GLM · Last 7 days

GLM 5.2

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

Updated 2026-10-04 08:32 UTC · 2026-09-27 – 2026-10-04 UTC
All-platform score71.0/10021 comments in 7 days

Discussion overview

What the full sample discusses

All platforms · 21 eligible comments over 7 days, including usage-limit feedback; deduplicated by source and comment.

  • General text · 14 comments: 12 positive / 2 negative / 0 mixed or neutral
  • Reasoning · 5 comments: 3 positive / 2 negative / 0 mixed or neutral
  • Coding · 1 comments: 1 positive / 0 negative / 0 mixed or neutral
  • Local deploy · 1 comments: 1 positive / 0 negative / 0 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: +16.5 points vs 7 days ago. Discussion mix can affect scores; this does not establish a cause.

Source coverage and discussion concentration

Hacker News 5 · Reddit 8 · Zhihu 8

10 identifiable discussions cover 16/21 comments; the largest has 6. Remaining thread identities are unknown; this is not a count of independent users.

Selected individual opinions

Explore individual experiences below, filtered by category and sentiment.

About the sample and scores

As of 2026-10-04 08:32:03 UTC, GLM 5.2 in the GLM family has 21 explicitly attributed community comments in the last 7 days; its community score is 71.0/100. Available category scores include General text 72.2/100 (n=14); Reasoning 52.9/100 (n=5). 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.

GLM 5.2 · Hacker News · 30 days of rolling 7-day experience scores 40506070 Observed-day mean: 68.4 2026-10-02 · 68.4 · n=5 · 2026-09-25T23:57:03+00:00 – 2026-10-02T23:57:03+00:002026-10-03 · 68.4 · n=5 · 2026-09-26T23:57:02+00:00 – 2026-10-03T23:57:02+00:002026-10-04 · 68.4 · n=5 · 2026-09-27T08:32:03+00:00 – 2026-10-04T08:32:03+00:00 68.4 09-0509-1309-2009-2710-04 GLM 5.2 · Hacker News · 30 days of rolling 7-day experience scores 40506070 Observed-day mean: 68.4 2026-10-02 · 68.4 · n=5 · 2026-09-25T23:57:03+00:00 – 2026-10-02T23:57:03+00:002026-10-03 · 68.4 · n=5 · 2026-09-26T23:57:02+00:00 – 2026-10-03T23:57:02+00:002026-10-04 · 68.4 · n=5 · 2026-09-27T08:32:03+00:00 – 2026-10-04T08:32:03+00:00 68.4 09-0509-1309-2009-2710-04
Daily readings & sample sizes
DateScorenScoring window (UTC)
2026-10-0468.452026-09-27T08:32:03+00:00 – 2026-10-04T08:32:03+00:00
2026-10-0368.452026-09-26T23:57:02+00:00 – 2026-10-03T23:57:02+00:00
2026-10-0268.452026-09-25T23:57:03+00:00 – 2026-10-02T23:57:03+00:00
2026-10-01—32026-09-24T23:57:02+00:00 – 2026-10-01T23:57:02+00:00
2026-09-30—32026-09-23T23:57:03+00:00 – 2026-09-30T23:57:03+00:00
2026-09-29—32026-09-22T23:57:03+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—12026-09-20T23:57:03+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—12026-09-18T23:57:03+00:00 – 2026-09-25T23:57:03+00:00
2026-09-24—12026-09-17T23:57:03+00:00 – 2026-09-24T23:57:03+00:00
2026-09-23—12026-09-16T23:57:03+00:00 – 2026-09-23T23:57:03+00:00
2026-09-22—02026-09-15T23:57:03+00:00 – 2026-09-22T23:57:03+00:00
2026-09-21—02026-09-14T23:57:03+00:00 – 2026-09-21T23:57:03+00:00
2026-09-20—02026-09-13T23:57:03+00:00 – 2026-09-20T23:57:03+00:00
2026-09-19—02026-09-12T23:57:03+00:00 – 2026-09-19T23:57:03+00:00
2026-09-18—02026-09-11T23:57:02+00:00 – 2026-09-18T23:57:02+00:00
2026-09-17—12026-09-10T23:57:03+00:00 – 2026-09-17T23:57:03+00:00
2026-09-16—12026-09-09T23:57:03+00:00 – 2026-09-16T23:57:03+00:00
2026-09-15—22026-09-08T23:57:03+00:00 – 2026-09-15T23:57:03+00:00
2026-09-14—22026-09-07T23:57:03+00:00 – 2026-09-14T23:57:03+00:00
2026-09-13—32026-09-06T23:57:03+00:00 – 2026-09-13T23:57:03+00:00
2026-09-12—32026-09-05T23:57:03+00:00 – 2026-09-12T23:57:03+00:00
2026-09-11—32026-09-04T23:00:04+00:00 – 2026-09-11T23:00:04+00:00
2026-09-10—32026-09-03T23:00:05+00:00 – 2026-09-10T23:00:05+00:00
2026-09-09—32026-09-02T23:00:04+00:00 – 2026-09-09T23:00:04+00:00
2026-09-08—42026-09-01T23:00:03+00:00 – 2026-09-08T23:00:03+00:00
2026-09-07—42026-08-31T23:00:03+00:00 – 2026-09-07T23:00:03+00:00
2026-09-06—32026-08-30T23:00:02+00:00 – 2026-09-06T23:00:02+00:00
2026-09-05—32026-08-29T23:00:03+00:00 – 2026-09-05T23:00:03+00:00

Hacker News · 68.4 · 5 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

GLM 5.2

Released 2026-06-16 · Back to GLM

Exact-version signals observed 2026-09-23–2026-10-03 · n=27

Data window: 2026-09-06 00:00:00 to 2026-10-04 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 2026-10-03

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=26
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 → 1 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 → 0 Category change– Weight share– Experience contribution– Discussion-mix contribution–
Local deploy Baseline → currentnot enough datan=0 → 2 Category change– Weight share– Experience contribution– Discussion-mix contribution–
Reasoning Baseline → currentnot enough datan=0 → 7 Category change– Weight share– Experience contribution– Discussion-mix contribution–
Roleplay / creative Baseline → currentnot enough datan=0 → 0 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 → 0 Category change– Weight share– Experience contribution– Discussion-mix contribution–
General text Baseline → currentnot enough datan=0 → 16 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?