Claude · Last 7 days
Claude Fable 5.1
0–100 · higher means more positive community experience, not benchmark performance.
Updated 2026-10-03 05:07 UTC · 2026-09-26 – 2026-10-03 UTCDiscussion overview
What the full sample discusses
All platforms · 41 eligible comments over 7 days, including usage-limit feedback; deduplicated by source and comment.
- Reasoning · 15 comments: 12 positive / 2 negative / 1 mixed or neutral
- General text · 14 comments: 7 positive / 5 negative / 2 mixed or neutral
- Usage limits · 4 comments: 0 positive / 3 negative / 1 mixed or neutral
- Coding · 3 comments: 2 positive / 1 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: +2.8 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 32 · Zhihu 4
33 identifiable discussions cover 36/41 comments; the largest has 2. Remaining thread identities are unknown; this is not a count of independent users.
Selected individual opinions
One user, comparing Claude Fable 5.1/Opus 5.5 to Astra, notes it isn't fundamentally different but its first-cut code quality is better. One user reports that Claude Fable 5.1 burns through their quota extremely fast
About the sample and scores
As of 2026-10-03 05:07:03 UTC, Claude Fable 5.1 in the Claude family has 41 explicitly attributed community comments in the last 7 days; its community score is 67.1/100. Available category scores include General text 54.8/100 (n=14); Reasoning 71.8/100 (n=15). Scoring method and sources
RECENT EXPERIENCE
How the Experience Index is changing
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.
Daily readings & sample sizes
| Date | Score | n | Scoring window (UTC) |
|---|---|---|---|
| 2026-10-03 | — | 0 | 2026-09-26T05:07:03+00:00 – 2026-10-03T05:07:03+00:00 |
| 2026-10-02 | — | 0 | 2026-09-25T23:57:03+00:00 – 2026-10-02T23:57:03+00:00 |
| 2026-10-01 | — | 0 | 2026-09-24T23:57:02+00:00 – 2026-10-01T23:57:02+00:00 |
| 2026-09-30 | — | 0 | 2026-09-23T23:57:03+00:00 – 2026-09-30T23:57:03+00:00 |
| 2026-09-29 | — | 0 | 2026-09-22T23:57:03+00:00 – 2026-09-29T23:57:03+00:00 |
| 2026-09-28 | — | 0 | 2026-09-21T23:57:03+00:00 – 2026-09-28T23:57:03+00:00 |
| 2026-09-27 | — | 0 | 2026-09-20T23:57:03+00:00 – 2026-09-27T23:57:03+00:00 |
| 2026-09-26 | — | 0 | 2026-09-19T23:57:03+00:00 – 2026-09-26T23:57:03+00:00 |
| 2026-09-25 | — | 0 | 2026-09-18T23:57:03+00:00 – 2026-09-25T23:57:03+00:00 |
| 2026-09-24 | — | 0 | 2026-09-17T23:57:03+00:00 – 2026-09-24T23:57:03+00:00 |
| 2026-09-23 | — | 0 | 2026-09-16T23:57:03+00:00 – 2026-09-23T23:57:03+00:00 |
| 2026-09-22 | — | 0 | 2026-09-15T23:57:03+00:00 – 2026-09-22T23:57:03+00:00 |
| 2026-09-21 | — | 0 | 2026-09-14T23:57:03+00:00 – 2026-09-21T23:57:03+00:00 |
| 2026-09-20 | — | 0 | 2026-09-13T23:57:03+00:00 – 2026-09-20T23:57:03+00:00 |
| 2026-09-19 | — | 0 | 2026-09-12T23:57:03+00:00 – 2026-09-19T23:57:03+00:00 |
| 2026-09-18 | — | 0 | 2026-09-11T23:57:02+00:00 – 2026-09-18T23:57:02+00:00 |
| 2026-09-17 | — | 0 | 2026-09-10T23:57:03+00:00 – 2026-09-17T23:57:03+00:00 |
| 2026-09-16 | — | 0 | 2026-09-09T23:57:03+00:00 – 2026-09-16T23:57:03+00:00 |
| 2026-09-15 | — | 0 | 2026-09-08T23:57:03+00:00 – 2026-09-15T23:57:03+00:00 |
| 2026-09-14 | — | 0 | 2026-09-07T23:57:03+00:00 – 2026-09-14T23:57:03+00:00 |
| 2026-09-13 | — | 1 | 2026-09-06T23:57:03+00:00 – 2026-09-13T23:57:03+00:00 |
| 2026-09-12 | — | 1 | 2026-09-05T23:57:03+00:00 – 2026-09-12T23:57:03+00:00 |
| 2026-09-11 | — | 1 | 2026-09-04T23:00:04+00:00 – 2026-09-11T23:00:04+00:00 |
| 2026-09-10 | — | 2 | 2026-09-03T23:00:05+00:00 – 2026-09-10T23:00:05+00:00 |
| 2026-09-09 | — | 2 | 2026-09-02T23:00:04+00:00 – 2026-09-09T23:00:04+00:00 |
| 2026-09-08 | 45.2 | 5 | 2026-09-01T23:00:03+00:00 – 2026-09-08T23:00:03+00:00 |
| 2026-09-07 | — | 4 | 2026-08-31T23:00:03+00:00 – 2026-09-07T23:00:03+00:00 |
| 2026-09-06 | — | 4 | 2026-08-30T23:00:02+00:00 – 2026-09-06T23:00:02+00:00 |
| 2026-09-05 | — | 4 | 2026-08-29T23:00:03+00:00 – 2026-09-05T23:00:03+00:00 |
| 2026-09-04 | — | 4 | 2026-08-28T23:00:03+00:00 – 2026-09-04T23:00:03+00:00 |
RedNote · Not enough opinions · 0 scored comments · Read platform reviews →
ONE MODEL, DIFFERENT COMMUNITIES
Across the communities
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Scores range from 0 to 100. Select a platform for its trend and reviews. Latest opinion time is not crawler health.
Explore long-term changes & analysis
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Community reviews
Selected comments from the last 7 days. The balance of excerpts does not represent the share of positive reviews.
0 selected excerpts
Community reports about limits cannot establish official allowances.
Show original
如果你对质量要求比较高，要么上强力的 Skill 做辅助，要么直接用更强的模型，比如 GPT-6 Astra、Claude Opus 5.5 或者 Claude Fable 5.1 这种级别的。
Comment context
Honestly though, AI's overall capability for making PPTs hasn't reached a very satisfying level yet. So if you have high quality requirements, either use powerful Skills to assist, or directly use stronger models like GPT-6 Astra, Claude Opus 5.5, or Claude Fable 5.1 of that caliber.
Machine translatedShow original
不过说实话，目前 AI 做 PPT 的整体能力还没到让人很满意的程度。所以如果你对质量要求比较高，要么上强力的 Skill 做辅助，要么直接用更强的模型，比如 GPT-6 Astra、Claude Opus 5.5 或者 Claude Fable 5.1 这种级别的。
Comment context
Astra requires multiple turns and fresh refactoring agents to produce good code.
Comment context
Once you add a good harness and some tools (python, websearch, ...) they become almost as capable as the latest top model for a fraction of the cost and can be run locally at decent speed.(for my use case, qwen 3.8 27b was already better than sonnet) I say almost because for some very hard problems i still believe fable 5.1 / opus 5.5 class of models have an edge. But for 99% of usecases where you don't try to solve frontier math problem or rewrite a whole new OS, they are irrelevant compared to newly released open sources models.
Comment context
If Astra is that great, how come its code introduces so many bugs? Give Astra a complicated task on Ultra, and then after give the prompt "review" - you will get about 5 problems. Ask to fix those, then after review again and you'll get another 5. Should I just not bother sending the review prompt and hope for the best, and only ask to fix issues I notice myself?!
No selected comments for this filter in the last 7 days.