AI_卡拉迪亚

Ok,it's fine.

[AI_卡拉迪亚 @new2taste]

@szu_jason @AlekssRG @memecoinassasin @MeteoraAG 玩这么大，我也在做，基本没赚

[AI_卡拉迪亚 @new2taste]

惨遭骑脸输出！当Sonnet 5.5都 比 Sol 更好且更具性价比，我不知道 OpenAI 还能如何应对……

[Artificial Analysis @ArtificialAnlys]

Anthropic has launched Claude Sonnet 5.5: it scores 56 on the Artificial Analysis Intelligence Index, just 2 points behind Opus 5.5 (max), but at the highest Output Tokens per Task we’ve seen

With max effort, Sonnet 5.5 gains 18 points over Sonnet 5 and to #2 on the Intelligence Index behind only Opus 5.5 (max). Anthropic has priced Sonnet 5.5 identically to Sonnet 5 at $0.2/$2/$10 per 1M cache input/input/output tokens, however it outputs a higher number of Output Tokens per Task and costs $7.60 per task (~50% higher than Sonnet 5’s Cost per Task)

Key takeaways:

➤ Meets leading models on agentic terminal use and knowledge work: in Terminal-Bench 4.0, Claude Sonnet 5.5 reaches 64% against 60% for Opus 5.5 and GPT-6 Astra. On AA-Briefcase (1811 vs 1822 Elo), GDPval-AA (1844 vs 1846 Elo), and AutomationBench-AA (71% vs 70% headline score), Sonnet 5.5 reaches parity with Opus 5.5, albeit with significantly higher token usage to achieve it

➤ Heaviest token use we have measured: at max effort, where it reaches performance nearing that of Opus 5.5, Claude Sonnet 5.5 used ~193k Output Tokens per Intelligence Index Task. This is the highest token use we have measured on around 60% higher than Opus 5.5 (max) or Sonnet 5 (max) and ~7x GPT-6 Astra (max)

➤ Pricing remains at $2/$10 per million tokens of input/output, matching GPT-6 Sol. At this pricing Claude Sonnet 5.5 sits off the Intelligence vs. Cost per Task Pareto Frontier. At high effort levels it sits behind Opus 5.5, while lower efforts have GPT-6 Astra or Sol configurations delivering equivalent performance for lower cost. The high effort setting is the most competitive on this basis, sitting very narrowly behind GPT-6 Sol on Intelligence at effectively the same Cost per Task

➤ Behind Opus 5.5 on factual knowledge and scientific reasoning: as a smaller class model, Sonnet 5.5 still lags on factual knowledge in AA-Omniscience compared to Opus 5.5. It scores 54% against 66% for factual accuracy, though with a lower hallucination rate (47% against 59%). It also sits ~6 points lower on Humanity's Last Exam and SciCode compared to Opus

These evaluations were conducted on a pre-release deployment of Claude Sonnet 5.5, which Anthropic found to have a bug that can degrade responses to requests that use structured outputs. This is fixed for the public release and Anthropic expects minimal change or slightly understated performance, but we will be re-running relevant evaluations soon.

Other model details:

➤ Context window: 1 million tokens with image and text input, unchanged from Sonnet 5

➤ Pricing: unchanged from Sonnet 5’s latest $2/$10 per 1M input/output tokens; cache writes at $2.5, cache reads $0.2

➤ Effort settings: five (low, medium, high, xhigh, max). Intelligence Index evaluations were run at all five with Anthropic's default fallback enabled. We see Sonnet 5.5 fall back in ~0.1% of tasks across the Intelligence Index, primarily in TerminalBench 4.0, falling back to Sonnet 5 in all cases.

[AI_卡拉迪亚 @new2taste]

@agntmarkets Waiting

[AI_卡拉迪亚 @new2taste]

草泥马，开票不处理催一催直接把我移除DC，100U而已，你妈死了缺丧葬费是嘛

[Blokyz @OriginalBlokyz]

The OG Blokyz for the top 100 raffle participants are ready to claim

How to claim?

1. Create an account on https://t.co/Sh1E7lJQJq using the same wallet you entered the raffle with by 30th Sep 5 PM UTC

2. Your OG Bloky phygital will get airdropped to you on 1st October

3. Any unclaimed OG blokyz will go back to the team treasury and will be used for future community incentives

These Blokyz will be digital tokens that you can redeem 1:1 for the physical OG Bloky collectible once redemptions open on @dyli_io . Only 100 of these will ever exist.

To check whether your wallet is in top 100 on the leaderboard, visit https://t.co/tJRMt0Jr4i

[AI_卡拉迪亚 @new2taste]

@zhouluobo 钓鱼吧

[AI_卡拉迪亚 @new2taste]

@fan128168 @RobinhoodApp 为啥我的LP一直在稳定亏钱😅

[AI_卡拉迪亚 @new2taste]

@LinearUncle 能力感觉如何

[AI_卡拉迪亚 @new2taste]

@Cryptobuyer2024 进场了吗派老师

[AI_卡拉迪亚 @new2taste]

@fan128168 @RobinhoodApp 有实力啊多总

[AI_卡拉迪亚 @new2taste]

非常惊艳

[Artificial Analysis @ArtificialAnlys]

GLM-5.3-Flash scores 57 on the Artificial Analysis Intelligence Index. At $0.09 Cost per Task, it sits comfortably on the Intelligence vs. Cost per Task Pareto frontier

@Zai_org has released GLM-5.3-Flash, a smaller and cheaper sibling to GLM-5.3 at 320B total parameters and just 18B active parameters. GLM-5.3-Flash supports low/high/max reasoning efforts, and scores 57 evaluation on the Artificial Analysis Intelligence Index with max reasoning effort. This places the model only 3 points behind GLM-5.3 at 60 and in line with GPT-5.6 Terra and Muse Spark 1.2.

On Z AI's first-party API, GLM-5.3-Flash is priced at $0.15 / 1M input tokens and $0.50 / 1M output tokens, just over 10% of the price of GLM-5.3. Cached input tokens are priced at $0.026 / 1M tokens, an 80% discount. Its Cost per Task on the Intelligence Index is $0.09, compared to $0.68 for GLM-5.3 (max), and it sits on the Pareto frontier for Intelligence vs. Cost per Task.

Key results:

➤ GLM-5.3-Flash is 3 points behind GLM-5.3 (max) on the Artificial Analysis Intelligence Index, at ~7.5x lower Cost per Task. At $0.09 per Intelligence Index task against $0.68 for GLM-5.3, it sits on the Pareto frontier for Intelligence vs. Cost per Task. It ties GPT-5.6 Terra ($0.51) and Muse Spark 1.2 ($0.40) at 57 while costing ~5.7x and ~4.4x less per task.

➤ GLM-5.3-Flash is less token efficient, but its low per-token pricing means this does not translate into a high Cost per Task. The model used 149M output tokens to run the Intelligence Index, ~11% fewer than GLM-5.3 at 168M, but more than Kimi K3 (133M) and Qwen3.8 2.4T A95B (136M) which score the same on the Intelligence Index. Reasoning tokens account for 134M of the 149M total (~90%).

➤ GLM-5.3-Flash matches GLM-5.3 on real-world agentic work on GDPval-AA v2. With an Elo of 1770, the model is tied within the margin of error for GLM-5.3 and Grok 4.6. This places it behind only Claude Opus 5 (xhigh and max). On Terminal-Bench v2.1 it also matches GLM-5.3 (84.3% vs 83.9%), and on τ³-Banking it trails by 3.1 p.p. at 47.2%.

➤ GLM-5.3-Flash demonstrates good real-world knowledge and hallucination rate, scoring +7 on AA-Omniscience. Its AA-Omniscience Accuracy is 28%, 6 p.p. below GLM-5.3 (max) at 34% and well below GPT-5.6 Terra at 47%. However, with a Hallucination Rate of 28%, it is an improvement over GLM-5.3 at 30%. In real-world knowledge, GLM-5.3-Flash knows less than the bigger models and frontier proprietary models in its Intelligence Index tier with an accuracy of 28%.

Additional model details:

➤ Pricing: On Z AI's first-party API, $0.15 / 1M input tokens and $0.50 / 1M output tokens . Cached input tokens are priced at $0.03/ 1M tokens, an 80% discount.

➤ Accessibility: Accessible through Z AI's first-party API at launch.

➤ Size: 320B total parameters with 18B active parameters

➤ License: MIT

➤ Context Window: 400k

[AI_卡拉迪亚 @new2taste]

幸运的是，在定价上它又给了我们惊喜，斩杀线喜迎新王，flash模型能有这么多高性价比的选择对于高频AI使用者来说实属福音，而这些在上半年的时候都难以想象

[AI_卡拉迪亚 @new2taste]

谜题揭晓
OX Alpha就是GLM-5.3-Flash
GLM-5系列第一个原生多模态模型
支持图像、视频和文本输入，原生上下文100万
近期通过open router高强度使用OX Alpha，最初仅仅是grok4.6额度不足的临时救场，最后却越用越顺手，以至于我开始担心，免费期结束后会不会很贵，至于它是谁家的，已经不重要了

[AI_卡拉迪亚 @new2taste]

谜题揭晓
OX Alpha就是GLM-5.3-Flash
GLM-5系列第一个原生多模态模型
支持图像、视频和文本输入，原生上下文100万
近期通过open router高强度使用OX Alpha，最初仅仅是grok4.6额度不足的临时救场，最后却越用越顺手，以至于我开始担心，免费期结束后会不会很贵，至于它是谁家的，已经不重要了

[AI_卡拉迪亚 @new2taste]

令人振奋，昨天刚通过openrouter把ox alpha接入Hermes

[Nous Research @NousResearch]

Ox Alpha is free via Nous Portal for a limited time. We have capacity for 1 quadrillion tokens per day.

Let the tokens flow at https://t.co/4KFwhsReyA

[AI_卡拉迪亚 @new2taste]

现在的codex plus别说coding了，跑办公任务都扛不住，几个小任务就用掉20%，想想两个月前的使用体验，云泥之别

[AI_卡拉迪亚 @new2taste]

@Vincent_AINotes 蹲个结果

[AI_卡拉迪亚 @new2taste]

于是连夜工作....

[AI_卡拉迪亚 @new2taste]

十分钟后已经蹬上了🫡🫡🫡

[Elon Musk @elonmusk]

Grok 4.6 is now out 🚀🚀🚀

Smart, fast & amazing bang for buck!

[AI_卡拉迪亚 @new2taste]

一大早起来就收到重置，昨天刚好清空，又被我掏到了🤣

[Tibo @thsottiaux]

That's right, GPT-5.6 Sol is awesome and can be used pretty much anywhere, including in the CC harness.

To celebrate this, together with the fact that I'm not going anywhere... I have reset usage limits for all paid users of ChatGPT Work and Codex.

Have fun out there!

[AI_卡拉迪亚 @new2taste]

不知道是5.6sol长脑子了还是opus5降智了，原来把opus5当小fable5，空降接替fable5主持coding工作，sol只能当个副手，不成想现在做规划和review老被sol教做事，倒反天罡了🤣

[AI_卡拉迪亚 @new2taste]

@ollama @opencode 什么时候接入cursor呢