Comment by KronisLV | Hacker News Reader

Comment by KronisLV | Hacker News Reader

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> There's definitely a way to use Claude code that is token conscious.

Colleague used Sonnet 4.6 on some pretty normal agentic coding tasks through AWS Bedrock to keep the data in the EU, 100 EUR usage in a single day. In comparison, the Mistral subscription costs about 20 EUR per month and we tested that for similar tasks it was okay, the usage got to around 10% of that monthly limit in a single day. Or Anthropic's own Max (5x) plan where you get way, way more tokens to do with as you please.

I feel like the sweet spot is having a monthly subscription with any of the providers (you're subsidized a bunch), but if you have to pay per tokens, now I'd just look in the direction of what tasks DeepSeek would be okay for, sadly probably not in the situation above. For a startup, though...

On the other hand, this feels a bit hypocritical:

> It was part of an effort to get project managers, designers, and other employees to experiment with coding for the first time, and sources tell me that Claude Code has proved very popular inside Microsoft over the past six months.

They're gonna say that the future is all AI... until they get the bill.

I was a Mistral Le Chat Pro subscriber (the €20/month plan). Yesterday I hit my monthly limit. Switching to PAYG I burned through another €40 in one evening, working on the same project, with the same tasks.

I upgraded my plan last night to Mistral Le Chat Teams. This now costs me €60 per month for two users. Limits have been reset, but I have no idea now if my per seat limit is higher than the Pro plan, or if the limit is shared between the seats, it’s really not clear. I guess I will find out next month. The limits reset on the first of the month and I really hope I don’t hit them in the next seven days.

I use Mistral Vibe CLI and I’ve written and implemented a couple of new skills[1]. Caveman, based on an idea I found online somewhere, this skill removes all extraneous response text, including articles. Makes for some fun reading, but supposedly reduces output tokens significantly. Hash-anchors, this one is based on a concept from Dirac[2], reduces search failures and also includes multi-file dispatch. It will be hard to measure, but Vibe tells me these two should result in roughly a 40% reduction in token burn.

[1] https://codeberg.org/MimosaDev/skills

[2] https://dirac.run/

I was trying to get a better sense of the time cost quality matrix of these, so I threw together a quick eval of Sonnet 4.6, Mistral's dev model, and Opus 4.7 (figuring it's what you'd use if you were on Max).

The results for a function implementation and test of levenshtein distance in js are pretty similar but Mistral is 30x cheaper than Opus 4.7 and 4x faster than Sonnet 4.6.

https://5m6qnuhyde.evvl.io/

But that's not very informative.

Levenshtein distance is not only a well-understood problem, it's small, self-contained, and extremely well-represented in the training data. The kind of problem where even small/bad models can excel. The golden standard for those tasks is just "use a library" so no wonder the beefy models are expensive: you're chartering a commercial airplane to go grocery shopping.

My personal benchmarks are software engineering tasks (ideally spanning multiple packages in a monorepo) composed of many small decisions that, compounded, make or break the implementation and long-term maintainability.

There's where even frontier models struggle, which makes comparisons meaningful.

>> many small decisions

It’s making guesses not decisions, framing as decisions will lead you astray to wasted time and tokens.

It’s vaguely productive to tell them a ton of relevant info upfront attempting to minimise their need for load bearing guesses. I say vaguely because obedience is generally only around the level where it's good enough to lull you into a false sense of security, not to actually be obedient.

It’s a bit more productive to use the various loop mechanisms (hooks, /goal etc) to evaluate each end of turn against guard rails and reject with clear instruction on whats unacceptable. Obviously if you only do this without the front load of info then you’re likely to spend more tokens to reach a satisfactory end of iteration.

If I perfectly know all the guardrails I need, I don't need an LLM, only Prolog.

While you are correct that something like Antigravity 2 + Opus 4.6 can handle large scale software engineering tasks, I would argue that it is usually (but not always) better "coding agent hygiene" to work on smaller code modules and as the human in the loop be a partner, not someone who prompts and then disengages.

Breaking code up into composable chunks has worked well for me over 50+ years as a professional software developer, and I can't get away from the idea that it is still usually the way to go using agentic coding tools.

The one detail I did forget to mention is that if anyone goes with the Mistral subscription (instead of paying per-token), then the Mistral Vibe tool gives you their Medium 3.5 model by default, with a 200k token context. It will probably be enough for plenty of tasks, though there's also a noticeable difference between that and up to 1M.

> They're gonna say that the future is all AI... until they get the bill.

I mean, the will continue to say so, they just want to be the ones being paid for the service, not anthropic :)

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