DeepSeek · Last 7 days

DeepSeek V4 Pro

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

Updated 2026-10-02 21:57 UTC · 2026-09-25 – 2026-10-02 UTC
All-platform score43.5/10018 comments in 7 days

Discussion overview

What the full sample discusses

All platforms · 18 eligible comments over 7 days, including usage-limit feedback; deduplicated by source and comment.

  • General text · 11 comments: 5 positive / 6 negative / 0 mixed or neutral
  • Reasoning · 2 comments: 2 positive / 0 negative / 0 mixed or neutral
  • Coding · 1 comments: 0 positive / 1 negative / 0 mixed or neutral
  • Image understanding · 1 comments: 0 positive / 0 negative / 1 mixed or neutral

Comments may cover multiple dimensions; counts are not additive or equivalent to the weighted score. Links show selected source excerpts.

Recent change

Rolling 7-day score: −24.1 points vs 7 days ago. Discussion mix can affect scores; this does not establish a cause.

Source coverage and discussion concentration

Hacker News 2 · Reddit 14 · Zhihu 2

10 identifiable discussions cover 16/18 comments; the largest has 7. Remaining thread identities are unknown; this is not a count of independent users.

Selected individual opinions

One user claims V4 Pro has superior abilities compared to V4.1 Flash because it is a bigger model, attributing the advantage to model size. One user notes that DeepSeek V4 Pro is not a vision model, so a subagent (such as V4.1 Flash or Qwen) should be used for vision tasks.

About the sample and scores

As of 2026-10-02 21:57:03 UTC, DeepSeek V4 Pro in the DeepSeek family has 18 explicitly attributed community comments in the last 7 days; its community score is 43.5/100. Scoring method and sources

RECENT EXPERIENCE

How the Experience Index is changing

Not enough historyvs 7 days ago · points

30 days of rolling 7-day scores at each daily snapshot. The vertical scale adapts to the data. Missing or insufficient samples leave gaps; today is still updating.

Tap or use ← → for dates, scores and sample sizes. Blank dates have insufficient data.

DeepSeek V4 Pro · Zhihu · 30 days of rolling 7-day experience scores 2030405060 Observed-day mean: 42.4 2026-09-04 · 50.1 · n=5 · 2026-08-28T23:00:03+00:00 – 2026-09-04T23:00:03+00:002026-09-05 · 46.2 · n=6 · 2026-08-29T23:00:03+00:00 – 2026-09-05T23:00:03+00:002026-09-06 · 44.3 · n=7 · 2026-08-30T23:00:02+00:00 – 2026-09-06T23:00:02+00:002026-09-07 · 47.2 · n=6 · 2026-08-31T23:00:03+00:00 – 2026-09-07T23:00:03+00:002026-09-08 · 48.4 · n=12 · 2026-09-01T23:00:03+00:00 – 2026-09-08T23:00:03+00:002026-09-09 · 29.0 · n=32 · 2026-09-02T23:00:04+00:00 – 2026-09-09T23:00:04+00:002026-09-10 · 22.9 · n=44 · 2026-09-03T23:00:05+00:00 – 2026-09-10T23:00:05+00:002026-09-11 · 22.4 · n=49 · 2026-09-04T23:00:04+00:00 – 2026-09-11T23:00:04+00:002026-09-12 · 29.9 · n=77 · 2026-09-05T23:57:03+00:00 – 2026-09-12T23:57:03+00:002026-09-13 · 35.2 · n=92 · 2026-09-06T23:57:03+00:00 – 2026-09-13T23:57:03+00:002026-09-14 · 36.0 · n=101 · 2026-09-07T23:57:03+00:00 – 2026-09-14T23:57:03+00:002026-09-15 · 35.1 · n=95 · 2026-09-08T23:57:03+00:00 – 2026-09-15T23:57:03+00:002026-09-16 · 45.6 · n=76 · 2026-09-09T23:57:03+00:00 – 2026-09-16T23:57:03+00:002026-09-17 · 53.9 · n=58 · 2026-09-10T23:57:03+00:00 – 2026-09-17T23:57:03+00:002026-09-18 · 56.2 · n=52 · 2026-09-11T23:57:02+00:00 – 2026-09-18T23:57:02+00:002026-09-19 · 56.4 · n=25 · 2026-09-12T23:57:03+00:00 – 2026-09-19T23:57:03+00:002026-09-20 · 47.5 · n=14 · 2026-09-13T23:57:03+00:00 – 2026-09-20T23:57:03+00:002026-09-21 · 52.0 · n=6 · 2026-09-14T23:57:03+00:00 – 2026-09-21T23:57:03+00:002026-09-22 · 48.1 · n=5 · 2026-09-15T23:57:03+00:00 – 2026-09-22T23:57:03+00:00 48.1 09-0309-1109-1809-2510-02 DeepSeek V4 Pro · Zhihu · 30 days of rolling 7-day experience scores 2030405060 Observed-day mean: 42.4 2026-09-04 · 50.1 · n=5 · 2026-08-28T23:00:03+00:00 – 2026-09-04T23:00:03+00:002026-09-05 · 46.2 · n=6 · 2026-08-29T23:00:03+00:00 – 2026-09-05T23:00:03+00:002026-09-06 · 44.3 · n=7 · 2026-08-30T23:00:02+00:00 – 2026-09-06T23:00:02+00:002026-09-07 · 47.2 · n=6 · 2026-08-31T23:00:03+00:00 – 2026-09-07T23:00:03+00:002026-09-08 · 48.4 · n=12 · 2026-09-01T23:00:03+00:00 – 2026-09-08T23:00:03+00:002026-09-09 · 29.0 · n=32 · 2026-09-02T23:00:04+00:00 – 2026-09-09T23:00:04+00:002026-09-10 · 22.9 · n=44 · 2026-09-03T23:00:05+00:00 – 2026-09-10T23:00:05+00:002026-09-11 · 22.4 · n=49 · 2026-09-04T23:00:04+00:00 – 2026-09-11T23:00:04+00:002026-09-12 · 29.9 · n=77 · 2026-09-05T23:57:03+00:00 – 2026-09-12T23:57:03+00:002026-09-13 · 35.2 · n=92 · 2026-09-06T23:57:03+00:00 – 2026-09-13T23:57:03+00:002026-09-14 · 36.0 · n=101 · 2026-09-07T23:57:03+00:00 – 2026-09-14T23:57:03+00:002026-09-15 · 35.1 · n=95 · 2026-09-08T23:57:03+00:00 – 2026-09-15T23:57:03+00:002026-09-16 · 45.6 · n=76 · 2026-09-09T23:57:03+00:00 – 2026-09-16T23:57:03+00:002026-09-17 · 53.9 · n=58 · 2026-09-10T23:57:03+00:00 – 2026-09-17T23:57:03+00:002026-09-18 · 56.2 · n=52 · 2026-09-11T23:57:02+00:00 – 2026-09-18T23:57:02+00:002026-09-19 · 56.4 · n=25 · 2026-09-12T23:57:03+00:00 – 2026-09-19T23:57:03+00:002026-09-20 · 47.5 · n=14 · 2026-09-13T23:57:03+00:00 – 2026-09-20T23:57:03+00:002026-09-21 · 52.0 · n=6 · 2026-09-14T23:57:03+00:00 – 2026-09-21T23:57:03+00:002026-09-22 · 48.1 · n=5 · 2026-09-15T23:57:03+00:00 – 2026-09-22T23:57:03+00:00 48.1 09-0309-1109-1809-2510-02
Daily readings & sample sizes
DateScorenScoring window (UTC)
2026-10-02—22026-09-25T21:57:03+00:00 – 2026-10-02T21:57:03+00:00
2026-10-01—22026-09-24T23:57:02+00:00 – 2026-10-01T23:57:02+00:00
2026-09-30—12026-09-23T23:57:03+00:00 – 2026-09-30T23:57:03+00:00
2026-09-29—12026-09-22T23:57:03+00:00 – 2026-09-29T23:57:03+00:00
2026-09-28—12026-09-21T23:57:03+00:00 – 2026-09-28T23:57:03+00:00
2026-09-27—02026-09-20T23:57:03+00:00 – 2026-09-27T23:57:03+00:00
2026-09-26—12026-09-19T23:57:03+00:00 – 2026-09-26T23:57:03+00:00
2026-09-25—22026-09-18T23:57:03+00:00 – 2026-09-25T23:57:03+00:00
2026-09-24—22026-09-17T23:57:03+00:00 – 2026-09-24T23:57:03+00:00
2026-09-23—22026-09-16T23:57:03+00:00 – 2026-09-23T23:57:03+00:00
2026-09-2248.152026-09-15T23:57:03+00:00 – 2026-09-22T23:57:03+00:00
2026-09-2152.062026-09-14T23:57:03+00:00 – 2026-09-21T23:57:03+00:00
2026-09-2047.5142026-09-13T23:57:03+00:00 – 2026-09-20T23:57:03+00:00
2026-09-1956.4252026-09-12T23:57:03+00:00 – 2026-09-19T23:57:03+00:00
2026-09-1856.2522026-09-11T23:57:02+00:00 – 2026-09-18T23:57:02+00:00
2026-09-1753.9582026-09-10T23:57:03+00:00 – 2026-09-17T23:57:03+00:00
2026-09-1645.6762026-09-09T23:57:03+00:00 – 2026-09-16T23:57:03+00:00
2026-09-1535.1952026-09-08T23:57:03+00:00 – 2026-09-15T23:57:03+00:00
2026-09-1436.01012026-09-07T23:57:03+00:00 – 2026-09-14T23:57:03+00:00
2026-09-1335.2922026-09-06T23:57:03+00:00 – 2026-09-13T23:57:03+00:00
2026-09-1229.9772026-09-05T23:57:03+00:00 – 2026-09-12T23:57:03+00:00
2026-09-1122.4492026-09-04T23:00:04+00:00 – 2026-09-11T23:00:04+00:00
2026-09-1022.9442026-09-03T23:00:05+00:00 – 2026-09-10T23:00:05+00:00
2026-09-0929.0322026-09-02T23:00:04+00:00 – 2026-09-09T23:00:04+00:00
2026-09-0848.4122026-09-01T23:00:03+00:00 – 2026-09-08T23:00:03+00:00
2026-09-0747.262026-08-31T23:00:03+00:00 – 2026-09-07T23:00:03+00:00
2026-09-0644.372026-08-30T23:00:02+00:00 – 2026-09-06T23:00:02+00:00
2026-09-0546.262026-08-29T23:00:03+00:00 – 2026-09-05T23:00:03+00:00
2026-09-0450.152026-08-28T23:00:03+00:00 – 2026-09-04T23:00:03+00:00
2026-09-03—42026-08-27T23:00:03+00:00 – 2026-09-03T23:00:03+00:00

Zhihu · Not enough opinions · 2 scored comments · Read platform reviews →

ONE MODEL, DIFFERENT COMMUNITIES

Across the communities

Last 7 days

Scored separately for each platform, with different samples and audiences. Platform scores are not simply averaged; scored counts exclude quota-only comments.

Scores range from 0 to 100. Select a platform for its trend and reviews. Latest opinion time is not crawler health.

Community reviews

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

0 selected excerpts

No selected comments for this filter in the last 7 days.

Explore long-term changes & analysis

Model lifecycle

DeepSeek V4 Pro

Released 2026-04-24 · Back to DeepSeek

Exact-version signals observed 2026-09-23–2026-10-01 · n=19

Data window: 2026-09-04 00:00:00 to 2026-10-02 00:00:00 UTC · Scoring method: experience_score_v3.

Insufficient historical baseline The original baseline window lacks enough current-method evidence for a long-term comparison.

Lifecycle updated daily · latest complete UTC day 2026-10-01

The current classification method has insufficient evidence in the original baseline window. The window is preserved; no long-term improvement or decline is inferred, and old-method scores are not compared.

Fixed 14-day baseline
–
n=0
Latest 28 days
–
n=19
Change vs baseline
–
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.

–

0 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 → currentnot enough datan=0 → 2 Category change– Weight share– Experience contribution– Discussion-mix contribution–
Image generation Baseline → currentnot enough datan=0 → 0 Category change– Weight share– Experience contribution– Discussion-mix contribution–
Image understanding Baseline → currentnot enough datan=0 → 1 Category change– Weight share– Experience contribution– Discussion-mix contribution–
Local deploy Baseline → currentnot enough datan=0 → 1 Category change– Weight share– Experience contribution– Discussion-mix contribution–
Reasoning Baseline → currentnot enough datan=0 → 3 Category change– Weight share– Experience contribution– Discussion-mix contribution–
Roleplay / creative Baseline → currentnot enough datan=0 → 1 Category change– Weight share– Experience contribution– Discussion-mix contribution–
Safety & refusals Baseline → currentnot enough datan=0 → 0 Category change– Weight share– Experience contribution– Discussion-mix contribution–
Speed & latency Baseline → currentnot enough datan=0 → 1 Category change– Weight share– Experience contribution– Discussion-mix contribution–
General text Baseline → currentnot enough datan=0 → 10 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–

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.

How's your AI experience today?