DeepSeek V4.1 Flash 1017 Tested: How a Cheap Open Model Stacks Up

Key takeaways

• DeepSeek V4.1 Flash 1017 is a refresh of the Flash model first released on 10 September
• Listed at $0.30 / $1.20 per million tokens (input / output) with a 1M token context window
• DeepSeek reports Terminal Bench 4.0 rising from 31.2% to 42.3% and HLE from 36.8% to 42.6%
• In the video’s tests it was often 3 to 44 times cheaper than the model it was up against
• The premium model usually won on quality, and DeepSeek was often slower to finish
• New search and fetch APIs cost $0.75 per 1,000 calls, with server-side calls free until 31 October
• Open weights, but too large to run on normal home hardware
• Claims that it copies OpenAI’s outputs are unconfirmed speculation

Cheap AI models are easy to dismiss until one of them builds a working 3D scene for less than the price of a coffee. That is the story WorldofAI tells in a new hands-on test of DeepSeek’s latest release.

This article explains what DeepSeek V4.1 Flash 1017 is, what it costs, how it compared with Opus 5.5, GPT-6.1 Sol, GPT-6 Luna, Gemini 4 Argon and Kimi K3 in the video, and how to decide whether it deserves a place in your own workflow.

What is DeepSeek V4.1 Flash 1017?

It is the latest update to DeepSeek’s fast, low-cost Flash model. The “1017” refers to this particular release. The original V4.1 Flash came out on 10 September, and DeepSeek has refined it several times since.

DeepSeek describes the update as a “Pareto improvement”. In plain terms, that means better results at the same or lower cost. The company says it scores higher on benchmarks, finishes tasks faster and gives shorter answers than earlier versions.

According to the video, DeepSeek has focused on agentic coding: getting the model to complete whole projects, such as games, apps and 3D work, rather than just writing snippets of code. It says it used reinforcement learning with self-feedback to get there.

You can use it in three ways, as shown at the time of recording:

• DeepSeek Chat on the web, including the DeepThink mode
• The DeepSeek mobile app
• The DeepSeek API, which you can plug into tools such as Claude Code, Codex or the DeepSeek Harness

How much does DeepSeek V4.1 Flash cost?

It is listed at $0.30 per million input tokens and $1.20 per million output tokens. A token is a small chunk of text, roughly three-quarters of a word. The model has a 1 million token context window, which is how much text it can consider at once.

Those are API prices. The chat app is free to try. Here is how the costs in the video’s own tests compared:

The pattern is clear. DeepSeek was usually much cheaper, but not always: Haiku beat it on both cost and time in the parkour game test. These are single runs on one person’s prompts, so your numbers will differ.

What do the benchmarks say?

DeepSeek’s own report shows clear gains over the previous Flash version. These are the vendor’s numbers, as quoted in the video:

DeepSeek says the Terminal Bench score puts it ahead of the GPT Sol model it compared against (39.9%). WorldofAI also ran the model through its own benchmark, WoAI Bench, where it landed just outside the top 10 overall and, according to the video, first among open models.

Keep in mind that WoAI Bench is the creator’s own product, and independent testing of this release was still limited when the video was made.

How did it do against Opus, Sol, Luna, Argon and Kimi?

It held its own on cost, but the premium model usually produced the better result. Here is what the video showed in each match-up.

• Claude Opus 5.5: Opus delivered clearly better animation, sound and detail. DeepSeek got the basic structure right, with a few errors, for roughly 1/26th of the cost.
• GPT-6 Luna: many people compare the two. In the Roadster test, Luna’s output was a little better, and DeepSeek was about 17% cheaper.
• GPT-6.1 Sol Ultra: both took about the same time. DeepSeek used web search to gather references and produced a decent result for far less.
• Gemini 4 Argon: the SVG results were close, and DeepSeek drew better water in places. Argon produced the better overall scene and finished almost four times faster.
• Kimi K3: Kimi’s Minecraft-style game was far richer, even adding an extra dimension. DeepSeek struggled with many features, though more prompting could improve it.
• Claude Haiku: in a four-model parkour game test, DeepSeek had the flashier visuals, but Haiku’s game was arguably more functional, cheaper and quicker.

There were standout moments too. DeepSeek built a medieval voxel kingdom with over 2 million voxels (3D pixels) in about 25 minutes for around $5, complete with a day-night cycle and a dragon. It also produced a grassy hill scene with wind effects and sound in a single turn, and a Blender project through Hermes Agent for $1.83, although that took about seven hours.

Is DeepSeek V4.1 Flash actually fast?

It generates text quickly, but big jobs often took longer to finish. The video reports about 244 tokens per second, with some early tests close to 300. WorldofAI suggests that could be faster than GPT-6.1 Sol in its ultra-fast mode.

Raw speed and finishing time are different things, though. In several tests DeepSeek took an hour or more where the rival took 13 to 20 minutes. More attempts, more fixing and more tool calls all add up.

WorldofAI’s suggestion is to give slow, long jobs to DeepSeek overnight, when finishing time matters less. If you try that, set spending caps and checkpoints first. Our guide How to Run Long AI Agent Jobs Overnight Without Wasting Money shows how.

What are the new search and fetch APIs?

They let the model search the web and read pages through DeepSeek’s own search system. DeepSeek says that system can handle hundreds of millions of requests a day.

• Price: $0.75 per 1,000 calls for each endpoint
• Free period: server-side search and fetch calls are free until 31 October at 16:00 UTC (5pm UK time) when you use the DeepSeek API with Claude Code, Codex or the DeepSeek Harness
• Why it matters: in the Sol test, web access helped DeepSeek gather references and improve its output

If you want to try it, the free window is the cheapest time to experiment. Check DeepSeek’s own pricing page before you rely on these figures, as offers change.

Why does the harness matter?

A harness lets the model test, spot errors and fix its own work, rather than answering in one shot. A harness is the software around a model that manages its tools, its context and running code.

The DeepSeek Harness is built around DeepSeek’s models. WorldofAI calls it the best way to use V4.1 Flash, because the model can keep refining an output until it works. Claude Code and Codex can also call the DeepSeek API.

The lesson applies beyond DeepSeek: a mid-priced model inside a good loop of checking and fixing often beats a raw single prompt. See How to Build an AI Agent Feedback Loop: Brief, Check, Score, Repeat for a simple version of that loop.

What does the video not confirm?

• Independent benchmarks: the headline scores come from DeepSeek’s own report and the creator’s own benchmark
• Consistency: each comparison is a single run, so we cannot tell how often DeepSeek wins or loses
• Local use: the weights are open, but the video says the model is far too large to run on ordinary hardware
• Data handling: the video does not cover where your data is processed or stored. Check DeepSeek’s terms before sending client or personal data.

For a wider checklist on separating real releases from hype, read How to Verify AI Model Leak Rumours Before You Switch Tools.

How should you try DeepSeek V4.1 Flash?

Start free in the chat app, then test one real task through the API with a spending cap.

1. Try it in DeepSeek Chat with a task you already know well, so you can judge the result.
2. Pick one repeatable job from your work, such as a landing page section, a data clean-up or a small script.
3. Run it on your current model too, using the same prompt and the same reasoning level.
4. Record cost, time and quality for both. Our side-by-side test guide includes a simple scoring table.
5. Set a spending limit on your API account before connecting it to Claude Code, Codex or the DeepSeek Harness.
6. Keep client data out until you have read the provider’s data terms.
7. Decide by task, not by brand: use the cheap model where it is good enough, and keep the premium one for the rest.

Who is DeepSeek V4.1 Flash right for?

It suits you if you run lots of drafting, coding or prototyping jobs where “good enough” is fine, you are happy to review and fix the output, and cost matters more than speed.

It matters less if you need the best possible result first time, you work to tight deadlines, or you handle sensitive client data and have not checked where it would be processed.

Many small businesses will get the most from a mix: a cheap model for first drafts and bulk work, and a premium model for the final polish. That is the idea behind our two-model front-end workflow.

Frequently asked questions

The bottom line

DeepSeek V4.1 Flash 1017 is a cheap, quick-talking open-weight model that came surprisingly close to frontier models in WorldofAI’s tests, often for a small fraction of the price. It did not beat Opus, Argon or Kimi on quality, and big jobs often took much longer. If cost matters to you, try it free in the chat app, then run one real task through the API with a spending cap and compare it fairly with what you use today.

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Garry Knight

I'm Garry Knight, the person behind Prodify Digital. I write about email list building, email marketing, SEO, AI search and the tools that connect them. My aim is to make online marketing easier to understand, so creators and small business owners can make informed decisions about building an audience and keeping people engaged. Here you'll find straightforward guides and product reviews that explain what something does, where it fits and which limitations matter. The focus is on clear explanations and useful next steps—not hype, shortcuts or promises of easy earnings.

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