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DeepSeek · Last 7 days

DeepSeek V4 Pro

0–100 · higher means more positive community experience, not benchmark performance.

Updated 2026-09-15 13:27 UTC · 2026-09-08 – 2026-09-15 UTC
Community score43.4/100165 comments in 7 days

As of 2026-09-15 13:47:02 UTC, DeepSeek V4 Pro in the DeepSeek family has 165 explicitly attributed community comments in the last 7 days; its community score is 43.4/100. Available category scores include General text 33.1/100 (n=105); Reasoning 71.3/100 (n=24); Roleplay / creative 65.7/100 (n=17). Scoring method and sources

Community reviews

Selected comments from the last 7 days. The balance of excerpts does not represent the share of positive reviews.

11 selected excerpts

NegativeSpeed & latency

“Just now when I used it, it was still broken [facepalm]”

ZhihuzhMachine translated
Show original

刚才用还是断的[捂脸]

NegativeGeneral text

“Tried it myself and it felt quite similar to DS flash while costing significantly more.”

Redditen
NegativeGeneral text

“Although Pro is already a lost cause, it really can't be taken off the table.”

ZhihuzhMachine translated
Show original

Pro虽然已经是路边一条了，但是还真下不了桌

NegativeRoleplay / creative

“I find it awful in that respect, the good news is that v4 pro will return, so all good”

Redditen
NegativeReasoning

“When V4 Pro first came out and when V4 Pro officially released, I mentioned the performance issues both times.”

ZhihuzhMachine translated
Show original

V4 Pro 刚出来的时候和 V4 Pro 正式版出来的时候，我都说过性能问题

NegativeSpeed & latency

“Pro just thought forever about everything. Even things that it could do instantly, it thought for a long time, debating with itself.”

Redditen
NegativeRoleplay / creative

“removing the Pro version without considering its advantages in other areas was pretty frustrating”

Redditen
NegativeReasoning

“v4Pro coudln't find the other day”

Redditen
NegativeReasoning

“There is still a significant gap in world knowledge compared to the Pro version”

ZhihuzhMachine translated
Show original

世界知识比pro还有较大差距

NegativeSpeed & latency

“16k tokens over 9 hours is not an acceptable speed in any 2 way of the word. Most people consider around 12 tks bare minimum for any agentic or chat tasks.”

Redditen
NegativeCoding

“Coding leaderboard: glm5.3 flash > deepseek v4 pro 0813 > kimi k3”

ZhihuzhMachine translated
Show original

编程分榜glm5.3 flash > deepseek v4 pro 0813 > kimi k3

Explore long-term changes & analysis

Model lifecycle

DeepSeek V4 Pro

Released 2026-04-24 · Back to DeepSeek

Exact-version signals observed 2026-08-07–2026-09-14 · n=1,129

Data window: 2026-08-18 00:00:00 to 2026-09-15 00:00:00 UTC · Scoring method: experience_score_v2.

Trend history still collecting The current window is ready; 3 of 4 independent segments are available.

Lifecycle updated daily · latest complete UTC day 2026-09-14

Fixed 14-day baseline
36.6
n=484
Latest 28 days
43.2
n=277
Change vs baseline
+6.6
90-day slope
–
pts / 30 days

14-day rolling experience

Each daily point summarizes the previous 14 complete UTC days. The dashed line is the fixed release baseline; the verdict still uses independent non-overlapping 14-day segments.

14-day rolling experience from 2026-08-14 to 2026-09-15; latest score 45.2, fixed baseline 36.6.30405060Fixed baseline 36.62026-07-31–2026-08-14 · 36.6 · n=4842026-08-01–2026-08-15 · 35.0 · n=5572026-08-02–2026-08-16 · 33.3 · n=7342026-08-03–2026-08-17 · 33.4 · n=8172026-08-04–2026-08-18 · 33.6 · n=8462026-08-05–2026-08-19 · 33.7 · n=8642026-08-06–2026-08-20 · 33.7 · n=8842026-08-07–2026-08-21 · 33.7 · n=8972026-08-08–2026-08-22 · 33.7 · n=9022026-08-09–2026-08-23 · 33.6 · n=9022026-08-10–2026-08-24 · 33.6 · n=9042026-08-11–2026-08-25 · 33.6 · n=9072026-08-12–2026-08-26 · 33.7 · n=8992026-08-13–2026-08-27 · 32.2 · n=7732026-08-14–2026-08-28 · 31.2 · n=4362026-08-15–2026-08-29 · 32.8 · n=3642026-08-16–2026-08-30 · 37.5 · n=1892026-08-17–2026-08-31 · 39.2 · n=1092026-08-18–2026-09-01 · 39.6 · n=822026-08-19–2026-09-02 · 39.5 · n=652026-08-20–2026-09-03 · 41.9 · n=452026-08-21–2026-09-04 · 47.0 · n=452026-08-22–2026-09-05 · 49.6 · n=462026-08-23–2026-09-06 · 49.9 · n=442026-08-24–2026-09-07 · 50.7 · n=422026-08-25–2026-09-08 · 49.5 · n=382026-08-26–2026-09-09 · 49.7 · n=442026-08-27–2026-09-10 · 42.6 · n=942026-08-28–2026-09-11 · 38.9 · n=1212026-08-29–2026-09-12 · 40.5 · n=1402026-08-30–2026-09-13 · 43.5 · n=1702026-08-31–2026-09-14 · 45.0 · n=1842026-09-01–2026-09-15 · 45.2 · n=19508-1408-2008-2609-0109-0709-1309-15

3 of 4 independent 14-day segments ready for trend judgment.

What explains the change

Experience change and discussion-mix change are separated. These are observational contributions, not proof of cause.

No category has enough samples in both windows for a contribution conclusion.

Category Baseline → current Category change Weight share Experience contribution Discussion-mix contribution
Coding Baseline → current56.9 → 59.0n=55 → 19 Category change+2.1 Weight share12.5% → 7.4% Experience contribution+0.21 Discussion-mix contribution−0.93
Speed & latency Baseline → current39.4 → 42.9n=33 → 27 Category change+3.5 Weight share6.9% → 9.7% Experience contribution+0.29 Discussion-mix contribution+0.04
Reasoning Baseline → current60.0 → 65.4n=95 → 52 Category change+5.4 Weight share21.1% → 19.5% Experience contribution+1.09 Discussion-mix contribution−0.37
General text Baseline → current22.6 → 29.9n=290 → 155 Category change+7.2 Weight share57.0% → 54.8% Experience contribution+4.04 Discussion-mix contribution+0.30
Image / vision Baseline → currentnot enough datan=0 → 0 Category change– Weight share– Experience contribution– Discussion-mix contribution–
Local deploy Baseline → currentnot enough datan=4 → 0 Category change– Weight share– Experience contribution– Discussion-mix contribution–
Roleplay / creative Baseline → currentnot enough datan=8 → 19 Category change– Weight share– Experience contribution– Discussion-mix contribution–
Safety & refusals Baseline → currentnot enough datan=1 → 5 Category change– Weight share– Experience contribution– Discussion-mix contribution–
Video generation Baseline → currentnot enough datan=0 → 0 Category change– Weight share– Experience contribution– Discussion-mix contribution–

Unresolved contribution from categories below the sample threshold: +1.95 pts.

Lifecycle scores use the same experience-signal weights as the main index, without sample shrinkage after n=30. They measure public user perception, not model capability or backend causes.

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