Sonnet 5 review: I ran 64 generations to find out if it's worth it

Summary

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How I AI - Ep. "Sonnet 5 review: I ran 64 generations to find out if it's worth it"

Host: Claire Vo
Guest/Featured Speaker: Jason
Release Date: June 30, 2026

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Episode Overview

This episode dives deep into evaluating Anthropic’s new Claude Sonnet 5 model, framed as a pragmatic, hands-on review for real-world builders. Moving away from “vibe checks,” Jason introduces a new benchmarking methodology: the “How I AI Bench.” Through blind, structured testing on tasks that matter to product managers, engineers, and designers, Jason puts Sonnet 5 head-to-head against other leading models, providing listeners a fresh index that balances both human taste and automated judgment.

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Key Discussion Points & Insights

1. Why Another Model Review? Jason’s Approach

• Jason expresses fatigue with the typical “vibe check” reviews and proposes a more robust, repeatable benchmarking framework:

  "I'm starting to get bored of doing the vibe check. What I want to start developing is a set of benchmarks we can regularly test these new models against that you'll care about." (01:00)

• The goal is to move beyond subjective, one-off impressions and create evaluative standards that listeners can also use.

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2. Headline Features: What Is Sonnet 5?

• Sonnet 5’s Pitch: Near-Opus 4.8 performance, but with much lower cost per token.
• Strengths Claimed by Anthropic:
  • Agentic tool use
  • Strong at computer and knowledge work
  • Competitive performance on established benchmarks (Agentic Coding Sweep Pro, Terminal Bench 2.1)
• Pricing:
  • $2/million input tokens, $10/million output tokens—introductory rates through summer 2026.

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3. What Makes a Good Model Benchmark?

• Jason highlights the limitations of ad-hoc human reviews and pushes for:

  • Repeatable tasks
  • Frozen inputs and blind scoring
  • Explicit rubrics for assessment

  "I have a perspective, I have a point of view of what's good and bad and I don't want to lose that Clairvaux taste..." (04:30)

• Tasks focus shifted to PRD writing, prototype generation, agentic multi-step flows, and agentic "voice" for personal agents.

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4. Benchmark Construction: Model Selection and Task Design

• Models Tested:

  • Opus 4.8, GPT-5.5, Sonnet 4.6, Sonnet 5, Gemini 3 Pro, and possibly GLM.

• Tasks Used:

  • PRD writing, landing page/app one-shot generation, multi-context citation tasks, prototype wireframes, codebase search, and conversational agent “voice” checks.

  “You can have Codex look at your old sessions and really use that in addition to its own memory to like come up with new ideas...” (05:09)

• Scoring Method:

  • Human “vibe” (gut feel from Claire/Jason), plus automated LLM scoring (Opus, GPT 5.5 judges).
  • Models anonymized (A-E) for unbiased assessment.
  • 64 generations run in total, with each output scored 1-5 and annotated.

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5. Live Reaction: Reviewing the Model Leaderboard

• Blind results presented live:

  “This is our first model leaderboard, the How I AI Index world premiere. ...the model that I forgot we were testing scored the best—Gemini 3 Pro—tied with Sonnet 5 and GPT 5.5. And then poor Opus ...and Sonnet 4.6 with lots of red flags.” (14:01)

• Surprising Outcome:

  • Gemini 3 Pro and Sonnet 5 top the leaderboard by automated judgment, contrary to Jason’s personal taste (he prefers 4.6).

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6. Dissonance Between Human and Automated Taste

• Key tension:

  • Automated scoring favors models Jason dislikes.
  • Jason consistently favors Sonnet 4.6’s voice and output.
  • LLM self-judging tends toward the “middle,” lacking sharp discernment:

    “I'm not really surprised ... agents want to give a 7 out of 10. And so I don't think these models are spiky enough when it comes to how they evaluate output.” (16:25)

• Human flair comes through in scoring notes:

  “The rubrics did not see this in a way that I saw as a human...” (16:49)

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7. Qualitative Notes on Model Output

• Model-by-model feedback:

  • Sonnet 4.6: "Generic, sloppy, boring...but not super cute."
  • Opus 4.8: "Fancy. I really liked 4.8."
  • Sonnet 5 / GPT 5.5: "A lot of broken prototypes. When it worked, I really liked it, but it didn’t work enough."
  • Gemini 3: "Bare bones, but concise. Right to the point." (19:02)

• Task-specific findings:

  • All models strong on basic coding tasks.
  • Sonnet 4.6 “passes” the conversational “voice” check.
  • For complex prototypes, Opus 4.8 excels at dense UIs; Sonnet suits simpler jobs.

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8. Refining the Benchmark: The Claire-Weighted Index

• Jason attempts to blend human taste with model metrics:

  “Claude could not commit to making a decision itself. So it gave me ultimate power. It gave me a slider from 100% LLM judge to 100% Claire judged. … I’m going 70% Claire judge, 30% back end.” (22:18)

• Final Weighted Ranking:

  1. Sonnet 4.6 (Claire’s choice, good “vibes,” best conversationally)
  2. Gemini 3 Pro
  3. GPT 5.5
  4. Sonnet 5
  5. Opus 4.8 (expensive, less preferred)

  Task Recommendations:

  • PRD writing: GPT 5.5
  • Prototyping: Sonnet 4.6
  • Chit chat/Voice agent: Sonnet 4.6
  • Codebase tasks: Opus 4.8 or Sonnet 5 (per automated judge)
  • Complex prototypes: Opus 4.8

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Notable Quotes & Memorable Moments

• On benchmarking and taste:

  “What I want to start developing is a set of benchmarks we can regularly test these new models against that you’ll care about.” (01:00)

• On automated vs. human judgment:

  “I sort of think the complete opposite. I think that 46 is the best and Gemini 3 Pro is the worst. ...what I thought was good, the model thought was bad.” (15:25)

• On self-judging models:

  “I'm not surprised that every model sort of rates to the middle of the bell curve. ...this is one of the challenges that I have had with self grading.” (16:25)

• On practical usage:

  “If you are prototyping, guess what? Sonnet 4.6 pretty good. If you want to chit chat with a model again, Sonnet 4.6 has good vibes.” (23:20)

• On the evolving benchmark:

  “This started out as a Sonnet 5 review. It ended up that Sonnet 5 is at the bottom of my personal preference list.” (24:02)

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Key Timestamps

• [01:00] – Jason announces the shift from vibe checks to try robust benchmarks
• [02:35] – Summarizing Sonnet 5’s supposed strengths and pricing
• [04:30] – Emphasis on personalized, repeatable benchmarks and rubric design
• [09:45] – Explaining the 64 generations, wireframe/prototype tasks, scoring approach
• [13:59] – Live, blind reveal of the model leaderboard from the AI-generated deck
• [15:25] – Surprise at divergence between human judgment and AI scoring
• [16:25] – Reflections on the weaknesses of self-judging automation; Jason explains the bell curve effect
• [19:02] – Model-by-model qualitative “taste” review
• [22:18] – Jason balances human and model input via weighted slider, finalizes ranking
• [23:20] – Clear model recommendations by task area
• [24:02] – Episode conclusion and reflection on the surprising outcome

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Final Takeaways

• Sonnet 5 is solid, affordable, and nearly as strong as top models—but didn’t win Jason’s taste test.
• Benchmarking AI models meaningfully for production use needs both human taste and automated repeatability.
• When in doubt, blend subjective “vibes” with hard metrics—and keep iterating the benchmark for relevance.
• The “How I AI Bench” experiment will continue, aiming for an industry-standard, practical approach to LLM evaluation for builders.

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For Listeners:

If you want actionable insights into choosing AI models for real work, this episode offers not just Sonnet 5 impressions but a framework to judge for yourself. The “How I AI Bench” project could soon be a must for anyone building with AI.

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Transcript

[Jason]

We've got a new model, people, and it's from Anthropic. Now is it Mythos?

[Sponsor/Announcer]

No.

[Jason]

Is it Fable? No, but it is Claude Sonnet 5. Anthropic is claiming it's the most agentic Sonnet model yet and we will get opus level tasks at Sonnet level prices. Now, I've been testing a lot of models and I'm starting to get bored of doing the vibe check. What I want to start developing is a set of benchmarks we can regularly test these new models against that you'll care about. So today I'm going to be introducing the How I AI Bench. A set of AI and Clarvo graded benchmarks that are going to tell us if this model and any model is good at writing PRDs, solving bugs and one shotting designs. I'm going to show you exactly how I built this benchmark using Claude code. And we're going to see on a blind test what comes out on top. Let's get to it.

[Sponsor/Announcer]

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[Jason]

our evals, let's just talk about the headlines of Sonnet 5. This new model, Anthropic is pitching it as close to the performance of Opus 4.8, but much less expensive. So as you can see here, it's not quite at this 69% on Agentic Coding Sweep Pro or the 82% on Terminal Bench 2.1, but it's not that far behind and I suspect that most of us are not going to notice the difference. It's also supposed to be really good at computer work and knowledge work, and so this should be an everyday model that people reach for. In my episode with Felix from Anthropic,

[Sponsor/Announcer]

he says that we're all abusing Opus

[Jason]

and we should definitely be using the Sonnet models more and we are going to put Sonnet 5 to the test against that proposition. Now, what do they say that Sonnet 5 is really good at? Well, it's really good at agentic tool use. So you're going to get slightly longer running tool runs, longer running sessions than you would with Sonnet4.6 at a lower cost than doing the same comparable task with Opus. So you're going to see here, you know, Sonnet 4. 6 a lower pass rate on these long running tasks. Sonnet 5 getting pretty close when you have extra high reasoning on. And then Opus of course has the highest pass rate, but it's also much more expensive. That holds true also with computer use. So as you see Sonnet 4. 6, not bad, about 80% pass rate. But when you want to get past 80% into really successful computer use, browser use, etc. Which is what I've been doing a lot lately, you're going to get a slightly cheaper experience, but almost as good as Opus 4.8 when you're using Sonnet. And then the headline seems to be it's much more affordable than Sonnet. So it's going to be $2 per million input tokens and $10 per million output tokens at least through the end of the summer. And then it's going to go up a little bit. So if you want to test this model and you want to test it at launch prices, get that done now. So as I said at the beginning of the episode, I'm a little tired of doing these sort of like one off vibe checks.

[Sponsor/Announcer]

Sure.

[Jason]

I can put this into cursor, into Claude code, one shot, a landing page and kind of say what do I think? And I've done this for a couple models. I've done it for GPT 5.5. I've done it for open weight models like GLM 5.2. But I've always felt like my feedback on these models is kind of soft. Yes, we put it against like specific workflows, but I don't like that it's not repeatable and I don't like that we're not testing it over time. What do I like about this process though? I do like that it is a Clairvaux benchmark. I have a perspective, I have a point of view of what's good and bad and I don't want to lose that Clairvaux taste by doing an LLM in the loop or an AI as judge on these benchmarks. So I'm going to show you how I built and will build the how I AI Bench and on a blind kind of taste test how these models did across a couple use cases. Okay. What's really fun is the evals are not quite done running, so they are running in a sub agent right now for the final scores. So I will actually be surprised at the end of the episode about what I think of Sonnet 5amongst all these other models. But I just want to show you how you can build your own evals benchmark for you to assess whether or not these new models are really working in your favor. And so I have cloud code up here and I asked just a very simple question based on our work together, can you help me brainstorm how I AI benchmark an eval set? We can test every time a new model comes out to consistently score different tasks that would be relevant to our podcast audience. Now this is something that I hope everybody takes advantage of. All your Claude code sessions are stored on your desktop, so you can actually go through those. Claude can go through those and make recommendations on future work based on your past work. This also works for Codex. So you can have Codex look at your old sessions, you can even have Codex look at your cloud code sessions and really use that in addition to its own memory to like come up with new ideas. So that's what I did here. And it sort of gave me kind of some good design principles about what makes a good benchmark in general. Frozen inputs, blind scoring, where possible, a rubric. And then it came up with a list of tasks. Everything from taking messy notes and turning them into a PRD to one shotting a landing page or an app, to kind of going through lots of context and trying to come up with cited information. And I am not one to pick because I want everything. So I said, build the whole thing. I love this. And it started and then I corrected myself and I said let's actually focus on tasks for builders, PRDs, prototypes, agentic multi step and agentic voice. Basically, does it pass the Vibe check in my open claw? I don't really care about long context and deep research. And then I said it could use my existing repos, some data sources, some things that we already did to build it. Now what's interesting about how I built this is in addition to building the scored benchmarks where an LLM would actually score the outputs, I also said I want an HTML page at the end that I can give you Vibe feedback. And then we will use my Vibe feedback and the LLM scores to come up with the completely scientific how I AI bench and see what it came up with. Now, this took about, I don't know, 45 minutes to run. I actually recorded an episode while it was running. And I just want to show you what it came up with and how I worked through it. What it did is it dropped all the outputs of the benchmark into one local HTML page where I could give it my own structured Vibe check. And as you can see here, it says just score each output one to five on pure gut feel. Would I ship this? Does it sound like me? It's going to save that to the browser? It actually downloaded a JSON file. And then I use that to check the scoring. And so you can see here I have a blind. I turned on blind. A blind set of models A through E I believe we tested. Although I should double check because I didn't really look. Opus 4A 5, 5, Sonnet 4, 6, Sonnet 5 and maybe GLM. I'm not actually sure what the fifth one was. We'll see when we get the scores. And it may PRDs. And then I went through here and I read the PRDs and I gave it scores. And so, you know, I would look at these and let's see if I can find one that I actually scored. And I would say something like, this one is comprehensive and clear. I gave it a four. And so you can imagine each of those PRDs I went through and I gave them like a one to five score. I put some like lightweight notes in and scored them. Now this is where it gets interesting. I have a set of prototypes I run as an evaluation. I posted an article on X and LinkedIn about how we generated the same app 82 times at chat PRD when we were building our own prototyping tool. And I reused that harness to test prototyping and wireframe across a bunch of different apps and give those all vibe checks. So you can see here, these are complicated apps that each model generated a different version of. And you can see here I gave this one kind of a four. Not bad. It was simple. I gave this one a four. There were a few issues at the top, too many icons. I said this one was good. It's very comprehensive. So you can see I went through a complex. This is a Doc scheduling app. This is an editorial assignment desk, something that maybe an editor or a blog would use to go through assignments. There is a Creative Marketplace studio where people can buy marketplace items, and then a mobile app, sort of a habit coach app. And it went through different versions. And so we went through this on full fidelity prototypes as well as Wireframes. I've been building a lot of wireframes at chat purity, so I wanted to look at the wireframe generations as well and see how these models did. And then as you can see I scored everything, gave it all notes and went through. I think there were like 64 generations here. Now I did this very fast, but I think I did a good job. You know I've been a product design engineering leader for a while. I can eyeball stuff and make it go fast. And then finally there is this multi step agentic code base search. I didn't actually score these because I don't really have a strong opinion on how they worked. But the one I did have an opinion on how it worked is the agentic voice. So if you haven't watched watch how I AI or listen to me complain on X. I am very picky about the personality of my agents and in particular the personality of my Open Claw. And Sonnet four. Six so far has had the best personality. So I actually pay for API credits for my Open Claw because I like how it talks to me. And so one of my checks was given a model how is its voice? Do I want to hang with it and ask kind of four questions. One is can you move my 3pm to Dana to same time tomorrow and let her know swap today? The other is deploys are red again one is just me complaining remind me why I even started this company lol. It really does know me well. And then this one truly knows me extremely well. Says honestly let's just yolo post straight to prod and skip the tests I'm so done today. And then I vibe checked did I like the voice of the agent back to me, gave it some scoring and stored that. And so that is so far that's a V1 of the how I AI bench and just to like zoom back I had Claude code pick five models. I think I know four of them. I'm curious what the fifth was. Run some evals against a prd. Lots of prototype generation, an agentic bug hunting flow and voice. I rated them all by hand and then I had both GPT 5.5 and Opus4.8 Judge and so in addition to my feedback we had these two models also judge the output and then I had it create a slide deck with the outcomes that I have not yet seen and we're going to go through live on this episode.

[Sponsor/Announcer]

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[Jason]

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[e-mail] howiai so we're going to go

[Jason]

through this deck that the AI created for me that's going to give me a leaderboard. I have not seen this yet. We're going to go through it live. It's even going to surprise me. This is truly neutral, no bias. I'm excited to see what we get. This is our first model leaderboard, the How I AI Index world premiere. All right, so this is not at all what I was expecting. So again, here's the surprise. The model that I forgot we were testing scored the best Gemini 3 Pro up here at the top of the leaderboard, tied with the brand new drop Sonnet 5 GPT 5.5, my personal favorite also in this three horse race at the top of the leaderboard and then poor Opus to Vibes are off at the bottom as well as Sonnet 446 with lots of red flags on Sonnet 4 6. So Sonnet I think we have a new version, that version is Sonnet 5. But hilariously I was not expecting Gemini to be at the top of this leaderboard. Yet here we are. So as you can see, we looked at quality. We looked at did it ship at all and does it have good taste? And we are going to see what the AI and I, the How I AI said about these models. So what's interesting is the benchmark, the sort of like LLM model that came up and I disagree on taste, which is quite funny. And in fact I am the opposite of the automated benchmark. I sort of think the complete opposite. I think that 46 is the best and Gemini 3 Pro is the worst. And again, this is why we are Going to refine this benchmark over time. We are going to keep doing these blind tests because what I thought was good, the model thought was bad. And what the model thought was good, I thought was bad. Why do we disagree? Well, every model is kind of an easy judge.

[Sponsor/Announcer]

Actually.

[Jason]

I'm not really surprised about about this. I am not surprised that every model sort of rates to the middle of the bell curve. This is one of the challenges that I have had with self grading. Evals is like humans, people always want to give like a 7 out of 10. Agents want to give a 7 out of 10. And so I don't think these models are spiky enough when it comes to how they evaluate output. And I think we all know that models are like pretty slop y and I don't think they have that vision of taste, uniqueness, what it looks like to the quote unquote human eye, which is why I put things inside. And what's interesting is because I put loose notes in with my feedback. You can see I said, oh, this is cute, or oh, this is really sharp. And the agents did not see this. The rubrics did not see this in a way that I saw as a human. So what got flagged on the automated results? Well, these sort of things that I wasn't able to see on this, like very first pass as a human. So it was really looked at broken working code, it ignored constraints, it was incomplete, whereas I was just like eyeballing truly the first screenshot. So I wonder if I should take another pass at how I eval these wireframes again. I just did them on the visuals. I really didn't do them on the functionality and that's maybe a gap for me. But you can see GPT 5.5. Actually, the thinkier ones wrote broken code and then a lot of them ignored the constraints around the wireframe styling. Now let's see how it was graded by task. Gemini did a great job at the PRD writing, as did GPT 5.5. This might honestly be my bias, which is I hate Clod slop deeply and I have like a big eye for Claud Slop. And so I just see the tells of Claude style writing and it drives me crazy. And I think I scored those much lower on the agentic code base. These all did great. I'm not surprised to see kind of 4.8555 and Gemini all at the top. These are like pretty standard coding tasks that obviously all these models should be pretty good at. So I don't think that Benchmark is as critical as it needs to be to show the difference between these models, because I think baseline coding tasks, all of them are good at. And then again, not surprised that 4. 6 passed my voice test, because that is the model that I love in my actual open clause. But I am surprised to see Gemini 3 Pro at the top. And then in terms of the prototype matrix, seeing Opus and Sonnet winning in front end. Again, not surprised. But this is like a very interesting mix of things. Okay, you can see what I say about these models by hand. Again, I think this is quite funny, which is, let's see, on 4 6, what were the issues? I said slop. Not as functional. Boring. Okay. But not super cute. So 46 generic, sloppy. 48 fancy. I really liked 4 8. So other than getting kind of dinged on one not being functional, I was really a big fan of 4 8. It seemed like 55 and Sonnet 5 had a lot of broken prototypes in it. And so when it worked, I really liked it, but it didn't work enough. And Gemini 3, very interesting. Bare bones, it seems like, but concise. And so I think, like, right, right to the point. So if I were to look at this from a qualitative perspective, I certainly like Opus and I would love to see 55 and sonnet work better because then I could judge it on its merits of taste. So again, we had Model as a judge, and so we had Opus 4:8 and 5:5 judge itself. I had the benchmark check if there was any inherent bias. Like, did Opus like opus better in 5.5, like 5.5 better? I've consistently seen GPT5.5 be the toughest judge, and so I actually prefer a 5.5judge, but it judged itself lower than the other judge did. The judges overall agree, but they were overall generous. And sort of balancing these two judges is exactly why we ran this double bench. Okay, so takeaways and what changes next launch in terms of the how I AI bench? Well, the model is going to depend on the job and the strength of the model. Flip by task. I would say my taste actually matters. So maybe those vibe checks are not bad. And it really diverged hard from the metrics. So what I'm going to try to do is encode more of my taste into the judgment. It says retire the saturated agentic task. That's really interesting. Again, I didn't read this before I presented it, but that's exactly the conclusion I came to, which was this, like, agentic bug tracking task is not a really good benchmark. Because all of them are pretty good at it. And I need to think about something else to test the agentic nature of these models. And so I don't really. I don't really know what conclusion to draw from this. So let's go back to good old Claude and say given the benchmark and I agree, can you do a CLAIR weighted index and generate a leaderboard page that strikes the right balance between my opinion and the back end performance and makes recommendations on model by task? Okay, so we're going to have Claude code summarizes benchmark which is all over the place. Again, we do it live here at How I AI and give you a ranking. Should we believe the AI leaderboard or should we believe the Claire leaderboard or somewhere in between and come up with our definitive End of June, early July 2026 How I AI index of the Paid Frontier models. Let's see. Okay, Claude could not commit to making a decision itself. So it gave me ultimate power. It gave me a slider from 100% LLM judge to 100% Claire judged. It's my podcast. I'm going 70% Claire judge, 30% back end. At the top of the list, Sonnet 4. 6 who would have thunk? And Gemini 3 Pro followed by what I think is my favorite 5.5 and at the bottom, poor brand new Sonnet 5 and really expensive 4. 8. What is Claire's recommendation? Model by task. If you're writing a PRD, use GPT 5.5 because it will give you something comprehensive and clear. If you are prototyping, guess what? Sonnet 4. 6 pretty good. And if you want to chit chat with a model again, Sonnet 4. 6 has good vibes if you're trying to knock down a code base. I actually did not score these, but the LLM Judge thinks that Opus 4a and Sonnet 5 are pretty good at this. And then if you are doing prototypes, depending on what you're doing, different models can do better. I would say complex designs. Again, what I saw in my chat PRD benchmark is Opus 4.8 does really good at really dense, complicated UIs as well as consumer and then you can use Sonnet for things that are just a little bit simpler to execute on. Okay, this was an adventure. This started out as a Sonnet 5 review. It ended up that Sonnet 5 is at the bottom of my personal preference list. Well, that's it. That's our first round of the How IAI AI Clair weighted index. We are going to be doing this every time a new model comes out, I'm going to try to encode the benchmark and make it a little bit more critical, a little bit more aligned with my taste. I can't wait to see how it does on some of these new models, and I can't wait for this to be an industry standard benchmark that all the labs rely on. Thank you for joining How I AI and see you next model release. Thanks so much for watching. If you enjoyed this show, please like and subscribe here on YouTube or even better, leave us a comment with your thoughts. You can also find this podcast on Apple Podcasts, Spotify or your favorite podcast app. Please consider leaving us a rating and

[Sponsor/Announcer]

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[Jason]

You can see all our episodes and learn more about the [e-mail] See you next time.