6-luna is at the pareto for most of the tasks! I dont know how they make money here but its insane value from a closed source model. I'd go further and say it makes no sense (privacy, sovereignty etc aside) to use many other models as its not only expensive but also many providers don't have that much GPUs to serve at a significant volume. https://openrouter.ai/rankings?view=month#top-models 5.6 luna is already the most used model this month.
My OpenCode Go stats for the last 30d:
Cached Read: ~6,500M
Input: ~150M
Output: ~20M
Approx $40 worth of usage across DeepSeek V4 Flash + MuseSpark Contributor 1.3. And a bit of both the GLM models. This is covered in a $10 subscription.
If I were to use Luna's API pricing:
$0.02 x 6,500 = $130
$0.20 x 150 = $30
$1.20 x 20 = $24
So $184. And this is assuming smaller coding sessions (<272K) beyond which Luna pricing doubles.
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Cost wise, these models are nice for small stuff. Translations etc. Any model that does not provide multiple Mtoks of cached reads per cent is not very useful to me for coding workflows.
These are also the orders of magnitude of our production agents for our business (NOT coding). Cache reads are so heavy compared to anything else that it's the only price point that really matters, regular input and output are negligible.
I need aggressive cache read pricing with full prompt_cache_key support to have a model be financially viable for our workload. Right now Meta Muse 1.3 Contributor is the only one that makes sense--but we are starting Evals on the new MiMo 2.6 class to see how it holds up.
How have you found Muse Spark 1.3? It doesn't get much mention, despite pretty good benchmarks. I've been using a bit at home and find it quite good, often finding mistakes made by Opus 5.
It’s not direct token to token pricing and everyone misses it. The cost is how much tokens to complete something multiplied by token pricing. I can have a model at .0001 per million tokens but it’s so inefficient that it takes 10B tokens to complete a task means it’s expensive.
I am not designing rockets. Most of my work is bog standard hobbyist stuff: compilers, vms, sandboxes, system tools of various kinds, SSGs, markup languages, plain text ledgers etc. Even Gemma/Qwen running locally can manage this.
Frankly, I have no idea what people do with Opus/Fable etc. I don't think anything I do needs something that charges $50/M for output tokens.
How long can OpenCode bleed for?
Are they bleeding? Their multipliers seem to be reasonable. They are not offering $60 worth of usage for $10 on every model, only some. In the case of the expensive ones, it is only $15.
Given how subscription models work (not every one uses every last $ of their plan), they should achieve breakeven soon enough I guess.
They already stopped. That's why the service quality declined.
What did you notice?
Snark aside, Luna 5.6 was (is) an incredible game-changer.
> Well, here's the neat thing: they don't!
perhaps it then does mean - squeeze as much as you can get off this actual free usage.
"We lose money on ever sale, but we plan to make it up in volume"
Can't agree more. Between 5.6 Luna and Gemini 3.8 flash I'm so happy for the value I'm getting for my dollar (subscription pricing not API pricing) :)
Gemini 3.8 Flash looks like its better than v7 Luna/Sol on DeepSWE v1.1 while at $0.75 per million input tokens and $3.75 per million output tokens. Luna is much cheaper, but Flash has nearly Astra's performance for under the price of Sol ($2/$10).
Flash thinks much more so it’s pretty much line with Sol for performance. That said I like flash coding style much more than OpenAi models.
out of curiosity, what type of code/language do you usually use flash to write?
I use it for golang, and it is fantastic. Incredibly fast. It seems the llm and I “understand” each other. I have to be less careful in my exact phrasing. It kind of just does what I want and expect.
When I ask for an explanation it adds the right amount of detail. Of course, some of the material is new to me so subtle errors are hard to spot. But at least I’ve caught Terra and Sol on inconsistent messaging.
Also I’ve found 3.8 flash to circle back to root issues even at the conceptual level like problem fit and conceptual solution direction or architecture when I wasn’t achieving my goals. It flat out said I was attempting to use the wrong tool. Whereas Sol and Astra kept rabbit holing and looking for tiny implementation errors. Even after prompting them specifically to look at it broader.
what harness or plan are you using 3.8 flash with?
TBH: I really like how fast 3.8 Flash is... Once I have clear plan, I feel quite confident in delegating large parts of implementation to Flash and Luna
look at token use, 3.8 flash is a huge token hog compared to openai models
Gemini 3.8 Flash and 3.1 Pro are pure rubbish. Very little thinking, mediocre and usually incorrect results. They cannot be compared to frontier models.
This is my experience as well. I am surprised that lot of people find it much better than Luna.
I suspect that the people saying this haven't used Luna.
It's also weird that anyone uses it outside of an enterprise. They force you to use Googles inferior harness on the plans and I doubt any mere mortal is paying that much, for so little usage, with the worst harness on the market.
6-luna is no improvement over 5.6, merely a price cut.
And info from the help page with message limits suggests the 50% price cut does not apply to the subscription, where they applied only a 1/3 price cut instead.
I'm not thrilled with this release.
Opus 5.5, which matches GPT-6 Astra performance at a cheaper price, is much more interesting.
> I dont know how they make money here
By raising it from investors.
To whom they promise the Sun, the Moon, and the Stars. Roflmao. Whatever the merits of the underlying technology, the business model is pure hucksterism.
How does 6-Luna xhigh compare to 6-Sol medium? Or more broadly newer/bigger model with lower effort vs older/smaller higher effort?
Read the link! It's in there.
MiMo 2.6 Pro is at the Pareto frontier (the one where you only need 20% of the smarts for 80% of the tasks) according to Artificial Analysis, nicely filling in as a substitute for a hypothetical 'GPT-6 Terra' (which doesn't exist as far as we know). That's pretty darn impressive from an open model.
That's not what the Pareto frontier is; you're mixing up Pareto frontier with Pareto principle.
https://en.wikipedia.org/wiki/Pareto_front
https://en.wikipedia.org/wiki/Pareto_principle
Despite the error in the parenthesis, it's exactly what he says: https://artificialanalysis.ai/?intelligence-category=text-on...
I was just responding to the error in the parenthesis.
perhaps they use this as the carrot to get you locked into their monthly plan over anthropic's.
works great until they raise prices.
There's no difficulty in cancelling.
I guess I have to update my pareto front then: https://philippdubach.com/posts/jev-model-router-for-pi/
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> I dont know how they make money here
I assume it's a subsidy to get more training data.
EDIT: Okay downvoters, what's your take on why they're giving away Luna for so cheap?
By default, OpenAI does not train on API data. I promise you that Luna's low pricing is not a subsidy to get more training data. We've been lowering prices for years.
(I work at OpenAI.)
Offering Luna for cheap is like restaurants giving you free bread and water. They're pretty sure that you're going to end up eating the expensive stuff on the menu.
Note that to sit at a restaurant you're obliged to order something, though. Here there is no obligation to go beyond the model you choose.