 (74.2%) at max reasoning effort.
- Input-heavy, long-context agentsActivates only 8B parameters during prefill and keeps a small KV cache, so 1M-token agent sessions stay cheap.
- Image and text understandingNatively processes images and text together, with a vision encoder trained jointly from the start of pre-training.
Consider alternatives for
- You need the strongest knowledge recallDeepSeek's own base-model comparison shows V4 Pro well ahead on knowledge tests such as SimpleQA-Verified.
- You need top hard-terminal performanceOn Terminal-Bench 3.0 and 4.0, DeepSeek's own table shows Opus 5.0 scoring higher.
- You need top security exploitation resultsOn SEC-Bench Pro and ExploitGym, DeepSeek's own table shows GPT-5.6 Sol ahead.
Benchmarks
DeepSeek V4.1 Flash results alongside comparable models
| Benchmark | DeepSeek V4.1 FlashDeepSeek | GLM 5.3Z.ai | Qwen3.8 MaxAlibaba | Kimi K3Moonshot AI | DeepSeek V4 ProDeepSeek | GPT-OSS 120BOpenAI |
|---|---|---|---|---|---|---|
| GPQA Diamond | 90.9% | Not evaluated | 92.6% | 93.5% | 90.1% | 80.1% |
| Humanity's Last Exam | 36.8% | Not evaluated | 43.6% | 43.5% | 37.7% | 14.9% |
| Humanity's Last Exam (with tools) | 63.9% | 62.5% | 56.2% | 56% | 48.2% | 19% |
Scores are self-reported by each model’s developer, so settings may vary between models. Sources: DeepSeek V4.1 Flash (Hugging Face), GLM 5.3 (Hugging Face), Qwen3.8 Max (Hugging Face), Qwen3.8 Max (release post), Kimi K3 (Hugging Face), DeepSeek V4 Pro (Hugging Face), GPT-OSS 120B (model card).
Give DeepSeek V4.1 Flash 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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