DeepSeek 4.1: Flash Models, Pricing and API Code (2026)

DeepSeek 4.1 is what most people type when they mean DeepSeek V4.1-Flash, the fast general and coding model DeepSeek released in September 2026 and updated in October as V4.1-Flash-1017. This post covers what the model is, what the 1017 update changed, how the three builds you can call differ, what real workloads cost, and the code to call it with tools and adjustable reasoning.

What is DeepSeek 4.1?

DeepSeek is a Chinese AI company based in Hangzhou that builds large language models (LLMs): neural networks trained on large amounts of text to read and write language and code. Its V-series has moved through several generations, and V4.1 is the current one. The model released under that name is V4.1-Flash, the fast, cheaper tier of the family.

DeepSeek released V4.1-Flash around September 10, 2026 (Beijing time). It describes the model as a multimodal Mixture-of-Experts (MoE) model with 552B backbone parameters. MoE means the network is split into many specialised sub-networks, called experts, and a router sends each token to only a few of them. You get the knowledge of a very large model while paying the compute cost of a much smaller one per token. That is the main reason MoE models like this can be fast and cheap at the same time.

On Unicorn API, DeepSeek 4.1 is available in three builds, all with the same request shape:

• DeepSeek V4.1 Flash (id deepseek-v4-1-flash), added on September 23, 2026.
• DeepSeek V4.1 Flash FP4 (id deepseek-v4-1-flash-fp4), added on October 18, 2026. The lowest price of the three.
• DeepSeek V4.1 Flash FP8 (id deepseek-v4-1-flash-fp8), added on October 18, 2026. Closer to the full-precision model.

The core capabilities are the same across the FP4 and FP8 listings: a one-million-token context window, tool calling (the model can ask your code to run a function and use the result), and adjustable reasoning (you choose how much the model “thinks” before answering). All three builds accept the same input fields: model, messages, tools, tool_choice, stream, max_tokens, temperature, top_p, stop, response_format and reasoning_effort.

What changed in DeepSeek V4.1 Flash 1017?

The “1017” suffix is a date stamp. On October 17, 2026, DeepSeek shipped an updated checkpoint of the same model rather than a new product line. Version tags like this are common: the lab keeps the model name and swaps in improved weights.

DeepSeek says the 1017 update does three things compared with the September release:

• Higher coding scores. Better results on code-generation and code-repair benchmarks.
• Higher agentic scores. “Agentic” means multi-step tasks where the model plans, calls tools, reads the results and continues, such as fixing a bug across several files.
• Less time and shorter output per task. The model reaches an answer with fewer generated tokens.

The third point gets less attention than benchmark scores, but it affects your bill directly. You pay for output tokens, and in agent loops output is usually the most expensive part of each step. A model that solves the same task with less text is cheaper and faster even at the same per-token price. It also returns control to your code sooner, which matters when a person is waiting on the result.

Developer forums show what people hoped DeepSeek 4.1 would deliver: code quality close to the top coding models at a fraction of the price. Whether it meets that bar depends on your codebase and your prompts, which is why the cheapest way to answer it is to run your own tests. The sections below show how to do that without spending much.

DeepSeek 4.1 FP4 vs FP8 vs standard: which build should you use?

FP4 and FP8 describe numeric precision, meaning how many bits the server uses to store each of the model’s weights. A model is trained at higher precision, then often “quantised” to fewer bits for serving. Fewer bits need less memory and less compute, so the model runs cheaper and often faster. The trade-off is that very low precision can slightly degrade output quality on hard tasks, such as long reasoning chains or exact code edits.

• FP4 uses 4-bit floating-point numbers. It is the cheapest way to run DeepSeek 4.1.
• FP8 uses 8-bit numbers and stays closer to the full-precision model.
• Standard is the original listing from September.

Build	Model id	Input (credits / 1M)	Output (credits / 1M)	Cached input (credits / 1M)
FP4	deepseek-v4-1-flash-fp4	12	24	0.32
FP8	deepseek-v4-1-flash-fp8	28	84	0.84
Standard	deepseek-v4-1-flash	24	95	0.5

Our practical guidance:

1. Start with FP4. Its output price is about 4x lower than the standard build and about 3.5x lower than FP8. For chat, summarisation, classification, extraction and most everyday coding help, it is the obvious default.
2. Move to FP8 when your evaluations say so. If FP4 starts missing details in long agent runs, or makes small mistakes in precise code edits that FP8 gets right, the higher price is worth it for that workload.
3. Keep the standard build for comparison. If you already built on deepseek-v4-1-flash, run the same prompts through the FP4 and FP8 ids before switching. Note that FP8 is slightly cheaper on output than standard but slightly more expensive on input, so input-heavy jobs such as long-document Q&A can cost a little more on FP8.

Switching builds only means changing the model string. Nothing else in the request changes, which makes A/B testing cheap and fast.

How much does DeepSeek 4.1 cost on Unicorn API?

Unicorn API prices everything in credits. On a monthly plan, a credit costs $0.01: Starter is $25, Creator $50, Studio $100 and Pro $200 per month. Top-up credits cost $0.015 each and never expire. Full details are on the pricing page. For chat models, the exact cost is debited from the token usage the model reports, and you can get the price of a request before running it with POST /v1/models/price.

At the plan rate, DeepSeek 4.1 FP4 works out to $0.12 per million input tokens and $0.24 per million output tokens. Here is how that compares with other chat models in the catalog:

Model	Input (credits / 1M)	Output (credits / 1M)
DeepSeek V4.1 Flash FP4	12	24
DeepSeek V4.1 Flash FP8	28	84
DeepSeek V4.1 Flash	24	95
GPT 6.1 Terra	60	300
Wish 1.0	120	480
Gemini 4 Argon	160	800

Worked example: one coding-agent session

Say an agent session reads 200,000 tokens of code and conversation in total and writes 20,000 tokens of plans, diffs and explanations. The cost per model:

• FP4: 0.2 × 12 + 0.02 × 24 = 2.4 + 0.48 = 2.88 credits, about $0.03 on a plan.
• Standard: 0.2 × 24 + 0.02 × 95 = 4.8 + 1.9 = 6.7 credits.
• FP8: 0.2 × 28 + 0.02 × 84 = 5.6 + 1.68 = 7.28 credits.
• GPT 6.1 Terra: 0.2 × 60 + 0.02 × 300 = 12 + 6 = 18 credits.
• Wish 1.0: 0.2 × 120 + 0.02 × 480 = 24 + 9.6 = 33.6 credits.
• Gemini 4 Argon: 0.2 × 160 + 0.02 × 800 = 32 + 16 = 48 credits.

Caching changes the picture further. Agents resend the same system prompt and file contents on every step, and cached input tokens are billed at a much lower rate. If 180,000 of those 200,000 input tokens are cache hits on FP4, the input cost drops to 20,000 × 12 / 1M + 180,000 × 0.32 / 1M = 0.24 + 0.058 credits. With the 0.48 credits of output, the session costs about 0.78 credits. At that price, a Starter plan’s 2,500 credits covers thousands of agent sessions.

The lesson: for DeepSeek 4.1, output and uncached input are what you pay for. Keep prompts stable so caching works, and cap output with max_tokens.

How to call DeepSeek 4.1 from code

Create a key on the API keys page and export it as UNICORN_API_KEY. If you want to try prompts before writing code, the chat playground lets you switch between the three builds in the browser.

curl: the minimal request

curl https://api.unicornapi.net/v1/chat/completions \
  -H "Authorization: Bearer $UNICORN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-1-flash-fp4",
    "messages": [
      {"role": "user", "content": "Write one sentence about unicorns."}
    ],
    "max_tokens": 200
  }'

Python: OpenAI SDK with streaming and reasoning effort

The endpoint is OpenAI-compatible, so the official OpenAI SDK works once you change the base URL. This example streams the reply token by token and sets reasoning_effort. The accepted values are listed on each model page and in the API documentation. Use lower effort for quick answers and higher effort for hard debugging or planning.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["UNICORN_API_KEY"],
    base_url="https://api.unicornapi.net/v1",
)

stream = client.chat.completions.create(
    model="deepseek-v4-1-flash-fp4",
    messages=[
        {"role": "system", "content": "You are a senior Python reviewer. Be concise."},
        {"role": "user", "content": "Review this function:\n\ndef avg(xs):\n    return sum(xs) / len(xs)"},
    ],
    reasoning_effort="low",
    max_tokens=600,
    stream=True,
)

for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)
print()

JavaScript: tool calling

Tool calling is what turns DeepSeek 4.1 into an agent. You describe functions in tools, the model replies with a tool_calls entry when it wants one run, your code runs it, and you send the result back as a tool message.

const BASE = "https://api.unicornapi.net/v1/chat/completions";
const headers = {
  "Authorization": `Bearer ${process.env.UNICORN_API_KEY}`,
  "Content-Type": "application/json",
};

const tools = [{
  type: "function",
  function: {
    name: "get_open_issues",
    description: "List open issues for a repository",
    parameters: {
      type: "object",
      properties: { repo: { type: "string" } },
      required: ["repo"],
    },
  },
}];

// Your real implementation goes here.
function getOpenIssues({ repo }) {
  return [{ id: 42, title: `Crash on empty input in ${repo}` }];
}

async function chat(messages) {
  const res = await fetch(BASE, {
    method: "POST",
    headers,
    body: JSON.stringify({
      model: "deepseek-v4-1-flash-fp4",
      messages,
      tools,
      tool_choice: "auto",
      max_tokens: 800,
    }),
  });
  if (!res.ok) throw new Error(`HTTP ${res.status}: ${await res.text()}`);
  return res.json();
}

const messages = [
  { role: "user", content: "What should I fix first in acme/api?" },
];

let data = await chat(messages);
let msg = data.choices[0].message;

while (msg.tool_calls && msg.tool_calls.length) {
  messages.push(msg);
  for (const call of msg.tool_calls) {
    const args = JSON.parse(call.function.arguments);
    const result = getOpenIssues(args);
    messages.push({
      role: "tool",
      tool_call_id: call.id,
      content: JSON.stringify(result),
    });
  }
  data = await chat(messages);
  msg = data.choices[0].message;
}

console.log(msg.content);
console.log("usage:", data.usage);

Log data.usage during development. Those token counts are what your credits are debited from, so they tell you exactly what each step costs. If you would rather use DeepSeek 4.1 inside Cursor, Claude Code or another coding agent, see the agents and MCP setup.

Is DeepSeek 4.1 the same as GPT-4.1?

No. The version numbers match by coincidence. GPT-4.1 is an OpenAI model, and DeepSeek V4.1 comes from DeepSeek. Search data shows the two get confused often, and queries like “gpt 4.1 vs deepseek v3” are still common.

Those older comparisons describe earlier generations on both sides. DeepSeek V3 came several releases before V4.1, and the MoE design, million-token context and the 1017 update’s agentic gains all postdate it. Benchmark tables comparing GPT-4.1 with DeepSeek V3 tell you little about how DeepSeek 4.1 performs today.

If you are choosing a model now, compare DeepSeek 4.1 with current models on your own tasks. On Unicorn API that means running the same prompts through, for example, the FP4 build, GPT 6.1 Terra and Gemini 4 Argon OpenAI with one key and one bill, then comparing quality against the usage each one reports. A common pattern is to route most traffic to DeepSeek 4.1 FP4 and send only the hardest requests to a pricier model.

Common mistakes when using DeepSeek 4.1

Filling the context window because you can

A one-million-token context lets you paste a whole repository, but every token you send is billed and slows the response. Send the files that matter, and let the agent fetch more through tools when it needs them.

Breaking the cache with changing prefixes

Cached input on FP4 costs 0.32 credits per million tokens against 12 for uncached input. Caching works on repeated prefixes, so put stable content (system prompt, tool definitions, reference files) first and changing content (the latest user message, timestamps) last. Adding a timestamp to the top of your system prompt removes most of that saving.

Leaving reasoning effort high for everything

Higher reasoning effort produces more tokens before the answer, and output is the expensive side of the bill. Use low effort for formatting, extraction and short answers, and save higher effort for debugging and multi-step planning.

Skipping max_tokens

Without a cap, a confused agent loop can generate far more text than you expect. Set max_tokens per call and a step limit in your loop.

Judging FP4 on one prompt

Quantisation effects show up unevenly. Build a small set of 20 to 50 real tasks from your own workload, run them on FP4 and FP8, and decide based on that. Because only the model string changes, this takes minutes.

Assuming every published number applies to your setup

Public figures for DeepSeek 4.1, including per-task costs and timing, come from specific benchmark harnesses. Your prompts, caching and reasoning settings will produce different numbers. Use POST /v1/models/price before a large batch and the reported usage afterwards, and you will know your real cost instead of estimating it.

Is DeepSeek 4.1 worth using?

For most developers, yes, as the default model for everyday work. DeepSeek 4.1 Flash combines a million-token context, tool calling and adjustable reasoning with output pricing that is a small fraction of the larger models in the catalog. The 1017 update targeted exactly the workloads where that matters most: coding and agents that run many steps.

The sensible setup is to run DeepSeek 4.1 FP4 by default, test FP8 on your hardest tasks, and keep a larger model like GPT 6.1 Terra or Wish 1.0 available for the cases your evaluations show need it. With one API key and one credit balance, switching between them is a one-line change, so you can let results decide instead of guessing.