DeepSeek V4: Everything We Know (Release Date, Features, Benchmarks)

Hey, Anna is here. I was halfway through a Sunday tidy-up of notes, one part meal ideas, one part stray project thoughts, when my chat feed lit up with "V4 when?" speculation. I assumed I'd glance, roll my eyes, and get back to figuring out what to do with three sad zucchinis. Instead, I fell into a quiet rabbit hole, the kind where you're not hyped, just… curious enough to keep reading.

What Is DeepSeek V4?

Quick Overview

I think of DeepSeek V4 as the next swing in the same direction DeepSeek's been heading for a while: cheaper reasoning, steadier coding help, and a longer attention span. The company's earlier models (V3.x) got popular because they were fast and surprisingly affordable. Then R1 landed with bolder claims around reasoning and "chain-of-thought"-style outputs, sometimes brilliant, sometimes a bit theatrical. If V4 exists the way it's being teased, it's meant to fuse the reliability of V3.2 with the structured thinking of R1, without the cost spike that usually ruins the fun.

I didn't see a glossy announcement yet, more of a slow drip of hints, repos, and papers. It's the kind of build-up that makes me cautious. But there's enough smoke to keep the kettle on.

V4 vs V3 vs R1

Here's how it shakes out in practice from my side of the desk:

• V3 (and V3.2): My quiet workhorse for everyday prompts, draft a message, sort a note pile, write a small script. It rarely dazzles, but it rarely derails me.
• R1: When I've needed deeper step-by-step reasoning, picking apart a tricky logic bug or mapping options for a decision, it's been helpful, though occasionally overconfident.
• V4 (expected): If it lands as teased, I'm expecting steadier reasoning than R1, fewer "I can fix the world" vibes, and better long-context handling than V3.2. The real test for me will be simple: can it keep a 20–30 minute personal planning session coherent without forgetting the early parts? If yes, that's meaningful.

Expected Release Date

Mid-February 2026 (Lunar New Year)

Most of the chatter points to mid‑February 2026, around Lunar New Year. I've seen this repeated enough times to take it seriously, but not enough to bet my grocery budget on it. Practically, that means: if you're deciding whether to switch tools right now, you're probably a few weeks away from knowing whether V4 is worth waiting for. I've penciled it into my "check back" list for the week after the holiday window, because launches slip and I'd rather be surprised than irritated.

Confirmed Information

MODEL1 Code in GitHub

As of late January 2026, there's code on DeepSeek's GitHub that references something called MODEL1. I'm not going to pretend I reverse‑engineered the whole thing: I didn't. What I did do was poke around the repo names, skim a few readmes, and look for breadcrumbs that hint at training pipelines and inference scaffolding. It looks like foundational plumbing rather than a shiny demo, useful as a signal, not yet a daily tool.

My takeaway: there's active, public groundwork that aligns with a major release. It's not marketing fluff: it's code. Whether MODEL1 is the internal name for V4, a training run, or a sibling project… I can't say. But it's not nothing.

Engram Memory Architecture

There's also material (papers, notes, and a few technical threads) pointing to an "Engram" memory architecture. The gist, from what I can gather without a lab coat, is longer, more stable recall: the model can reference earlier parts of a conversation or document without the usual amnesia that creeps in around the edges. Think: fewer "wait, what did we decide five minutes ago?" moments.

I care about this for one silly but real reason: my personal planning chats tend to wander. If Engram helps V4 keep track of meal ideas, appointments, and half‑finished to‑dos across a long session, I'll notice it immediately. I can't confirm production behavior yet, only that the concept shows up in official-looking places.

mHC Paper Clues

I also saw mentions of an mHC approach in a preprint, framed as a way to juggle long contexts efficiently. I'm not going to invent math here. From the abstract-level descriptions, it reads like a clever compression and retrieval strategy designed to keep the model responsive even when you dump a novel's worth of notes into it.

If true, it could mean fewer trade‑offs: you get a longer context window without the sluggishness that usually makes me close the tab out of annoyance. But again, these are paper clues, not a feature I've used yet.

Unconfirmed Rumors

Benchmark Claims (vs Claude/GPT)

I've seen chart screenshots claiming V4 edges past current Claude and GPT models on reasoning and code tasks. These images travel fast, context travels slowly. Until we get a proper eval suite with reproducible runs, I treat all of it as "maybe." My practical yardstick will be boring: does it make fewer dumb mistakes when I ask it to refactor a small function? Does it keep track of constraints in a multi‑step plan? If yes, it wins for me, regardless of which line is taller in a tweet.

Parameter Counts

There's also speculation about parameter sizes, some saying a large dense model, others hinting at a mixture‑of‑experts style setup. I care less about the number and more about behavior. If the design means faster, cheaper, and steadier responses in my everyday prompts, great. If it means "more powerful" but doubles the cost, I'll probably stay on V3.2 for routine use and call V4 in selectively.

Open-Source Release

Another rumor: parts of V4 might be open‑sourced (weights or strong distilled variants). I'd love that, mostly because local or self‑hosted options can be calmer on the wallet and nicer for privacy. But it's a big "we'll see." If we do get a credible small or medium model from the V4 family, I'll try it for local journaling prompts and simple coding tasks first.

What V4 Means for Users

Better Coding Assistance

I tested DeepSeek models throughout last year for little scripts: a grocery list parser, a "rename files like a sane person" helper, and some notebook cleanup. V3.2 handles these fine, but it occasionally drops context, like forgetting a naming rule halfway through. If V4's reasoning is steadier, I'm expecting cleaner, more reliable code edits on the first pass. The win isn't speed for me: it's fewer retries and less babysitting.

A small example: when I ask for a Bash one‑liner, I want a one‑liner, not a lecture. If V4 leans practical over theatrical, that alone saves me a few minutes and a smidge of patience daily.

Longer Context Windows

This matters for people like me who think in messy piles. I keep a weekly "roll‑up" note: meals, errands, small project steps, practice schedules. Long context means I can paste the whole thing, 1–2k lines sometimes, ask for a tidy plan, and not watch the model quietly forget the top section. If Engram or mHC actually help with recall, I'll feel it when V4 remembers that we moved Tuesday's workout because I said I'd be on a train.

Also, long context is weirdly great for learning. I like dropping in a spread of examples, two good, one bad, and asking the model to infer my style. When the context is tight, it overgeneralizes. With more room, it picks up tone and small preferences. If V4 extends that, it could make personal assistants feel more… personal.

Potential Cost Savings

DeepSeek has a reputation for aggressive pricing. If V4 keeps that pattern, it could push everyday use cases, journaling prompts, drafting emails, quick research, into the "why not?" category. I budget my AI use (not rigidly, just awareness), and when something feels costly, I reserve it for "hard" tasks. Cheaper, capable models invite casual use, which is where most of the quiet benefits live.

But "savings" can also mean fewer corrections. If V4 is a touch more accurate and less forgetful, I'll spend less time re‑prompting and cleaning up. That's invisible on a receipt but obvious at 11 p.m. when I'm trying to close the day.

Should You Wait for V4?

Current V3.2 Is Still Great

If you're already using DeepSeek V3.2 for daily stuff, habit nudges, small plans, code snippets, I wouldn't pause your life waiting for V4. V3.2 is good enough that you'll keep moving. I'm still using it for:

• A morning "what's on deck" prompt based on my calendar and a messy note
• Drafting tidy checklists from rambling paragraphs
• Quick code fixes that don't need a novel of context

If V4 arrives and slots in smoothly, switching will be easy. If it slips, you won't have lost momentum.

Who Should Care Most

• If you paste long notes and want the model to actually remember them across a session, V4 is worth watching.
• If your coding tasks involve multi‑step refactors or careful constraints, V4's steadier reasoning (if it's real) could shave off retries.
• If you're cost‑sensitive but use AI daily, the pricing story could matter more than any benchmark.

If you're happy on a different model right now, I wouldn't uproot a working setup. I'd add V4 to the shortlist, run a week of side‑by‑side tests on your actual tasks, and keep what feels lighter in your hands.

One last note on expectations: launches arrive noisy. I plan to test V4 the same way I test everything: a few real prompts, repeated over a week, no special tuning. If it quietly reduces friction, I'll keep it. If it doesn't, I won't force it just to have "the new thing."

For keeping track of all my experiments and notes—whether it’s DeepSeek prompts or random ideas—I rely on Macaron. It keeps things tidy and saves me from losing track → Click here!

For more detailed information about DeepSeek's reasoning capabilities, you can check out the official DeepSeek R1 documentation and their API documentation.

I'll keep an eye out mid‑February. For now, I'm back to the zucchinis. And I'm curious whether V4 will remember where I put the recipe I liked last spring.