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GLM · Last 7 days

GLM 5.2

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

Updated 2026-09-08 17:31 UTC · 2026-09-01 – 2026-09-08 UTC
Community score72.4/10036 comments in 7 days

As of 2026-09-08 17:00:03 UTC, GLM 5.2 in the GLM family has 36 explicitly attributed community comments in the last 7 days; its community score is 72.4/100. Available category scores include General text 69.1/100 (n=14); Coding 63.8/100 (n=11); Reasoning 63.5/100 (n=6). 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.

15 selected excerpts

PositiveGeneral text

“GLM 5.2 (perf quasi frontier, avec parfois -très rarement- qqs glitchs style idéogrammes)”

PositiveCoding

“Working with it in VS Code with GLM 5.2 is so good!”

PositiveGeneral text

“GLM5.2's response in workbuddy is as follows: (excerpt) Key point: Anchor for full-chain ID tracking: Identified or temporarily stored as raw material, evidence book sorted according to evidence list, page numbers won't be messed up or drift. Answer your five levels point by point”

ZhihuzhMachine translated
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GLM5.2在workbuddy的回答如下:(摘录)重点: 全链路ID追踪的锚点: 识别出来或者暂存的作为原料,证据册根据证据清单排序,页码不会乱,不会漂移。 逐点回答你的五个层次

PositiveGeneral text

“I prefer it over 5.3 and 5.3-flash because it feels more verbose and answers questions more detailed”

PositiveReasoning

“I think that the glm 5.2 move was a right direction”

NegativeGeneral text

“I say this while having a devin cli subscription that gives me a "free and unlimited" GLM 5.2 that really sucks as I explained in my original comment.”

PositiveCoding

“With GLM 5.2 -Mistral added it to Vibe this week- you reach Opus 4.5/Sonnet 5 level of usefulness”

NegativeCoding

“The GLM 5.2 they offer is highly capped, medium reasoning, and who knows which quant. I can tell you qwen3.8 27B single shots tasks that GLM 5.2 (quantized, medium reasoning, free tier) takes a full day at and still struggles.”

PositiveReasoning

“it reasoned "I had permission to use this script in the past (earlier in the session) so it edited the script with the commands it was asking for and accomplished its goal”

NegativeReasoning

“GLM 5.2 on the 60 dollar plan was great at following instructions, but lacked the actual good thought that Opus and Fable have.”

NeutralSpeed & latency

“GLM 5.2 ~210–235 tok/s aggregate For 16 concurrent streams Prefill: ~3,200–3,500 tok/s aggregate”

PositiveSpeed & latency

“the model seems very fast”

PositiveSafety & refusals

“From what I've heard, GLM 5.3 is extremely censored, while 5.2 is comparatively uncensored.”

NegativeGeneral text

“Compressed the Chinese model glm5.2, performance directly dropped by ten points, this is distillation and they can't even distill properly”

ZhihuzhMachine translated
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压缩国模glm5.2,性能直接低了十分,这就是蒸馏都不会蒸

NegativeSpeed & latency

“The same level GLM-5.2 costs around 28”

ZhihuzhMachine translated
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同级别的 GLM-5.2 要 28 左右

Explore long-term changes & analysis

Model lifecycle

GLM 5.2

Released 2026-06-16 · Back to GLM

Exact-version signals observed 2026-08-07–2026-09-07 · n=294

Data window: 2026-08-11 00:00:00 to 2026-09-08 00:00:00 UTC · Scoring method: experience_score_v2.

Trend history still collecting The current window is ready; 2 of 4 independent segments are available.

Lifecycle updated daily · latest complete UTC day 2026-09-07

Fixed 14-day baseline
74.7
n=90
Latest 28 days
66.7
n=244
Change vs baseline
−8.0
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 2026-08-13 to 2026-09-07; latest score 60.5, fixed baseline 74.7.4050607080Fixed baseline 74.72026-07-30–2026-08-13 · 74.7 · n=902026-07-31–2026-08-14 · 74.6 · n=1202026-08-01–2026-08-15 · 74.2 · n=1382026-08-02–2026-08-16 · 74.1 · n=1432026-08-03–2026-08-17 · 73.9 · n=1542026-08-04–2026-08-18 · 72.2 · n=1632026-08-05–2026-08-19 · 71.7 · n=1722026-08-06–2026-08-20 · 71.7 · n=1772026-08-07–2026-08-21 · 70.3 · n=1682026-08-08–2026-08-22 · 71.0 · n=1582026-08-09–2026-08-23 · 70.6 · n=1582026-08-10–2026-08-24 · 69.8 · n=1592026-08-11–2026-08-25 · 68.5 · n=1542026-08-12–2026-08-26 · 65.3 · n=1612026-08-13–2026-08-27 · 65.9 · n=1462026-08-14–2026-08-28 · 63.4 · n=1212026-08-15–2026-08-29 · 59.1 · n=1092026-08-16–2026-08-30 · 59.3 · n=1112026-08-17–2026-08-31 · 58.0 · n=1012026-08-18–2026-09-01 · 60.4 · n=1012026-08-19–2026-09-02 · 61.7 · n=952026-08-20–2026-09-03 · 59.8 · n=942026-08-21–2026-09-04 · 61.6 · n=912026-08-22–2026-09-05 · 61.7 · n=962026-08-23–2026-09-06 · 60.7 · n=922026-08-24–2026-09-07 · 60.5 · n=8708-1308-1808-2308-2809-0209-07

2 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.

Largest negative experience contributions: General text, Speed & latency, Reasoning.

Category Baseline → current Category change Weight share Experience contribution Discussion-mix contribution
General text Baseline → current69.3 → 62.6n=30 → 79 Category change−6.6 Weight share33.1% → 31.3% Experience contribution−2.14 Discussion-mix contribution+0.02
Speed & latency Baseline → current56.1 → 44.9n=16 → 43 Category change−11.2 Weight share17.8% → 17.2% Experience contribution−1.96 Discussion-mix contribution+0.09
Reasoning Baseline → current76.4 → 71.6n=21 → 55 Category change−4.8 Weight share24.7% → 23.1% Experience contribution−1.16 Discussion-mix contribution−0.12
Coding Baseline → current72.7 → 78.6n=20 → 54 Category change+5.9 Weight share21.3% → 23.3% Experience contribution+1.31 Discussion-mix contribution+0.17
Image / vision Baseline → currentnot enough datan=0 → 0 Category change Weight share Experience contribution Discussion-mix contribution
Local deploy Baseline → currentnot enough datan=3 → 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 → 6 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: −4.24 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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