Advancing the price-performance frontier with GPT‑5.6

"Half the money I spend on advertising is wasted; the trouble is I don't know which half." -John Wanamaker

This applies even more strongly to model choosing. I know for a fact that majority of my work doesn't require a very strong model, but separating the trivial and non-trivial tasks is a famously hard problem (if at all decidable).

> Starting today, GPT‑5.6 Luna, our fastest and most affordable model, will cost 80% less,

I don't have the words.

I genuinely thought we were in a stage where we were plateauing and going in for 5-10% improvements over months. Seeing spikes like this makes me question about where the floor really is.

Making Luna, which was already very cheap and extremely capable, 5x cheaper is crazy. I use Sol at work but Luna at home, and while there's definitely a difference, it doesn't feel like night-and-day. After a year of ever-increasing prices it suddenly feels (between this, Kimi K3, GLM 5.2) that prices are falling again.

> The kernel work helped reduce the end-to-end cost of serving the model by 20%, while its experiments increased token-generation efficiency by more than 15%.

If the cost of serving GPT-5.6 just dropped by 20%, does that add up to literally billions of dollars in savings per month?

We know Anthropic spend $1.25 billion renting inference capacity from SpaceX (in two Colossus datacenters) from the SpaceX IPO, but we don't know how much of Anthropic's inference capacity that is (presumably a small fraction, since they were operating on top of AWS and other providers before the SpaceX deal.)

I've not seen any numbers that hint at OpenAI's per-month inference bill, but surely that has to be in the multiple billions of dollars as well.

So 20% is a really, really big deal.

This feels like the dialup->broadband transition to me.

I was already a huge proponent of Luna for things like deep research. Being able to run 5x more for the same cost is simply bananas. We are already running 10 parallel agents for hypothesis generation. I cannot imagine 50. The statistics become much more interesting & powerful when you can run so many samples of the exact same prompt+model without breaking the bank.

Didn't expect that. Luna pricing is crazy now. I don't think there is anything on the market that competes at this price-performance point.

For our production app, OpenAI clearly is the best provider now. Their API is very reliable and has many nice features. The price-performance of the model lineup is incredible. We used open weights model via Fireworks for a long time (e.g. Kimi K2.5). Fireworks is a great provider but we still ran into issues here and there (Same with Anthropic and Google). OpenAI just works, is fast and in my view has a better price-performance ratio across almost all levels of intelligence.

I generally just check the Price/Performance graph on Openrouter: https://openrouter.ai/rankings#performance#benchmarks. Activate the "Show Pareto" toggle on the right.

I was still using GLM-5.2 in my personal projects, but this just made Luna a very easy choice.

80% price cut for luna is a very aggressive pricing move

makes it by far the best choice for most workloads that do not need bleeding edge intelligence (reminder: luna can be comparable to opus 5!)

I prepaid for a ton of DeepSeek v4 flash tokens. When I use them I would like to try purchasing Luna tokens for a while. I like cheap and fast models and since I am retired, if I waste time having to sometimes manually switch to a stronger model, that is OK with me.

80% less for Luna is absolutely crazy, in my opinion we may reach a point in the next year where powerful models on the API could potentially be cheaper than subscriptions. Compute just keeps decreasing in price.

Friendly reminder that GPT-5.6 in Codex cannot spawn Luna subagents, only Terra [1]

To fix this you currently need to make your own copy of the bundled model catalog [2] and opt Luna into MultiAgent V2.

1. https://github.com/openai/codex/issues/32031

2. https://github.com/openai/codex/issues/32031#issuecomment-51...

What are your use case for these? I’m manly interested in coding where more capability is better - give me a 10x model at 10x the price and I’ll take it. A worse model at very low cost has no appeal to me. At-least not for coding. Translation maybe? OCR?

> $0.20 per million input tokens and $1.20 per million output tokens for Luna

This is... ridiculously cheap. Amazing!

> In a compute-constrained world where model demand is growing faster than capacity

I don't buy it.

There have been recent weeks where some of the mid-level models (Hy3, Laguna M.1) are free (true for parts of June and July, see Hy3 in Cyan) . Even then the total token usage appears to be reaching a steady-state.

https://openrouter.ai/rankings#top-models

^ the first graph is tokens per week across all models

I guess we just can only throw ideas at an LLM at a certain rate.

I still have ideas and now I can have an LLM vibe code what I want, but I'm not going to let an agent just run unattended for longer than a few minutes or a few bucks for hobby projects.

So maybe it is a matter of lowering the cost of an LLM so I can let it churn for hours at a cost of pennies... But I suspect demand for tokens is very price-elastic.

I've been having issues with it using unreasonable timeouts for tasks that should have failed within minutes, not hours.

This is one of the things OpenAI has been focused on for an year or so that led to the doomed autoswitcher in ChatGPT .com (switching models based on estimated task complexity) that was quickly reverted

Whereas Google with Gemini 3.x, Anthropic with Fable etc are happy to just go for 'big model with dense params'

It's hard to guess from the outside of course but just this kind of talking points focus on GPU efficacy is what we see from OpenAI and Chinese open source labs more often than from Anthropic or Google Deepmind and this benchmark chart seems to concur

It says Luna is fastest, but doesn't it take way more steps to get the same job done?

https://deepswe.datacurve.ai/ - (See the Agent Steps view)

Or is the output speed so much higher that it cancels out?

I don't see a lot of benchmarks that record actual time. But on AA, Sol on Low beats Luna on High for Time Per Task.

If Luna is so good and cheap, I'm wondering why ChatGPT Free users still only have access to GPT 5.5 Instant. The API pricing for chat-latest is the same as Sol(!),[1] while Luna is 25× cheaper [2]. Even assuming highly inflated API pricing for chat-latest, Luna not being the daily driver on Free and Go plans feels off.

Perhaps users prefer instant responses over thinking models so much so that using a more expensive and less performant non-thinking model is worthwhile.

[1] https://developers.openai.com/api/docs/models/chat-latest [2] https://developers.openai.com/api/docs/models/gpt-5.6-luna

It's a clever strategic move: grab the market of cheap low end models within the product range. It's lower friction to switch a model than a provider.

"too cheap to meter" and Luna is still more expensive than deepseek-v4 pro

I truly wonder what kind of model is luna now.

Before I thought it was just an improved version or at least in the same class as gpt 5.4 mini but now it's being priced like a nano model!

I thought about it because Terra has similar pricing to 5.4 and Sol is similar to 5.5.

Luna was already my workhorse before, it performs very well on high/xhigh for most of the tasks, very happy about this drop.

Has anyone ever done a comparison between the smaller models like Luna, against the previous GPT 5 frontier models? Have we gotten to the point where the small models are as good as the frontier models of the past, or is there still a way to go?

> These lower prices for Luna and Terra are also reflected in how usage is counted against paid subscriptions

Does this seem higher or lower limits ?

Isn't OpenAI burning billions and have billions more spending commitments? If they managed to downsize so much the cost they should have kept the price the same and become profitable, really weird move, unsure what led to this.

While I can't deny this is a huge technological result, and it's laudable they reduced the price because of it, 80% is really a lot. I can't help but wonder, is this because of the model's capabilities, or was the initial system just really sloppy? The public will probably never know the details

Does that also increase the token count in their subscription like ChatGPT GO?

This is awesome. I’ve recently set up my opencode to use 5.6 terra for my main agent, who delegates work to a 5.6 Luna coder agent. So far it seems to work well, and reduce costs a lot. With this price reduction, it will work a whole lot better. Perhaps I can get my github copilot quota to last the whole month now.

Those prices on luna are killer.

Haiku was already in a ditch.

But this is coming straight for the jugular of a ton of models on openrouter.

Deepseek Flash is still much cheaper:

- lower input/output token pricing

- the cached token price is $0.0028/Million tokens, which is like 50-90% of tokens

Might just resub. Will experiment with Luna next sessions. 5 h window is not working very well for me. But if I can drop down to Luna at 20-30 % left and comfortably ride out the wave then.. that might just work.

Looks like I might have a reason to use something other than Deepseek V4 Flash.

I would pay significantly more to use these models if there was a legal contract that guaranteed they weren't ever terfing them and some way to prove that.

How is that economically possible? I’m so confused by those prices

It would appear that rolling my own Anthropic-free harness / serving stack with a closed-weight carve out for Codex models is an absolute win.

gpt 5.6 luna was already at the intelligence/cost frontier and it's now even cheaper ...

This is awesome. Luna is a pretty great model on xhigh.

Seems like they cut the tiers(GPT-5.6 Luna) where GLM and Kimi compete and still held margin for their frontier models

Basically lunar at extra level can cover all use cases scenarios other than those requiring opus up. Goodbye sonnet and haiku

Your move, Anthropic

Not sure who would use Terra anymore. Pair Luna High/Xhigh with Sol Medium and that's your power stack

We swapped an internal system from gpt-5-mini to gpt-5.6-luna and saw no benefit but 4x cost. Sufficed to say: we swapped back to gpt-5-mini.

Seems like they're working to destroy the local LLM argument. Right now Haiku is $1/$5 in/out. You can grind out $12,000 worth of haiku (or arguably, sonnet) class tokens in about 5 months on a Blackwell RTX 6000 96GB especially if using concurrency. BUT, but, if you use a g6e.xlarge on aws it's now more expensive than buying tokens from OpenAI @ $0.20/$1.20. It also destroys "the Mac Mini argument", pushing the ROI to ~4 years.

> GPT‑5.6 Luna, our fastest and most affordable model, will cost 80% less

Looks like the Chinese models are really making a dent. Having 3 different price categories with the "most affordable" one still costing more than GLM 5.2 never made sense.

There were people on HN who still thought that the API prices were being subsidised. The level of conspiracy theory was off the charts on this topic. You would get these price reductions month over month you would still have people believing in crazy stuff.

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The milking games have started. The billionaires want their money back.

Edit: Yes, 80% minus is still milking. Because you empower these greedy mega-corporations. Just look at the RAM prices increase, then you see that the more money you give these hungry dragons, they more they will eat up. Don't get fooled by their "less cost now" advertisement.

Model segmentation & distillation like this that asks the consumers to pick exactly which version of the algorithm will solve their problem is evidence for lack of intelligence instead of its presence.

They don't mention Grok at all.