Claude · Last 7 days
Claude Sonnet 5.5
0–100 · higher means more positive community experience, not benchmark performance.
Updated 2026-10-02 13:57 UTC · 2026-09-25 – 2026-10-02 UTCRECENT 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.
Daily readings & sample sizes
| Date | Score | n | Scoring window (UTC) |
|---|---|---|---|
| 2026-10-02 | 66.2 | 122 | 2026-09-25T13:57:04+00:00 – 2026-10-02T13:57:04+00:00 |
| 2026-10-01 | 65.7 | 120 | 2026-09-24T23:57:02+00:00 – 2026-10-01T23:57:02+00:00 |
| 2026-09-30 | 65.8 | 107 | 2026-09-23T23:57:03+00:00 – 2026-09-30T23:57:03+00:00 |
| 2026-09-29 | 65.7 | 89 | 2026-09-22T23:57:03+00:00 – 2026-09-29T23:57:03+00:00 |
ONE MODEL, DIFFERENT COMMUNITIES
Across the communities
Scored separately for each platform, with different samples and audiences. Platform scores are not simply averaged; scored counts exclude quota-only comments.
Scores range from 0 to 100. Select a platform for its trend and reviews. Latest opinion time is not crawler health.
What people think
One user added a mini router that dispatches harder coding slices to Sonnet 5.5, and the setup improved first-pass implementation quality. One user, after briefly reviewing Claude Sonnet 5.5's Thomson problem proof, sarcastically said anyone who finds inspiration from it should just become a computer.
About the sample and scores
As of 2026-10-02 13:57:04 UTC, Claude Sonnet 5.5 in the Claude family has 122 explicitly attributed community comments in the last 7 days; its community score is 66.2/100. Available category scores include General text 66.2/100 (n=59); Coding 76.4/100 (n=9); Reasoning 66.2/100 (n=26). 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.
0 selected excerpts
Community reports about limits cannot establish official allowances.
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能在这里面产生灵感的可以当计算机去了
Comment context
Some responses claim this is Anthropic's excessive promotion, but I feel I need to stand up for Anthropic. The reason is simple—this is not Anthropic's release at all... The original message was from a company doing various LLM benchmarks called Vals.AI. Here is the original text: We asked ten Claude Sonnet 5.5 agents to use Lean to prove the lowest-energy arrangement of seven electrons on a sphere (the Thomson problem, with N=7). Within 15 hours, they produced a 17,895-line proof, accepted by the Lean kernel, showing that the answer is a pentagonal bipyramid.
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有些回答说这是anthropic的过度宣传，我觉得我得为anthropic站一下台。理由很简单，这根本不是anthropic发的…… 原消息是一家做各种LLM Benchmark的公司Vals.AI发的，这是原文： We asked ten Claude Sonnet 5.5 agents to use Lean to prove the lowest-energy arrangement of seven electrons on a sphere (the Thomson problem, with N=7). Within 15 hours, they produced a 17,895-line proof, accepted by the Lean kernel, showing that the answer is a pentagonal bipyramid.
Comment context
I learned through my testing that sonnet 5.5 sometimes spawns opus 5.5 subagents. Might be one of the main reasons why sonnet 5.5 feels a lot more expensive for people.
And, this is not about the API price, but the percentage weekly usage of the subscription.
Comment context
I actually added a mini router in front of it this week, and the harder slices get dispatched to sonnet 5.5 instead, pretty effective so far, it improved first-pass implementation quality.
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这玩意儿哪里都不如Opus 5.5，也是给A畜学到国产小参数做题家模型雷霆大思考刷分的精髓了
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So I don't know what's your process but I would suggest models wise to just use Opus 5.5 high or medium as the orchestrator to supervise a Sonnet 5.5 high implementer and use 6.1 sol xhigh (or medium if you notice xhigh is not sustainable for you) as a reviewer.
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It really depends on the task, but it's often either sonnet 5.5 high or opus 5.5 med.
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丫抠字儿太快了 找她吹牛逼容易跟不上思路
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而且很快
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说人话的，废话也少了
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依靠无限制拉长思考来换取所谓的更高智商，感觉可能不是正确方向
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sonnet5.5审美确实一眼高级
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追问，发现有幻觉
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I discussed a JEV-related question and found that Company A's models now have a certain characteristic.
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我拿一个跟JEV相关的问题讨论了一下，发现A家的模型现在有个特点。
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在智能体命令行编程测试 Terminal-Bench 4.0 中，它以 70.6% 的高分拿下 全球第一 ，相比 Sonnet 5 的 10.3% 属于跨越式暴涨
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A社的账号"娇贵"啊，隔三岔五给你大陆ip全干挺了
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才发现Sonnet5.5还挺耐用啊
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思考用更多token导致更慢
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免费用户也能用几下sonnet5.5，反观隔壁免费用户只能用luna
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同思考档位的sonnet 5.5 比 opus 消耗token更多，效率更低
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单价只有Opus一半，额度还给得更多，还学会了O社时不时发重置次数
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I've never once hit a safeguard refusal. Dunno what you guys are doing
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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.
Comment context
Tokens | cost to serve/run the same agentic task | performance across multiple benchmarks
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