llm coordination is just one feature - the core (and why i built amux) was so that i can quickly delegate from my phone, see outputs, monitor, etc without raw ssh.
You can pay 1 cent for a mediocre answer or 2 cents for a great answer.
So a lot of these things are relative.
Now if that equation plays out 20K times a day, well that's one thing, but if it's 'once a day' then the cost basis becomes irrelevant. Like the cost of staplers for the Medical Device company.
Obviously it will matter, but for development ... it's probably worth it to pay $300/mo for the best model, when the second best is $0.
For consumer AI, the math will be different ... and that will be a big deal in the long run.
Right now I'll pay 2x for a subjectively 20+% better coding agent. But in a year I don't think there will be an agent that to me is subjectively 20% better amongst the big three.
So where is the moat for these companies then, in the end will they all be almost the same from the pov of a normal person? So it's just price competition?
> You can pay 1 cent for a mediocre answer or 2 cents for a great answer.
But Gemini is also a great answer (possibly slightly less great or more great).
When consumers cannot easily assess a product's quality, they frequently use price as a primary indicator, equating higher costs with superior quality.
Gemini is the most paradoxical model because it benchmarks great even in private benchmarks done by regular people, Deep Mind is unquestionably full of capable engineers with incredible skill, and personally Gemini has been great for my day job and my coding for fun (not for profit) endeavors. Switching between it and 4.6 in antigravity and I don't see much of a difference, they both do what I ask.
But man, people are really avid about it being an awful model.
I feel like a lot of this is just Googles tooling - if you're using Antigravity/Gemini CLI and then use Claude Code it feels like a huge difference. I can say from experience though (using Cline + OpenCode) that they are really close.
The harness is just much better on the Anthropic side.
I personally found Gemini 3.0 to step on my toes in Agentic coding. I tried it around 10 or so times but it quickly became apparent that it was somehow coming to its own conclusions about what needs to be done instead of following instructions.
Like files I didn't mention being edited and read and stuff of that nature. Sometimes this is cute in fixing typos in docs but when its changing things where it clearly doesn't even understand the intentionality behind something it's annoying.
Gemini 3.1 is clearly much better when trying it today. It stayed focused and found its way around without getting distracted.
I've found in everyday chat use with Gemini that it confuses things _it_ says for things I've said, which is normally fine for my purposes but I imagine would lead to the scenario you're describing in coding sessions.
The only cases where I've had gemini step on my toes like that is when a) I realized my instructions were unclear or missing something b) my assumptions/instructions were flawed about how/why something needed to be done.
Instruction following has improved a lot since a few years ago but let's not pretend these things are perfect mate.
There's a certain capacity of instructions, albiet its quite high, at which point you will find them skipping points and drifting. It doesn't have to be ambiguity in instructions.
So strange. I switched from claude few months ago to gemini3 and didn’t look back. Speed is big one, code quality just vastly better, all while far cheaper. I do need to try latest claude models tho.
All perceptions are very personal and anecdotal. Here's mine: I tried to rebuild a website from Hugo to Astro. Gemini 3.0 was mediocre and in the end just failed and was unable to complete the task. Sonnet did almost well. I had to flush the context once most of the job was finished, for atomic git commits and deployment scripts.
It's so weird. I actually prefer the web version for generic questions like "how would I do X in git" or something, and it'll answer it well. Gemini CLI will immediately try to run git log on the entire graph, grep every single file in the repo, like just answer the question. I actually put in gemini.md to just answer first without running other commands unless explicitly requested and it's been a lot better
There are 4 models, all receiving the exact same prompts a few times a day, required to respond with a specific action.
In the first experiment I used gemini-3-pro-preview, it spent ~$18 on the same task where Opus 4.5 spent ~$4, GPT-5.1 spent ~$4.50, and Grok spent ~$7. Pro was burning through money so fast I switched to gemini-3-flash-preview, and it's still outspending every other model on identical prompts. The new experiment is showing the same pattern.
Most of the cost appears to be reasoning tokens.
The takeaway here is: Gemini spends significantly more on reasoning tokens to produce lower quality answers, while Opus thinks less and delivers better results. The per-token price being lower doesn't matter much when the model needs 4x the tokens to get there.
That sounds great, but if Opus generates 20% better code think of the ramifications of that on a real world project. Already $100/month gets you a programmer (or maybe even 2 or 3) that can do your work for you. Insanity. Do I even care if there is something 80% as good for 50% the cost? My answer: no. That said, if it is every bit as good, and their benchmarks suggest it is (but proof will be in testing it out), then sure, a 50% cost reduction sounds really nice.
It's not half price or cost effective if it can't do the job, that I am happy to pay twice the price for to get done.
But I agree: If they can get there (at one point in the past year I felt they were the best choice for agentic coding), their pricing is very interesting. I am optimistic that it would not require them to go up to Opus pricing.
There's cost, and cost effectiveness. I'd say so far that received negative value for the prompts that I've sent to Gemini 3.
Skill issue, maybe, but I can't get gemini to do any nontrivial tasks reliably, and it's difficult to have it do trivial tasks without getting distracted and making unrelated changes that eat my time and mental energy to think about.
The breakthrough advance of Opus 4.5 over 4.1 wasn't so much an intelligence jump, but a jump in discerning scope and intent behind user queries.
Why do you believe it has to? Uber took 15 years to show a profit. 15 years from 2022 when chatgpt launched is 2037. That's long enough that to say I don't know if I'll even be alive by then.
Homelab and hobby assistant. I have spent $300 for 12 months of tokens. If I'm burning up more than $25 a month then I'd have to pay more or curb use at the end of the year. $25 / month as a new expense is something I can accept for a toy that is letting me accelerate my fun stuff. I can't justify more than that. So I'm left constantly evaluating if my current task is worth more than future tasks and if it is expected to be harder than future tasks. Speculative execution is already one of the harder things I do at work.
> "People underrate Google's cost effectiveness so much. Half price of Opus. HALF."
Google undercutting/subsidizing it's own prices to bite into Anthropic's market share (whilst selling at a loss) doesn't automatically mean Google is effective.
What does that have to do with what I said? Everyone knows that the companies are operating at a loss right now to capture market share in the hope that it's sticky. Google is losing far less money and will not need to get nearly as extreme with how they try to extra money from the product. That honestly makes me feel better about it's long term prospects. And who knows, maybe local llms will prevent it from getting truly bad anyways. Competition tends to keep product quality high.
The pricing page for Claude literally says "More usage" for the $17/month pro plan. Doesn't really quantify anything. The usage is whatever they feel like it should be.
And then the very expensive plan says "Choose 5x or 20x more usage than Pro". It's all arbitrary.
Any tips for working with Gemini through its chat interface? I’ve worked with ChatGPT and Claude and I’ve generally found them pleasant to work with, but everytime I use Gemini the output is straight dookie
Even though I don't like the privacy implications, make sure you use the option to save and use past chats for context. After a few months of back and forth (hundreds of 'chat' sessions), the responses are much higher quality. It sometimes does 'callbacks' to things discussed in past chats, which are typically awkward non-sequiturs, but it does improve it overall.
When I play with it in 'temporary chat' mode that ignores past chats and personal context directives, the responses are the typical slop littered with emojis, worthless lists, and platitudes/sycophancy. It's as jarring as turning off your adblocker and seeing the garish ad trash everywhere.
You must be joking. I’ve turned that off after first month of use. It’s unbearable. “Oh since you are in {place i mentioned a week ago while planning trip but ultimately didnt go} the home assistant integration question changes completely”. Or ending every answer with “since you are salesforce consultant, would you like to learn more about iron smelting?”
I told Gemini I'm a software engineer and it explains absolutely everything in programming metaphors now. I think it's way undertrained with personalization.
While price is definitely important, results are extremely important. Gemini often falls into the 'didn't do' it part of the spectrum, this days Opus almost always does 'good enough'.
Gemini definitely has its merits but for me it just doesn't do what other models can. I vibe-coded an app which recommends me restaurants. The app uses gemini API to make restaurants given bunch of data and prompt.
App itself is vibe-coded with Opus. Gemini didn't cut it.
I was careful not to draw binary. I was saying that Opus in Claude Code is good enough for me to make projects. Using Gemini after it seems like a significant downgrade, which actually doesn't get the job done helping me code. This is my experience, it can change if Gemini will get better.
However, for internal use I opt to Gemini, because of API cost. It is great in sorting reviews and menues out.
The order of priority for most people is: 1\ output quality 2\ latency 3\ cost. I will always pays more money if output quality is significantly better and latency is worth the tradeoff. There's also enough cost optimization strategies for applied AI applications that token cost rarely outweighs unless it's a SIGNIFICANT difference (e.x. 100-200% more).
Is it? Honestly, I still chuckle about black Nazis and the female Indian Popes. That was my first impression of Gemini, and first impressions are hard to break. I used Gemini’s VL (vision) for something and it refused to describe because it assumed it was NSFW imagery, which is was not.
I also question statis as an obvious follow up. Is Gemini equal to Opus? Today? Tomorrow? Has Google led the industry thus far and do I expect them to continue?
Counterpoint to that would be that with natural language input and output, that LLM specific tooling is rare and it is easy to switch around if you commoditize the product backend.
"There is hardly anything in the world that some man cannot make a little worse and sell a little cheaper, and the people who consider price only are this man's lawful prey."
I think we highly underestimate the amount of "human bots" basically.
Unthinking people programmed by their social media feed who don't notice the OpenAI influence campaign.
With no social media, it seems obvious to me there was a massive PR campaign by OpenAI after their "code red" to try to convince people Gemini is not all that great.
Yea, Gemini sucks, don't use it lol. Leave those resources to fools like myself.
I predict Gemini Flash will dominate when you try it.
If you're going for cost performance balance choosing Gemini Pro is bewildering. Gemini Flash _outperforms_ Pro in some coding benchmarks and is the clear parento frontier leader for intelligence/cost. It's even cheaper than Kimi 2.5.
Massive kudos to Anthropic.
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