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DeepSeek · Last 7 days

DeepSeek V4 Pro

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

Updated 2026-10-03 16:37 UTC · 2026-09-26 – 2026-10-03 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 2026-10-03 16:37:03 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

−29.2vs 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 · Reddit · 30 days of rolling 7-day experience scores 4050607080 Observed-day mean: 59.3 2026-09-04 · 52.0 · n=23 · 2026-08-28T23:00:03+00:00 – 2026-09-04T23:00:03+00:002026-09-05 · 53.2 · n=24 · 2026-08-29T23:00:03+00:00 – 2026-09-05T23:00:03+00:002026-09-06 · 54.7 · n=22 · 2026-08-30T23:00:02+00:00 – 2026-09-06T23:00:02+00:002026-09-07 · 54.9 · n=21 · 2026-08-31T23:00:03+00:00 – 2026-09-07T23:00:03+00:002026-09-08 · 55.7 · n=20 · 2026-09-01T23:00:03+00:00 – 2026-09-08T23:00:03+00:002026-09-09 · 54.8 · n=44 · 2026-09-02T23:00:04+00:00 – 2026-09-09T23:00:04+00:002026-09-10 · 51.3 · n=46 · 2026-09-03T23:00:05+00:00 – 2026-09-10T23:00:05+00:002026-09-11 · 51.0 · n=42 · 2026-09-04T23:00:04+00:00 – 2026-09-11T23:00:04+00:002026-09-12 · 54.9 · n=58 · 2026-09-05T23:57:03+00:00 – 2026-09-12T23:57:03+00:002026-09-13 · 54.4 · n=60 · 2026-09-06T23:57:03+00:00 – 2026-09-13T23:57:03+00:002026-09-14 · 55.0 · n=66 · 2026-09-07T23:57:03+00:00 – 2026-09-14T23:57:03+00:002026-09-15 · 54.4 · n=71 · 2026-09-08T23:57:03+00:00 – 2026-09-15T23:57:03+00:002026-09-16 · 56.5 · n=45 · 2026-09-09T23:57:03+00:00 – 2026-09-16T23:57:03+00:002026-09-17 · 63.2 · n=38 · 2026-09-10T23:57:03+00:00 – 2026-09-17T23:57:03+00:002026-09-18 · 64.0 · n=25 · 2026-09-11T23:57:02+00:00 – 2026-09-18T23:57:02+00:002026-09-19 · 64.4 · n=23 · 2026-09-12T23:57:03+00:00 – 2026-09-19T23:57:03+00:002026-09-20 · 65.9 · n=20 · 2026-09-13T23:57:03+00:00 – 2026-09-20T23:57:03+00:002026-09-21 · 66.6 · n=14 · 2026-09-14T23:57:03+00:00 – 2026-09-21T23:57:03+00:002026-09-22 · 78.6 · n=11 · 2026-09-15T23:57:03+00:00 – 2026-09-22T23:57:03+00:002026-09-23 · 76.2 · n=9 · 2026-09-16T23:57:03+00:00 – 2026-09-23T23:57:03+00:002026-09-24 · 77.5 · n=10 · 2026-09-17T23:57:03+00:00 – 2026-09-24T23:57:03+00:002026-09-25 · 74.7 · n=8 · 2026-09-18T23:57:03+00:00 – 2026-09-25T23:57:03+00:002026-09-26 · 71.2 · n=6 · 2026-09-19T23:57:03+00:00 – 2026-09-26T23:57:03+00:002026-09-27 · 66.3 · n=9 · 2026-09-20T23:57:03+00:00 – 2026-09-27T23:57:03+00:002026-09-28 · 64.6 · n=8 · 2026-09-21T23:57:03+00:00 – 2026-09-28T23:57:03+00:002026-09-29 · 62.2 · n=7 · 2026-09-22T23:57:03+00:00 – 2026-09-29T23:57:03+00:002026-09-30 · 55.9 · n=5 · 2026-09-23T23:57:03+00:00 – 2026-09-30T23:57:03+00:002026-10-01 · 40.7 · n=12 · 2026-09-24T23:57:02+00:00 – 2026-10-01T23:57:02+00:002026-10-02 · 42.0 · n=14 · 2026-09-25T23:57:03+00:00 – 2026-10-02T23:57:03+00:002026-10-03 · 42.0 · n=14 · 2026-09-26T16:37:03+00:00 – 2026-10-03T16:37:03+00:00 42.0 09-0409-1209-1909-2610-03 DeepSeek V4 Pro · Reddit · 30 days of rolling 7-day experience scores 4050607080 Observed-day mean: 59.3 2026-09-04 · 52.0 · n=23 · 2026-08-28T23:00:03+00:00 – 2026-09-04T23:00:03+00:002026-09-05 · 53.2 · n=24 · 2026-08-29T23:00:03+00:00 – 2026-09-05T23:00:03+00:002026-09-06 · 54.7 · n=22 · 2026-08-30T23:00:02+00:00 – 2026-09-06T23:00:02+00:002026-09-07 · 54.9 · n=21 · 2026-08-31T23:00:03+00:00 – 2026-09-07T23:00:03+00:002026-09-08 · 55.7 · n=20 · 2026-09-01T23:00:03+00:00 – 2026-09-08T23:00:03+00:002026-09-09 · 54.8 · n=44 · 2026-09-02T23:00:04+00:00 – 2026-09-09T23:00:04+00:002026-09-10 · 51.3 · n=46 · 2026-09-03T23:00:05+00:00 – 2026-09-10T23:00:05+00:002026-09-11 · 51.0 · n=42 · 2026-09-04T23:00:04+00:00 – 2026-09-11T23:00:04+00:002026-09-12 · 54.9 · n=58 · 2026-09-05T23:57:03+00:00 – 2026-09-12T23:57:03+00:002026-09-13 · 54.4 · n=60 · 2026-09-06T23:57:03+00:00 – 2026-09-13T23:57:03+00:002026-09-14 · 55.0 · n=66 · 2026-09-07T23:57:03+00:00 – 2026-09-14T23:57:03+00:002026-09-15 · 54.4 · n=71 · 2026-09-08T23:57:03+00:00 – 2026-09-15T23:57:03+00:002026-09-16 · 56.5 · n=45 · 2026-09-09T23:57:03+00:00 – 2026-09-16T23:57:03+00:002026-09-17 · 63.2 · n=38 · 2026-09-10T23:57:03+00:00 – 2026-09-17T23:57:03+00:002026-09-18 · 64.0 · n=25 · 2026-09-11T23:57:02+00:00 – 2026-09-18T23:57:02+00:002026-09-19 · 64.4 · n=23 · 2026-09-12T23:57:03+00:00 – 2026-09-19T23:57:03+00:002026-09-20 · 65.9 · n=20 · 2026-09-13T23:57:03+00:00 – 2026-09-20T23:57:03+00:002026-09-21 · 66.6 · n=14 · 2026-09-14T23:57:03+00:00 – 2026-09-21T23:57:03+00:002026-09-22 · 78.6 · n=11 · 2026-09-15T23:57:03+00:00 – 2026-09-22T23:57:03+00:002026-09-23 · 76.2 · n=9 · 2026-09-16T23:57:03+00:00 – 2026-09-23T23:57:03+00:002026-09-24 · 77.5 · n=10 · 2026-09-17T23:57:03+00:00 – 2026-09-24T23:57:03+00:002026-09-25 · 74.7 · n=8 · 2026-09-18T23:57:03+00:00 – 2026-09-25T23:57:03+00:002026-09-26 · 71.2 · n=6 · 2026-09-19T23:57:03+00:00 – 2026-09-26T23:57:03+00:002026-09-27 · 66.3 · n=9 · 2026-09-20T23:57:03+00:00 – 2026-09-27T23:57:03+00:002026-09-28 · 64.6 · n=8 · 2026-09-21T23:57:03+00:00 – 2026-09-28T23:57:03+00:002026-09-29 · 62.2 · n=7 · 2026-09-22T23:57:03+00:00 – 2026-09-29T23:57:03+00:002026-09-30 · 55.9 · n=5 · 2026-09-23T23:57:03+00:00 – 2026-09-30T23:57:03+00:002026-10-01 · 40.7 · n=12 · 2026-09-24T23:57:02+00:00 – 2026-10-01T23:57:02+00:002026-10-02 · 42.0 · n=14 · 2026-09-25T23:57:03+00:00 – 2026-10-02T23:57:03+00:002026-10-03 · 42.0 · n=14 · 2026-09-26T16:37:03+00:00 – 2026-10-03T16:37:03+00:00 42.0 09-0409-1209-1909-2610-03
Daily readings & sample sizes
DateScorenScoring window (UTC)
2026-10-0342.0142026-09-26T16:37:03+00:00 – 2026-10-03T16:37:03+00:00
2026-10-0242.0142026-09-25T23:57:03+00:00 – 2026-10-02T23:57:03+00:00
2026-10-0140.7122026-09-24T23:57:02+00:00 – 2026-10-01T23:57:02+00:00
2026-09-3055.952026-09-23T23:57:03+00:00 – 2026-09-30T23:57:03+00:00
2026-09-2962.272026-09-22T23:57:03+00:00 – 2026-09-29T23:57:03+00:00
2026-09-2864.682026-09-21T23:57:03+00:00 – 2026-09-28T23:57:03+00:00
2026-09-2766.392026-09-20T23:57:03+00:00 – 2026-09-27T23:57:03+00:00
2026-09-2671.262026-09-19T23:57:03+00:00 – 2026-09-26T23:57:03+00:00
2026-09-2574.782026-09-18T23:57:03+00:00 – 2026-09-25T23:57:03+00:00
2026-09-2477.5102026-09-17T23:57:03+00:00 – 2026-09-24T23:57:03+00:00
2026-09-2376.292026-09-16T23:57:03+00:00 – 2026-09-23T23:57:03+00:00
2026-09-2278.6112026-09-15T23:57:03+00:00 – 2026-09-22T23:57:03+00:00
2026-09-2166.6142026-09-14T23:57:03+00:00 – 2026-09-21T23:57:03+00:00
2026-09-2065.9202026-09-13T23:57:03+00:00 – 2026-09-20T23:57:03+00:00
2026-09-1964.4232026-09-12T23:57:03+00:00 – 2026-09-19T23:57:03+00:00
2026-09-1864.0252026-09-11T23:57:02+00:00 – 2026-09-18T23:57:02+00:00
2026-09-1763.2382026-09-10T23:57:03+00:00 – 2026-09-17T23:57:03+00:00
2026-09-1656.5452026-09-09T23:57:03+00:00 – 2026-09-16T23:57:03+00:00
2026-09-1554.4712026-09-08T23:57:03+00:00 – 2026-09-15T23:57:03+00:00
2026-09-1455.0662026-09-07T23:57:03+00:00 – 2026-09-14T23:57:03+00:00
2026-09-1354.4602026-09-06T23:57:03+00:00 – 2026-09-13T23:57:03+00:00
2026-09-1254.9582026-09-05T23:57:03+00:00 – 2026-09-12T23:57:03+00:00
2026-09-1151.0422026-09-04T23:00:04+00:00 – 2026-09-11T23:00:04+00:00
2026-09-1051.3462026-09-03T23:00:05+00:00 – 2026-09-10T23:00:05+00:00
2026-09-0954.8442026-09-02T23:00:04+00:00 – 2026-09-09T23:00:04+00:00
2026-09-0855.7202026-09-01T23:00:03+00:00 – 2026-09-08T23:00:03+00:00
2026-09-0754.9212026-08-31T23:00:03+00:00 – 2026-09-07T23:00:03+00:00
2026-09-0654.7222026-08-30T23:00:02+00:00 – 2026-09-06T23:00:02+00:00
2026-09-0553.2242026-08-29T23:00:03+00:00 – 2026-09-05T23:00:03+00:00
2026-09-0452.0232026-08-28T23:00:03+00:00 – 2026-09-04T23:00:03+00:00

Reddit · 42.0 · 14 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.

RedditLatest scored opinion 2026-10-02 09:39 UTC42.014 opinionsvs 7d ago −29.2Hacker NewsLatest scored opinion 2026-09-29 22:12 UTC—2 opinionsInsufficient sampleZhihuLatest scored opinion 2026-10-01 03:20 UTC—2 opinionsInsufficient sampleRedNoteNo scored opinion timestamp—0 opinionsInsufficient sample

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.

Platform
Sentiment

0 selected excerpts

Community reports about limits cannot establish official allowances.

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 2026-09-23–2026-10-02 · n=20

Data window: 2026-09-05 00:00:00 to 2026-10-03 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 2026-10-02

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=20
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 → 11 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?