DeepSeek V4.1 Flash 1017: what changed and how to switch

In our test calls, DeepSeek V4.1 Flash 1017 did not reason unless we asked it to. Reasoning is text a model writes for itself before the answer, and reasoning_effort is the request setting that switches it on or off. So set reasoning_effort yourself before the 27 October routing.

Should you switch to DeepSeek V4.1 Flash 1017 now, and what changes when you do? Our 21 October mail promised a one-line switch, and that line can still change how your requests behave.

What changes in DeepSeek V4.1 Flash 1017

DeepSeek V4.1 Flash 1017 is a minor update of DeepSeek V4.1 Flash. Its weights, the trained model files, are open under the MIT License. DeepSeek's model card says it improves reasoning, coding, agent and visual benchmarks. DeepSeek's release note adds that it finishes tasks faster and writes shorter outputs.

The table compares both versions as our API lists them on 22 October 2026. The last row comes from our own test calls, described below.

The context window is the amount of text the model reads in one request. Current prices for the older version are on the DeepSeek V4.1 Flash model page. For its specs and image input, see our DeepSeek V4.1 Flash guide.

DeepSeek V4.1 Flash 1017 benchmarks, as DeepSeek reports them

DeepSeek reports higher scores for 1017 on all five benchmarks it leads with. A benchmark is a fixed set of test tasks, scored the same way for every model. HLE stands for Humanity's Last Exam, a set of hard questions, here answered without tools. Higher is better.

The largest gain is on Terminal-Bench 4.0. DeepSeek ran it in the Claude Code harness. A harness is the agent program that drives the model through each test task. A different harness can give a different score, so the number belongs to that setup.

Benchmarks do not show how 1017 handles your prompts, your tools or your output format. We tested the update before the launch and recommend it, after the checks below.

Reasoning is off by default in 1017 on our API

Reasoning bills as output tokens. On DeepSeek V4.1 Flash, reasoning is on by default, and reasoning_effort "none" turns it off. Read about it on our model page.

We sent both versions the same prompt on 22 October 2026. It asked for a Python function that turns an ISO 8601 date into a weekday name. Each setting ran three times, with max_tokens at 8,000. The table shows the median output tokens per request.

Two things stand out. With reasoning switched on in both, 1017 used about half the output tokens. That matches DeepSeek's claim of shorter outputs.

After midnight CET on 27 October, requests that still name deepseek/deepseek-v4.1-flash go to the new model. A pipeline that counted on reasoning by default may then get answers without it. Setting reasoning_effort in the request removes that dependency.

In the same test, 1017 accepted reasoning_effort values "low", "medium", "high", "max" and "none". This was one prompt and three runs per setting, not a benchmark.

What the switch does to your output token bill

Here is the formula for a batch of identical requests:

cost in USD = requests × (input tokens × 0.50 + output tokens × 1.50) / 1,000,000

Our worked example takes 10,000 requests, a hypothetical volume. Each uses the median token counts from the test above. Each request had 30 to 56 input tokens in our test. At $0.50 per million tokens, input adds $0.15 to $0.28 per 10,000 requests.

The drop from $31.81 to $5.46 mixes both effects above. The fair comparison is the pair with reasoning_effort "high" in both: $18.75 against $9.52, about half.

The example uses a short prompt, where output is almost the whole bill. A long agent task that resends a large context pays mostly for input, so its saving is smaller. The example also says nothing about whether the shorter reasoning gives answers that are good enough for you.

How to switch to DeepSeek V4.1 Flash 1017 before 27 October

Lyceum is a sovereign European AI inference provider for open-source models. We build and run it in Europe, and our serverless models run in Paris and Finland. Our model page lists DeepSeek V4.1 Flash as EU-hosted, with prompts and outputs processed, not stored, and never used for training.

The switch is the model value, plus a reasoning setting so the behaviour is yours to choose.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["LYCEUM_API_KEY"],
    base_url="https://api.lyceum.technology/openai/v1",
)
response = client.chat.completions.create(
    model="deepseek/deepseek-v4.1-flash-1017",
    reasoning_effort="high",
    messages=[{"role": "user", "content": "Which weekday is 2026-10-27?"}],
)
print(response.choices[0].message.content)
print(response.usage.completion_tokens)

Use "none" for direct answers, and "high" when you want the model to reason first. Then run a sample of your real prompts on both versions, and compare the answers and the output token counts. Our serverless inference documentation covers the request options.

If you do nothing, your code keeps working, and after the routing the reasoning default changes as described above.

When to switch now and when to test first

Switch now when your requests already set reasoning_effort, or when they do not need reasoning. Your code then behaves the same on 1017, and you get the update's gains early.

Test first when a pipeline relied on reasoning being on by default, or when it parses the model's output. Shorter answers can break a parser that expects a fixed length or layout. In that case, test before 27 October, while you can still compare the two versions side by side.

Open models now trail the best closed models by about four months on Epoch AI's capability index, according to its data from 29 May 2026. An explicit reasoning_effort keeps each future switch a one-line change. To compare DeepSeek models for heavier work, read where to run DeepSeek in Europe. Live latency figures are on our status page.

Send one request with the new model value from your Lyceum dashboard, and read its token count before 27 October.