Comment by simonw
5 hours ago
GPT-6 Luna being half the price of GPT-5.6 Luna is a really big deal.
Here's GPT-6 Luna pelicans: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
And GPT-6 Sol: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Scroll to the bottom for the GPT-6 Sol max one: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
For comparison, here are the pelicans I got for GPT-6 Astra: https://tools.simonwillison.net/markdown-svg-renderer?url=ht... - I still like the Astra Max one best.
Here's a comparison grid showing all of the GPT-6 and GPT-5.6 pelicans at all effort levels: https://static.simonwillison.net/static/2026/gpt-6-and-5.6.h...
The grid is actually really interesting, because it shows that the 5.6 family default to brighter colors than the 6 family.
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.
--
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.
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.
How long can OpenCode bleed for?
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Well, here's the neat thing: they don't!
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).
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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
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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.
perhaps they use this as the carrot to get you locked into their monthly plan over anthropic's.
works great until they raise prices.
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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?
Simon, love your work, one piece of minor feedback for the individual model pages is to make the font of the model name potentially bigger than (and above) the conversation id (which means nothing to the audience) "2026-09-22T18:28:00 conversation: 01m355zvyw8946qyraa8zpz6h9 id: 01m355zvyx47zxx5c6q6b3fg0m#".
I had all the tabs open individually and harder to scan which model is which... otherwise keep up the great work! I like the grid view a lot. (Also the pages have no OG images set, which impacts what the link looks like shared)...
That's a good idea. It's the default output for my `llm logs` command, but that header could at least show the model ID.
OG images will require me to move away from publishing in a Gist and linking to from a JavaScript page that loads the Gist. Probably worthwhile though.
I think you can make it work without leaving Gists by using a Cloudflare Worker as a workaround. The Worker sits in front of the renderer page and adds the og tags to the HTML before it's sent out. You'd also need to turn the SVGs in the Gist into a PNG for the og:image, and decide if you want a grid or just one image, any text formatting, and how long to cache...
I got it working in a quick local test (grid of all the reasoning efforts, cached per Gist, loads from the raw Gist URL so it doesn't hit the GitHub API rate limit).
Code + prompt + notes here: https://gist.github.com/matznerd/ece297107bd99ac028c7962c217...
Basic concept is to:
1. Put a Worker on the /markdown-svg-renderer route. Normal visitors get your page exactly as it is now.
2. When a link has ?url=<gist>, the Worker reads the Gist and adds og:title, og:description and og:image to the page's HTML. Link previewers like Slack and iMessage don't run JS, so this is the only way they see them.
3. og:image points to a second Worker URL (og.png?url=<gist>). It takes the SVGs from the Gist, puts them in a grid, and converts it to a PNG, since previewers won't show SVGs.
4. Both results get cached per Gist, so each Gist is only fetched and rendered once, even with a lot of traffic.
Things to customize:
- Title and description (mine: "gpt-6-luna SVG of a pelican riding a bicycle" / "6 runs, reasoning effort none to max")
- Grid of all runs vs just one image, plus layout, labels and font
- How long to cache (I used a day, but edited Gists keep the old preview until it expires)
I find it very interesting that for both these models we such a clear progression of better images with higher thinking levels from 'hardly useful' to 'pretty nice'. I feel on many other models low and max are much closer.
Not that this benchmark is super relevant anymore but these look worse than I expected.
Yeah, it's interesting how much worse they are than the Astra pelicans. I think that reflects a tiny bit of genuine value still left in the benchmark, to be honest.
Tons of value left, especially for open source models. I would say the benchmark is yet to be truly saturated (just look at the legs and seat to see what I am talking about) and I always look forward to seeing them. Thank you!
To me the main upshot of this benchmark is precisely that the pelicans still usually look a bit wonky. It's bizarre, since this definitely has a good solution, but it's in line with my experience that memorization of the training set just... isn't happening very much? As in, whether a model fails or not doesn't have much to do with whether that exact question was likely posed many times before.
It would be extremely funny if the explosion in SVG generation capability in particular was a result of this benchmark
It's surprising but MiMo V2.6 Pro performs better and is cheaper than GPT 6 Sol on my benchmark[1]. Open weight models are really snapping at the heels of the major western models.
1 - https://bench.killswitch-lang.org
Here are some somehow standardized pelican tests but for 3d scenes in threejs at threejseval.com
Luna 6 High: https://threejseval.com/models/gpt-6-luna-high
Sol 6 High: https://threejseval.com/models/gpt-6-sol-high
You can compare any other model on the same prompt. Gallery unlocks after 4 votes: https://threejseval.com
> GPT-6 Luna being half the price of GPT-5.6 Luna is a really big deal.
Is it? It was already too cheap to meter for me. Luna 6 is actually worse on some benchmarks than 5.6. I’d have loved improved performance for 2x the price than ~equal performance for 0.5x the price.
I’ve been doing really heavy text analysis work with LLMs where false negatives/misses are important to minimize and my god did I hit cost thresholds quickly with 5.6 Luna — it was the first time I felt motivated to seriously work with local open models, even if inference was degraded for the task. Cheaper and much better inference now brings me back to the closed models for better or worse.
5.6 Luna was already discounted at half the price on OpenRouter. Looks like they made it permanent.
Hopefully Terra 6 slots somewhat nicely into this space.
Yes, I am mildly disappointed with these releases.
I expected a Fable 5 -> Opus 5 situation, where GPT 6 Sol would perform on par with GPT 6 Astra.
Instead it's more like a price cut on GPT 5.6 Sol, and I'll have to stick with Astra for my work.
The only thing I can hope for is that more users switching to the GPT 6 Sol model frees capacity, allowing OpenAI to hand out some usage resets.
Pelicans gonna devour capibaras if they see these depictions.
What's with the radial spokes? When are we gonna start seeing proper cross lacing?
And how about that head tube angle?
How come the pelicans get older with more reasoning? Is GPT 6 taunting us with our mortality?
Wow I cannot believe Luna is getting even cheaper. IMO this is the model that is going to change the world.
Everyone said tokens were too expensive but these are getting close to free while still having fantastic performance.
Many of them still get the layers wrong.
They put both legs on the same side of the bike.
Even Astra max which actually put one leg on each side of the bike still somehow messed it up because when it added the bike chain, it put the left leg between the bike chain and the frame.
Yep. We just switched several classification jobs we run over to gpt-6 luna. Love the cost savings.
What I overwhelmingly love about that Pelican grid is the two best ones, they've put a neck scarf on to show speed and wind.
I've been sharing that pelican grid in my circles a whole bunch, it's great! I think only one data point is missing, generation speed. Would be interesting to see how the reasoning level/token counts relate to speed.
The sol max looks like it's absolutely ripped for some reason
He’s been biking a lot
he's been cycling a lot
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It's funny that even Astra doesn't know you ride a bike by straddling it between your legs. (EDIT: Oh, I guess Max gets the occlusion. But it doesn't realize it has to pick direction the knee bends in.)
> Error: Gist API returned 403
Is what I'm getting on the top two links.
Is GPT-6 50% cheaper?
> GPT‑6 Luna vs. GPT‑5.6 Luna | $0.20 → $0.10 | $1.20 → $0.50 | 50% cheaper
I can read it as follows (below), meaning that GPT-5.6 is 50% cheaper.
- GPT-6 = $0.20
- GPT-5.6 = $0.10
The table on https://developers.openai.com/api/docs/pricing is more readable:
Yeah, how they put, is confusing to me, they should have put that table instead of what they have right now in the article.
Yes, GPT-6 Luna is 50%-58% cheaper than GPT-5.6 Luna. (I think the blog text and graphs make it pretty clear.)
Yeah, but it confuses me, I read left to right, so if they put GPT-6 and $0.20 first, I would assume that's the new pricing, they should make it clear, not confusing.
> half the price of GPT-5.6 Luna
Half the price when it launched, or after the price dropped by 75%?
After the price drop. GPT-6 Luna does not perform better than 5.6, so they can't raise the price.
If you're happy with letting Meta train on you, Muse Spark 1.3 Contributor pricing is a much better deal than Luna
looks like they're positioning luna to tackle the low-cost cn models
> GPT-6 Luna being half the price of GPT-5.6 Luna is a really big deal.
good god
Is Luna (on "low" thinking) the first left handed model?
That GPT-6 Sol max pelican looks… so old and depressed.
out of curiosity, do you retry the same model multiple times to see the range of output it comes up with? or is it purely a 1-shot test
Isn't everyone pelican-maxxing these days?
He also blogged why he thinks it's still useful
When the Astra one was last time run? It's probably better to run these 2-4 weeks after release when models get nerfed to get idea of performance closer to what it is.
How do you know they are nerfed, and how do you know the timeframes?
Do you have a page showing all the pelicans you've ever created? Could be fun to browse - kinda like https://progress.openai.com/ but visual. (It's a shame they don't keep it updated)
I'm so tired of looking at benchmarks. I always look fwd to the pelicans.
https://simonwillison.net/tags/pelican-riding-a-bicycle/ but I need to build something better.
My new sub-benchmark is which combinations achieve the hook at the end of the upper beak. Right now just 4: Astra Max, XHigh and Medium; GPT 6 Sol Max
What I like about the grid of SVGs is from I can see that Astra high seems to yield similar quality and price to Sol 6 max.
And Astra medium seems to yield similar or better quality for the same price as Sol 6 xhigh.
Looking at this, AI still has a long way to go. In Sol Max, the pelican's legs are missing on one side—how can one side have two pedals and two legs...
And the bicycles have weird dimensions -- extremely slack head tube angle, handlebars in the wrong orientation, etc.
That's just foreshadowing the next generation of 32" all-mountain frames.
Out of all of the benchmarks out there, pelican bicycle bench is the only one I care about. Thank you Simon.