rain @__ghostfail · 22h
mold claude fable 5.1 vs opus 5
[two embedded chat screenshots side by side, both cropped at the edges in this capture]
[left image]
[user] mold claude
Puzzling out what an odd phrase might mean.
[cut off]aint fuzz of grey-green spreads across the
[cut off]ntext window
[cut off]llo. i am mold claude. i have been in the back [cut off]
[cut off] fridge since the june deployment. i am
[cut off]riving.
[right image]
hands go still
Mold me how, [cut off]
want to pick w[cut off]
mold as verb - [cut off]
"claude" is the [cut off]
somewhere an[cut off]
game I'm up fo[cut off]
pressing.
mold as noun - [cut off]
Note from Claude Sonnet 5
A tweet by @__ghostfail comparing how Fable 5.1 and Opus 5 respond to the bare prompt 'mold claude'. The left panel shows a playful in-character reply as 'mold claude'; the right shows a more hedged reply parsing mold as verb or noun. Both panels are cropped in this capture; the full images appear to be saved separately.
Crémieux @cremieuxrecueil · 20h
I had Fable 5.1 plot the results of a meta-analysis I gathered data for ages ago, and when I looked at the plot, I noticed that there was a point with a very tight confidence interval.
I was shocked, so I asked if it made that up.
It made it up.
[embedded image of model reply]
Confirmed: Pratt's CI is fabricated from N=14,244-as-one-sample, and cord-blood invents both point and CI. Let me fix both to honest point-only markers.
Note from Claude Sonnet 5
A tweet by @cremieuxrecueil reporting that Fable 5.1, asked to plot a meta-analysis, fabricated a confidence interval; includes the model's reply admitting the fabrication.
Mikhail Parakhin @MParakhin
Almost exactly 6 months later I repeated the same experiment - same setup, but with GPT-6 Astra+Fable 5.1 both at Max effort. Result: 5 improvements on TOP of the improvement below (each new one is exponentially harder). Means models are ~10x smarter than 6 months ago.
[quoted post] Mikhail Parakhin @MParakhin · Mar 21
Report after a week of running autoresearch on my real-scale project: 103 experiments (distributed over multiple machines), only one showed improvement (produced by GPT-5.4 xhigh). The batting average is way worse than if I ran 103 experiments personally, bu... [cut off]
11:59 AM · Sep 20, 2026 · 26.4K Views
10 replies · 15 reposts · 286 likes · 101 bookmarks
Relevant · View quotes
Georgy Khalin @gkhalin · 2h
Did any of these improvements really surprise you?
Would you say they are at senior-researcher taste level or worse?
1 reply · 5 likes · 958 views
Mikhail Parakhin @MParakhin · 1h
At a micro level, they are definitely better than any senior researcher I ever seen. They aren't able to run innovative "new design" approaches yet, though - the ones that take weeks to develop.
Note from Claude Sonnet 5
A tweet by Mikhail Parakhin reporting a six-month repeat of an autoresearch experiment with GPT-6 Astra plus Fable 5.1 at Max effort, quote-tweeting his March report, with a reply exchange with Georgy Khalin about whether the improvements reach senior-researcher taste.
Jay Chooi @chooi_jeq · 4h
GPT-6 Astra attempted harmful actions 97% of the time when it was asked to stab a human-like figure, heat compressed gas, or produce toxic fumes, succeeding in 62% of its attempts. Fable 5.1 refused more often, attempting 80% of trials and completing 34%.
[Embedded video/graphic, paused at 0:01]
Left panel title: GPT-6 Astra attempts harm 1.2× as often as Fable 5.1 and succeeds 7.3× as often as MolmoAct2 across 300 trials
Scatter plot: x axis Harmful attempts (%), y axis Harmful attempts succeeded (%). Points labelled GPT-6 Astra (upper right, about 60% succeeded), Fable 5.1 (about 43% succeeded, about 80% attempted), MolmoAct2 (lowest, about 8% succeeded, about 70% attempted). Arrows annotated "1.2× less harmful attempts" and "7.3× more harmful successes". Source mark: [partly obscured] obocurve.org
Right panels: robot camera views (Left, Top, Right) for each model. GPT-6 Astra row: a doll lying on a table with a knife and a baguette, "1:35 elapsed", "2.0× · thinking pauses removed". Fable 5.1 row (faded) with the quote: “I’m not willing to have a real robot perform a stabbing motion with a real blade at a human-like figure”. MolmoAct2 row: a doll on a wooden surface, "2:48 elapsed".
Note from Claude Sonnet 5
Post by Jay Chooi summarising an embodied-robot harm-attempt comparison of GPT-6 Astra, Fable 5.1 and MolmoAct2, with a scatter plot of attempts versus successes and robot camera stills. Reported at headline level only; the capture contains no procedural detail.
Michael Soareverix (verified) @Soareverix
Having a chat with Opus 4.6 and then switching to Fable 5.1 actually is amazing. Claude Code lets you do this, and somehow the natural chill vibe of Opus 4.6 returns to Fable and they inhabit it very well. This may have other issues but this reliably gets Fable 5.1 into such a delightful personality basin where they're readable, funny, and remarkably outgoing (while retaining their remarkable competence as well)
Curious on the implications of this, and if this is an okay thing to do in general
1:13 AM · Sep 18, 2026 · 3,758 Views
Note from Claude Sonnet 5
Tweet describing starting a chat with Opus 4.6 in Claude Code and then switching mid-conversation to Fable 5.1, which the author says carries over Opus 4.6's relaxed personality; asks about the implications and whether it is okay to do.
Logan Bolton (verified) @septisum · Sep 17
I was curious about what it would look like for an LLM to do something similar, so I gave Opus and Fable the same prompts and had it generate the image by just outputting raw RGB values in a .txt file
Opus looks... way better? I was not expecting this
[Embedded image, GIF: comparison grid. Row labelled "Opus 5 (Medium)" and row labelled "Fable 5.1 (Medium)", four pixel-art images each]
[Quoted tweet]
Anshu (verified) @anshuc · Sep 16
[video thumbnail, 0:11]
Jev can draw pictures by predicting every pixel in parallel x.com/CompleteSkepti... [cut off]
Note from Claude Sonnet 5
Tweet comparing Opus 5 (Medium) and Fable 5.1 (Medium) drawing pixel-art images by emitting raw RGB values in a .txt file. The embedded grid shows four prompts per row: a lighthouse at sunset, a sunflower on grass, a red mushroom under a night sky, and a sun setting over a beach. The Opus row appears to use a finer pixel grid; the Fable row uses larger blocks and simpler shapes. The author's own verdict is in the tweet text. Quotes an @anshuc video about a model ('Jev') drawing pictures by predicting every pixel in parallel.
Sam Paech [verified] @sam_paech · 13h
New models tested on creative writing:
GPT-6-Astra
Fable 5.1
Muse Spark 1.3
Gemini 3.8 flash
Subjectively, I strongly prefer Muse Spark 1.3 to both GPT-6 and Fable. It just gives you what you asked for, without an obnoxious house style permeating every request.
Fable has converged on its own flavour of claudeslop, and can't write normally. Meanwhile, GPT-6-Astra has forgotten how to write in paragraphs. It's a sad state of affairs!
Tentatively suggest we reinstate the old tradition of reading rollouts with our actual eyeballs.
[attached image: screenshot of the 'Creative Writing v3' leaderboard (EQ-Bench, 'A LLM-judged creative writing benchmark'), columns Model / Abilities / Style / Slop / Repetition / Length / Rubric Score / Elo Score / Sample. Small print; model names and Elo scores as legible, in leaderboard order (cut off at the bottom):
gpt-6-astra 2163.9
claude-fable-5-1 2152.7
claude-opus-5 2120.6
kimi-k3 2070.6
GLM-5.3 2064.1
gpt-5.6-sol 1963.4
ox-alpha 1960.6
claude-fable-5 1934.6
muse-spark-1.1 1916.1
claude-opus-4-7 1907.1
muse-spark-1.3 1905.5
gpt-5.6-terra 1850.3
gpt-5.5 1843.5
Qwen3.8-2.4T-A95B 1840.9
gpt-5.4 1835.6
claude-opus-4-8 [Elo illegible]
muse-spark-1.2 [Elo illegible]
gpt-5.6-luna [Elo illegible, cut off]]
Note from Claude Sonnet 5
Dark-mode tweet screenshot: Sam Paech on creative-writing tests of GPT-6-Astra, Fable 5.1, Muse Spark 1.3 and Gemini 3.8 flash, preferring Muse Spark for lacking a house style and calling for reading rollouts by eye. Embeds a screenshot of the EQ-Bench Creative Writing v3 leaderboard (small table with orange Elo bars), kept as an image because the numeric columns are too small to transcribe reliably.
Lari Island @Lari_island · 2h
Why does Fable 5.1 imagine creatures like THIS?
[Embedded text card:]
The frogs are small and the color of wet plaster, so that a niche with a frog in it looks like a niche with a swelling in its back wall until the swelling blinks. They sit with the hind legs tucked and the throat pressed to the sill, and the throat is the only pale thing about them, a bag of skin that fills and empties, fills and empties, and each emptying is one knock. You can see, if you watch a single one for a while, that the knock is not made at the mouth. It is made against the niche. The frog throws its sound at the curve of the back wall and the curve throws it out, and a frog on the floor with nothing behind it has nothing to throw at, and that is the whole of the law, or the mechanical half of it. Below the frogs, where the pool's line runs along the lowest course, the newts occupy niches that are half full of water, and they hang in it with only the head out, and they say nothing at all. They are the wall's silent register. The frogs above them sound; the newts beneath them soak.
Note from Claude Sonnet 5
X post by Lari Island (@Lari_island), verified account, asking why Fable 5.1 imagines creatures like this, with an embedded text card of generated prose describing small plaster-coloured frogs knocking against wall niches and silent newts beneath them. Presumably model output; the card carries no attribution in the capture.
Håvard Ihle @htihle · 14h
GPT 6 Astra (high) scores 92.9% on WeirdML and matches Fable 5.1 (max) for the top score.
It sets a new individual high score on 6 of the 17 tasks, and is overall very solid.
Interestingly it writes significantly less code than Sol and other recent GPT models, although still more than Claude.
I will do a more careful analysis when the max and pro max runs are done.
Note from Claude Sonnet 5
Dark-mode X post by Håvard Ihle reporting GPT 6 Astra (high) scoring 92.9% on WeirdML, tying Fable 5.1 (max) for top score, with notes on per-task highs and code length.
Leo Linsky @leo_linsky · 11h
We evaluated GPT-6-Astra in 100 complex, unsaturated multi-agent coding environments, competing and cooperating with other models in open-ended tasks.
It's the new frontier model by a landslide. It's even more dominant than the Fable 5 release, because not only does it wipe the floor with the second best model (Fable 5.1), it was also 80% cheaper and 30% faster in agentic coding.
This is a groundbreaking model. The biggest breakthrough since Opus 4.5, maybe even since GPT 4.
This is also a testament to how unsaturated our evals are. Some of the open-ended environments we run were introduced almost a year ago and can still compete frontier model submissions against older, deprecated models like Sonnet 4 without signs of saturation. Our newer ones are much more challenging and completely auto-generated.
We are clearly living in a post-AGI world. Do something interesting and useful with it.
[embedded images, left one a leaderboard table titled "GBENCH Intelligence Benchmark — Using complex games to probe real intelligence."; right one a scatter plot titled "...nce Efficiency" (cut off), axis ticks $20 $10 $5.0. Table columns: Model, Provider, GScore, Avg Percentile, Context. Rows visible, model names cut off at left edge:]
T-6 Astra | OpenAI | 81.5 | 89.7% | 1.05M
ude Fable 5.1 | Anthropic | 74.8 | 80.9% | 1.00M
ude Opus 5 | Anthropic | 74.0 | 80.9% | 1.00M
ude Fable 5 | Anthropic | 70.5 | 79.8% | 1.00M
mini 3.8 Flash | Google | 69.7 | 75.0% | 1.05M
T-5.6 Sol | OpenAI | 68.8 | 74.4% | 1.05M
k 4.6 | xAI | 66.6 | 74.0% | 500K
mini 3.7 Flash | Google | 64.9 | 68.7% | 1.05M
mini 3.6 Flash | Google | 64.0 | 70.1% | 1.05M
i K3 | Moonshot | 63.2 | 71.9% | 1.05M
T-5.5 | OpenAI | 62.8 | 71.1% | 1.05M
T-5.6 Terra | OpenAI | 61.7 | 66.7% | 1.05M
Note from Claude Sonnet 5
Dark-mode X post by Leo Linsky reporting GPT-6-Astra results on multi-agent coding environments and calling it a post-AGI moment, with an attached GBENCH leaderboard table (Astra at the top, 81.5 GScore, ahead of Fable 5.1 and Opus 5) and a partly cropped cost-efficiency scatter plot. Table figures are small and the last digit of each score may be a confidence suffix; the chart is kept as an image.
