From the AI ROI Series, recorded 13 October 2026. On Monday Meta launched Muse Wonder, and on the independent tests it sits within a few points of Claude Opus 5.5. Meta priced it at exactly the same list price as Opus, $4 per million input tokens and $20 per million output, to the cent. Mark Zuckerberg says Meta has reached the frontier. And yet in all my conversations with enterprise clients, I have yet to meet one running a Meta model in production. So who is this for, and is it worth a dollar of your AI budget?
If you own the AI budget, or you pick the models your company runs on, this one is for you. If you lead FP&A and have to defend that budget, share it. The plan is simple: where Meta sits in the enterprise league table, who Meta actually built this for, the tokenomics against Claude and OpenAI, and whether this is a buy today or a direction to watch.
The room Meta just walked into
Menlo Ventures surveyed about 500 enterprise AI buyers at the end of last year, which is a long time ago in this market, but it is the best league table we have. Anthropic held about 40% of enterprise model spend, OpenAI about 27%, Google about 21%, and everyone else, Meta, Mistral, Cohere and the rest, shared about 12%. Chinese models were around 1% of enterprise usage, and open-source share fell from 19% to 11%, partly because Llama stalled after Llama 4. So that is the room Meta just walked into: three vendors with almost 90% of the spend, and xAI pushing for a seat as well.
Let me be fair, because this is a real model. On Artificial Analysis, Muse Wonder Full scores 56 on the Intelligence Index, tied with Claude Sonnet 5.5 and two points behind Opus 5.5 at 58. On the test of real professional work it is close to a tie with Opus. That is 14 months after Zuckerberg rebuilt the whole lab and six weeks after the previous model, which is a team shipping fast, and I would expect the next one to be better. Credit to them.
Who Meta actually built this for
Zuckerberg was direct about it. He told the Financial Times that everyone else is focused on knowledge work and automation, while Meta’s goal is what it calls personal superintelligence for consumers. Follow the money and it makes sense. Meta made about $60.8 billion in revenue last quarter, almost all of it from ads, and that one quarter is roughly what Anthropic makes in a year. Meta does not need to sell you an API to pay for its AI, because AI pays for itself inside Meta. Two weeks ago on this show we covered Meta cutting its internal Claude Code users from about 60,000 to 30,000 and moving them onto its own model, and its Muse app is now the most downloaded app in the US. The first customer is Meta itself.
Enterprise is on the list. On the July earnings call Zuckerberg said they see a large enterprise opportunity to sell to businesses through APIs, business agents, and maybe compute. But the launch announcement names no enterprise customers and no cloud partners. So today, Meta’s AI is first for Meta.
The price sheet tells you nothing
Say you want to try it anyway. What does it cost per token? Identical to Claude Opus 5.5, so the price sheet tells you nothing at all. Artificial Analysis runs the same work on every model and adds up the bill, and that is where the models separate. Muse Wonder comes in at about $4.52 a task and Opus at about $5.98, so roughly a quarter cheaper, because it uses fewer tokens to get there. The one I find most interesting is GPT-6 Astra. Its list price is two and a half times higher, yet per task it is cheaper than both of them at about $3.26, and GPT-6.1 Sol is around $0.72.
| Model | List price per million tokens (in / out) | Cost per task (Artificial Analysis) | Intelligence Index |
|---|---|---|---|
| Claude Opus 5.5 | $4 / $20 | $5.98 | 58 |
| Meta Muse Wonder | $4 / $20 | $4.52 | 52 (Full: 56) |
| GPT-6 Astra | $10 / $50 | $3.26 | 53 |
| GPT-6.1 Sol | n/a | $0.72 | n/a |
One quick lesson inside that number. Muse Wonder reads about 7.7 million tokens for every 54,000 it writes, and cache reads are about 20 times cheaper than fresh ones. Without the cache, I get about $32 a task instead of $4.52. My math, so check it. The cache is doing most of the work, and almost nobody reports it. It is a little geeky, I know, but the cache line is the biggest number on your AI bill, and it is the reason you have to measure cost per task, per execution, per unit of value delivered, rather than reading a rate card.
Did the work come out right?
Then the number that really matters. Nathan Romeo D. runs his own private benchmark, MSP Bench, where a model has to find and fix hidden problems in a real IT provider’s systems with a million dollars at risk. Great work, go follow him. Muse Wonder protected about $657,000 of that million. Claude Opus 5.5 protected about $893,556. Muse was much faster, four to nine minutes a run against 17 to 30, but it protected about a quarter less, and Sol protected more than Muse at a sixth of the cost. One tester, one benchmark, so it is directional. But it is the outcome, and the outcome is what goes to the board.
Think about your own enterprise AI program for a second. Do you measure it like that? Forget which model was faster; that is not my point. I am giving you the detail to make the case that you should absolutely be measuring, benchmarking, and working this out on your own workloads. Maybe that is your next step.
What to check before this goes near production
Before this goes anywhere in production, here is what I would check from Meta’s own documentation and the launch coverage. The API version is listed as a preview. The stronger model, Muse Wonder Full, is not on the API at all, only inside Meta’s Muse Code subscriptions. Those subscriptions default to a contributor mode in which Meta may train on your prompts and code, and zero data retention is available subject to approval. The launch had bugs that Meta’s own AI chief owned publicly, and there is no research paper yet. All of that is pretty normal for a week-one launch, and it is exactly what security and procurement will ask on day one. One point in Meta’s favour: the API speaks the same language as OpenAI’s and Anthropic’s tools, so trying it is close to changing a model name. A test is cheap.
Buy or watch?
So is it justifiable today? For most enterprises, honestly, I would call it a direction to watch and a cheap test on non-sensitive work through the API. It is not a platform decision yet, but I will watch it closely. Meta is spending $130 to $145 billion on capex this year, it ships every few weeks, and it clearly wants to sell to businesses the way Anthropic does. They want a piece of the pie.
For your budget this year, I think the biggest effect of Meta at the frontier may be on the price of the models you already use. Every new contender gives buyers leverage at renewal, but only if you can show what each model cost per finished task on your own work. Without that, you pay list. That is the same lesson as the cost-to-serve story from last month, seen from the buyer’s chair: the vendors measure every token to the cent, and the buyer who cannot has nothing to negotiate with.
The page you bring to a renewal
What you see in the video comes from our demo environment, built with a handful of prompts. My customers do this on their own data. Every AI dollar broken down by provider and by model, side by side. Cost per finished task by model on this company’s own work, rather than on a benchmark we quote here. And for each workload, which model would have done the same work for less. That is the page you bring to a renewal, and it is the page a CFO can defend. The same record sits underneath whether you read it through our platform, through Kai, or through your own AI with our MCP connector. This is what Olakai, the Enterprise AI System of Record, was built to do: capture every interaction and every outcome across every coding tool and agent you run, so the model question becomes a measurement question rather than a guess.
So let me ask you four things, and they are table stakes for every AI leader all the way up to the CFO.
- Do you know which models your teams used last quarter?
- Do you know what each one cost per finished task on your own work?
- Do you know whether that data went through a door you approved?
- If a new model showed up tomorrow, could you test it in a week?
The one line I would take to the board: we know what each model costs per finished task on our own work, so we can test any new one in a week and negotiate with the numbers.
Directional as always. The data is public, so check my math, and tell me in the comments whether anyone in your company has run a Meta model in production yet, and on which workload. I’m Paul, co-founder of Olakai. Your AI is an investment, so let’s measure it like one.