[KongchangAI]
Modelclaude-fable-5-1 / Fable 5.1

Claude Fable 5.1

A large language model in Anthropic's Fable series, offering an extended context window of one million tokens. It is designed for programming assistant scenarios such as Claude Code.

Core Facts

Timeline (last 90 days)

Oct 25

In the Monitoring stage, the official statement mentioned "a fix has been deployed" rather than "a workaround has been applied"; the former usually means the fix has entered the production environment and taken effect, while the latter is more often seen with temporary workaround solutions.

Unverified50%
Oct 25

In the Identified stage, the official wording was "elevated errors on requests to Claude Fable 5.1," and no specific error codes, scope of impact, or proportion of affected users were given.

Unverified50%
Oct 25

Investigating and Identified appearing in the same minute indicates that the monitoring alert and the root-cause localization were completed almost simultaneously, which often suggests the problem is close to the surface configuration or deployment level rather than a complex underlying logic defect.

Unverified50%
Oct 25

For teams building applications with Claude Fable 5.1, logs can be traced back based on the official UTC 08:08 to 08:29 window to confirm whether failed requests are concentrated in that interval.

Unverified50%
Oct 25

From the deployment of the fix (08:30 UTC Monitoring) to the confirmation of resolution (08:49 UTC Resolved), 19 minutes elapsed, and this monitoring window length is consistent with a conventional recovery observation period.

Unverified50%
Oct 25

Claude Fable 5.1 experienced an elevated request error rate on October 25, and the impact window recorded on the official status page was 01:08 to 01:29 Pacific Time (08:08 to 08:29 UTC), roughly 21 minutes in total.

Unverified50%
Oct 24

In Val's finance agent evaluation, Argon scored 65.4% and Claude Fable 5.1 scored 58.9%.

Unverified50%
Oct 24

At 05:16 UTC the official first public notice was given with the status Investigating, and at 05:24 UTC the status was upgraded to Identified.

Unverified50%
Oct 24

Officially, the fix was deployed at 05:41 UTC and the service entered the monitoring phase, and at 05:53 UTC it was formally marked as resolved.

Unverified50%
Oct 24

This incident did not disclose the specific distribution of error codes or the proportion of affected requests, nor did it state whether it was related to a particular region or calling method.

Unverified50%

40 more timeline events

All Facts (20)

Verified

Artificial Analysis gave Argon an intelligence index score of 53, on par with GPT-6 Astra.

80%
Verified

Opus 5.5 scored 66 on the Terminal agent benchmark, higher than Sonnet's 55 and Astra's 57.9.

70%
Verified

The competitive focus of today's top AI models has shifted from 'can it do it' to 'how elegantly and how well it fits the scenario' — i.e., presentation quality and scenario adaptability rather than pure intelligence level.

65%
Partially Verified

The core difference between Fable 5.1 and Mythos 5.1 lies in the depth of safety measures, not in the underlying capability level.

75%
Partially Verified

Calculated per thousand repair tasks, Claude Haiku 5.5 costs about $0.70, DeepSeek V4.1 Flash 1017 about $1.35, and MiniMax M3.1 Flash about $4.45 by default and about $0.64 at the low setting.

65%
Partially Verified

In the legal work test, Argon scored 19.6% and Fable scored 6.7%, giving Argon nearly a threefold advantage.

65%
Partially Verified

Gemini 4 Argon reached 51.3% in the workflow automation test, while the second place had only 42.5%.

65%
Unverified

In an AI travel website landing page frontend design test, the reviewer judged GPT-6 Astra to outperform Fable 5.1.

60%
Unverified

The name "Claude Fable 5.1" may be the community's designation for a certain Claude variant or internal version.

60%
Unverified

In actual testing, GPT-6 Astra generated a first-person shooter game prototype featuring container bunkers, fully automatic rifles, and recoil and ballistic determination within half an hour, while the control group Claude Fable 5.1 took close to 5 hours.

60%
Unverified

In the Terminal Bench 4.0 highest-accuracy test, Fable 5.1 achieved an accuracy of 55.8% at a cost of $19.50, while Astra achieved an accuracy of 56.7% at a cost of $10.35.

60%
Unverified

In its product description, Anthropic states that Fable 5.1's research capabilities offer an early glimpse into how AI models can contribute to scientific progress.

60%
Unverified

For teams building applications with Claude Fable 5.1, logs can be traced back based on the official UTC 08:08 to 08:29 window to confirm whether failed requests are concentrated in that interval.

50%
Unverified

Investigating and Identified appearing in the same minute indicates that the monitoring alert and the root-cause localization were completed almost simultaneously, which often suggests the problem is close to the surface configuration or deployment level rather than a complex underlying logic defect.

50%
Unverified

In the Identified stage, the official wording was "elevated errors on requests to Claude Fable 5.1," and no specific error codes, scope of impact, or proportion of affected users were given.

50%
Unverified

In the Monitoring stage, the official statement mentioned "a fix has been deployed" rather than "a workaround has been applied"; the former usually means the fix has entered the production environment and taken effect, while the latter is more often seen with temporary workaround solutions.

50%
Unverified

From the deployment of the fix (08:30 UTC Monitoring) to the confirmation of resolution (08:49 UTC Resolved), 19 minutes elapsed, and this monitoring window length is consistent with a conventional recovery observation period.

50%
Unverified

In Val's finance agent evaluation, Argon scored 65.4% and Claude Fable 5.1 scored 58.9%.

50%
Unverified

The incident began at 22:00 Pacific Time on October 23 (05:00 UTC on October 24) and ended at 22:35 PT / 05:35 UTC, lasting about 35 minutes in total.

50%
Unverified

This incident did not disclose the specific distribution of error codes or the proportion of affected requests, nor did it state whether it was related to a particular region or calling method.

50%

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