DeepSeek V4 Flash Review (2026): What V4.1 Changed

I used to scroll past DeepSeek every time I saw it in a model list. That was a mistake.

The short answer from my own testing in May 2026: DeepSeek V4 Flash handled everyday coding work about as well as Claude Sonnet 4.6, it was fast, and it cost less than half of Xiaomi MiMo v2.5 Flash per million input tokens. Since then DeepSeek has replaced it with V4.1 Flash and released a further update called 1017. I have not re-tested those two yet, so everything about them below comes from published sources and is labeled that way.

What Changed Since My Review: V4.1 Flash and the 1017 Update

My hands-on testing covers V4 Flash only. The model has moved on since, so here is the timeline with sources.

The Pro rerouting stays in place until a V4.1 Pro arrives, and The Next Web reports that DeepSeek has given no date for it. The 1017 model card calls the release a minor update with improvements in reasoning, coding, agentic tasks, and visual benchmarks. It also says the update reduces task execution time and output length. The card does not give a release date or a price.

Image input may have changed

This is the change that matters most for my own verdict. Below I list the lack of image support as the one real downside of V4 Flash. According to The Next Web, the V4.1 Flash model card says the model understands images natively. The same article says DeepSeek’s technical report admits it still lags the best closed models at reading complicated images.

I have not sent a screenshot to V4.1 Flash yet, so I cannot tell you how well it reads a terminal error or a UI bug. If that was your reason to pick MiMo over DeepSeek, it is worth testing again.

Pricing: check before you compare

The cost table later in this post uses the price I recorded in May. Current numbers are less clear. The Next Web reports an off-peak output price of $0.60 per million tokens and says peak rates on weekday mornings UTC are double the off-peak rates. It does not state the uncached input price, and other sources I found disagree with each other. Check DeepSeek’s own pricing page before you set the May table against today’s bill.

The benchmark numbers are DeepSeek’s own

Here are the scores DeepSeek published for V4.1 Flash and for the 1017 update. DeepSeek ran the DeepSWE test with the Mini-SWE-V2 harness and Terminal-Bench 4.0 with Claude Code. No independent lab has verified these figures, so treat them as a claim and not as a result.

For context, The Next Web lists Claude Opus 5 at 74.0 on DeepSWE v1.1 in the launch table, so V4.1 Flash is level with it on that one test. Opus 5 is far ahead on Humanity’s Last Exam, 56.3 against 36.8. A model can be close on one coding benchmark and still behind on general reasoning, and a benchmark is not your repo.

OpenCode Made the Introduction

My first experience with the DeepSeek family was DeepSeek R2. The responses felt off. Not wrong exactly, just not the kind of sharp, confident output I was getting from Claude Sonnet 4.6 or GPT-5.4. So I filed DeepSeek under “not for me” and moved on.

That changed when OpenCode announced that DeepSeek V4 Flash and Pro were free for a limited time. I was already using OpenCode as my daily coding assistant. When the free V4 Flash option appeared in the model selector, I switched to it on a whim for a refactoring task.

The first thing I noticed was the speed. The tokens streamed in noticeably faster than what I was used to. It was the kind of fast where you start reading the response before the previous line finishes rendering.

Also Read: OpenCode Multi-Model CLI: Switch AI Without Limits

Intelligence That Did Not Drop With the Price

I kept waiting for the quality drop that usually comes with free and cheap. It did not come.

The responses were coherent and the code suggestions were accurate. When I threw a TypeScript refactoring problem at it, V4 Flash handled it as cleanly as Claude Sonnet 4.6 would have. I use Claude daily, so I know what a good coding response looks like.

For pure text and code tasks, V4 Flash landed in the same league as GPT-5.4 and Claude Sonnet 4.6 in my experience. Not every model can claim that.

DeepSeek V4 Flash vs Xiaomi MiMo v2.5

For the months before this, Xiaomi MiMo v2.5 Flash had been my go-to budget model. It is fast, multimodal, and handles coding well. Switching to V4 Flash for a few days made for a clean side-by-side comparison.

For coding tasks, the two models were very close. The quality gap, if it exists at all, is not something you would notice in everyday work. Both produce clean, well-structured code and both are fast on tokens per second.

The one area I have not compared is agentic AI. I have not tested either model in frameworks like Hermes or OpenClaw, so I cannot say how they hold up in longer autonomous workflows. That comparison stays a draw for now.

Also Read: Xiaomi MiMo Review: 60M Tokens for $6, Faster Than GPT-5.4

The Cost Difference in May 2026

This is where V4 Flash became genuinely interesting for anyone running AI-heavy workflows. These are the input prices I recorded in May, before V4.1.

That was a 65 percent cost reduction for a model that delivered comparable output on coding tasks. If you process large volumes of code through an API, a gap like that adds up fast.

The One Real Con in May: No Image Support

This is where MiMo v2.5 kept its edge. If your workflow involves sending screenshots, diagrams, or UI mockups to the model, V4 Flash could not help. It was text and code only.

I occasionally drop a screenshot into a conversation to ask about a UI bug or a terminal error. Those moments needed MiMo or another multimodal model. Based on the V4.1 model card described above, this may no longer be true, but I have not verified it myself.

Verdict and Who Should Use It

My verdict on V4 Flash stands for what I tested. It is fast, it is good enough to compete with the big names on everyday coding, and in May it cost far less than the alternative I was using.

Use the DeepSeek Flash line if your work is mostly code and text, you run a lot of tokens, and you can live with a vendor that publishes its own benchmarks. Stay with MiMo or a closed model for now if you depend on reading screenshots and cannot afford a surprise. Test V4.1 Flash on your own repo before you move a team to it.

Also Read: Why I Chose Codex CLI as Claude Code Alternative: 2025 Review

Do not let one bad experience close the door on a model family. A second look was worth it for V4, and it is probably worth it for V4.1 too.