DeepSeek V4.1 FlashOpen
DeepSeek V4.1 Flash is an open-weights MoE reasoning model with 552B total (16B active) parameters, 1.0M context, and chain-of-thought problem solving.
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Radar axes are approximated from category scores where direct benchmark measurements are missing — see Methodology.
Strengths & Trade-offs
Where it wins
- Open weights — self-host, fine-tune and audit with no vendor lock-in
- 1.0M context — swallows whole repos, codebases or corpora in one pass
- 214 tok/s output — fast enough for real-time agents
- Very low blended price — $0.18/1M mixed tokens
Killer Use Cases
Developer Quickstart & API
DEEPSEEK_API_KEY first — get an API keycurl https://api.deepseek.com/v1/chat/completions \
-H "Authorization: Bearer $DEEPSEEK_API_KEY" \
-H "content-type: application/json" \
-d '{
"model": "deepseek-v4-1-flash",
"messages": [{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}]
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.deepseek.com/v1",
api_key=os.environ["DEEPSEEK_API_KEY"],
)
resp = client.chat.completions.create(
model="deepseek-v4-1-flash",
messages=[{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}],
)
print(resp.choices[0].message.content)const res = await fetch("https://api.deepseek.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.DEEPSEEK_API_KEY}`,
"content-type": "application/json",
},
body: JSON.stringify({
model: "deepseek-v4-1-flash",
messages: [{ role: "user", content: "Refactor this function for O(n) time and explain the change." }],
}),
});
console.log((await res.json()).choices[0].message.content);Official Resources & Links
Compare hosts & latency
Throughput, TTFT and cache pricing across Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock and Azure.
Scores by Source
Category Scores
Benchmark Results
Live Specs & Architecture
- Switchable reasoning effort — trading latency and tokens for accuracy on hard problems.
- Prompt caching supported — cached input reads at $0.03/1M tokens.
Estimated monthly bill
Cite & embed
Loading badge markdown...@misc{llmpodium2026deepseekv41flash,
title = {Benchmark Analysis and Intelligence Evaluation of DeepSeek V4.1 Flash},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/deepseek-v4-1-flash}
}Embed Score Badge
Add a live Podium Score badge to your GitHub or Hugging Face README — it links back to the live ranking.
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