Introducing Muse Spark 1.1

I personally do not like Meta, but I'll say this. The more competition, the better for regular consumers. (Enterprise too)

- Chinese models

- Grok

- Meta

- Google

- OpenAI

- Anthropic

I think this is a win. I'm building like crazy to take advantage of all these subsidized tokens while I can.

My trust factor is gone with Meta right now. Has there been any independent analysis to confirm they didn't cheat on benchmarks again?

The pricing is insane: $1.25/$4.5 for 1M tokens, and $0.15 for cached input!

https://dev.meta.ai/docs/getting-started/pricing-rate-limits

Their published benchmarks seem to indicate that it's pretty good at coding and multimodal, but VERY good at successful tool calls.

What kind of use case would be best for that shape?

Competition for cheaper and efficient models is a good thing, regardless of if you don't like SpaceX, Meta, etc. Especially from US based labs

I for one am really glad to get competitive models that will push the major labs to bring prices down. While Chinese open source labs are also great, unfortunately when it comes to US/Western political pressure it won't often have as much of a bearing on labs bringing prices down, especially for enterprises.

Also if these numbers are true, this is truly breaking ground finally for Meta.

I personally do not like Meta, but I'll say this. The more competition, the better for regular consumers. (Enterprise too)

- Chinese models

- Grok

- Meta

- Google

- OpenAI

- Anthropic

I think this is a win. I'm building like crazy to take advantage of all these subsidized tokens while I can.

My trust factor is gone with Meta right now. Has there been any independent analysis to confirm they didn't cheat on benchmarks again?

The pricing is insane: $1.25/$4.5 for 1M tokens, and $0.15 for cached input!

https://dev.meta.ai/docs/getting-started/pricing-rate-limits

Their published benchmarks seem to indicate that it's pretty good at coding and multimodal, but VERY good at successful tool calls.

What kind of use case would be best for that shape?

Competition for cheaper and efficient models is a good thing, regardless of if you don't like SpaceX, Meta, etc. Especially from US based labs

Also if these numbers are true, this is truly breaking ground finally for Meta.

Considering the DeepSWE result (imho if you're gonna give value to benchmarks this is one of the best) it's not good enough.

Is this the model trained on Meta "draftees"? Are we seeing this in the jump on JobBench?

Very strong pricing, cheaper than Grok 4.5, particularly the cached reads. We'll have to wait to see if it's actually worth using (it's not on OpenRouter yet).

Yeah, no thanks. I cannot think of a worse company to trust with additional personal data.

A lot of these benchmarks are unfamiliar. Are labs just choosing the ones that make them look best?

This is not open-weights, right?

Meta is back in the game, albeit not at the top. Impressive stuff, nonetheless.

Considering the DeepSWE result (imho if you're gonna give value to benchmarks this is one of the best) it's not good enough.

Very strong pricing, cheaper than Grok 4.5, particularly the cached reads. We'll have to wait to see if it's actually worth using (it's not on OpenRouter yet).

Yeah, no thanks. I cannot think of a worse company to trust with additional personal data.

A lot of these benchmarks are unfamiliar. Are labs just choosing the ones that make them look best?

Meta's local llama models used to be the face of open source AI. The scene has really changed.

Yeah, I think it is definitely great. Having said that, I am still debating in my mind whether the volume of software engineers needed in the AI era is going to increase or decrease because of all of these advancements.

On the one hand, because it is easy to build products, more and more people will build. And more and more products and features will be built. However, a lot of people who are non-technical will also try to build, but they get stuck, and then they will need engineers. The sheer volume of product built by both experienced technical companies and non-technical novice startups and founders and wannabe founders is going to be massive. That is the bull case for having more software engineers needed in the near future.

On the other hand, in a year or so, people will build all these products, and most of them won't be able to market them, sell them and make money. Eventually, there won't really be a need for that many software engineers.

I think overall the bull case is probably going to win net net.

To expand on Chinese models:

- DeepSeek

- GLM (Z.ai)

- Minimax

- Kimi (Moonshot)

- Hy3 (Tencent)

- Qwen (Alibaba)

(Each one of these with weights available to download and run locally)

While data centers are still using lots of energy created from fossil fuels and many still evaporate water for cooling?

No wonder we still can’t get climate change under control

Its the biggest technology race we have ever seen. Richest companies, smartest people, richest countries.

I do not know if competition is good, we will see in a few years.

Looking forward having a physical job for a change :D

Debugging and diagnosis is very tool call heavy, whether that's grepping / transforming logs, calling out to profilers/tracers, or even just writing up incident reports.

Bug diagnostics is about being okay at coding but better at tooling.

Given a good diagnostic report, it can be handed to opus for the fix.

Opus is okay at writing reports, but it still regularly gets table widths wrong in typst documents, leaving the last column full of text but only a handful of characters wide.

I wonder if we'll start to see that pattern with every new release. Tool use likely changes rapidly, so the newest, rather than most intelligent, model may always have an edge.

This sounds... kind of useless? Really good JSON or similar constrained decoder performance is interesting, but normal decoder > tool validator loop with good error message > tool retry is almost always able to get a tool to work second try, and input is cached so it's not expensive.

Meta isn’t right now on the radar for most folks picking models.

If they have a really good model, it makes sense to subsidise it, to gain users, before they align prices with competitors.

Cheaper than Qwen 3.7 Max. Second indication, after Grok 4.5 ($2 in / $6 out), that the BigLabs are feeling the GLM 5.2 heat.

It's a high quality benchmark for sure, but it being public means it's at risk of leaking into the models (unintentionally or not), right? For that reason I prefer to look at the private ones, like: HLE, SimpleBench, Kagi, ARC-AGI.

That's what one does when its product and public perception is way behind competitors.

Me neither, though LLMs also provide services that don’t involve personal or sensitive data

This is not open-weights, right?

Meta is back in the game, albeit not at the top. Impressive stuff, nonetheless.

Weren't they caught multiple times gaming the benchmark even more so then the rest?

They are not open source anymore, right?

Debugging and diagnosis is very tool call heavy, whether that's grepping / transforming logs, calling out to profilers/tracers, or even just writing up incident reports.

Bug diagnostics is about being okay at coding but better at tooling.

Given a good diagnostic report, it can be handed to opus for the fix.

Opus is okay at writing reports, but it still regularly gets table widths wrong in typst documents, leaving the last column full of text but only a handful of characters wide.

I wonder if we'll start to see that pattern with every new release. Tool use likely changes rapidly, so the newest, rather than most intelligent, model may always have an edge.

Me neither, though LLMs also provide services that don’t involve personal or sensitive data

That's what one does when its product and public perception is way behind competitors.

They are not open source anymore, right?

Is this the model trained on Meta "draftees"? Are we seeing this in the jump on JobBench?

Meta's local llama models used to be the face of open source AI. The scene has really changed.

they likely got the Peter Theil newsletter proclaiming open source models are the antichrist

I think overall the bull case is probably going to win net net.

I see some similarities to 3D printing here. It’s great that everyone can make their own toothbrush holder (or whatever) but I’m probably not going to pay for someone’s weekend project.

I’m “seeing” more devs stepping into the SendCutSend stage where they’re cleaning up/fixing/productizing vibe coded projects so maybe there will be some new demand in that space?

> On the one hand, because it is easy to build products, more and more people will build.

And those people won't need to be software engineers.

> but they get stuck, and then they will need engineers

You've implicitly assumed here that the AI systems will always be worse than the average engineer. That is IMO myopic. I'm not sure that it's even true now let alone in the nebulous future.

At least in China a lot of software developers are now struggling.

I think for a lot of type of software we have now reached peak employment.

Someone payed a few k just for a normal website.

To expand on Chinese models:

- DeepSeek

- GLM (Z.ai)

- Minimax

- Kimi (Moonshot)

- Hy3 (Tencent)

- Qwen (Alibaba)

(Each one of these with weights available to download and run locally)

GLM 5.2 is great, but is so rate limited now I no longer recommend it

While data centers are still using lots of energy created from fossil fuels and many still evaporate water for cooling?

No wonder we still can’t get climate change under control

> No wonder we still can’t get climate change under control

This is was historically a money issue, being green used to be wildly more expensive.

Now being green is cheaper, the limiting factor is how fast PV and batteries can be made or imported.

Recent reports of the sum of all US data centres currently in planning, has a power demand exceeding the (capacity-factor-adjusted!) global annual supply of PV.

This would be less of a problem if Trump wasn't trying to get in the way of anything green, or if the companies building data centres decided to also support factories to make more PV.

* Planned new demand: 300 GW; PV factory capacity ~ 600 GW nameplate, but the capacity factor is 14% so that's really 84 GW on average.

Its the biggest technology race we have ever seen. Richest companies, smartest people, richest countries.

I do not know if competition is good, we will see in a few years.

Looking forward having a physical job for a change :D

A bit much describing our tech leadership as smartest people we've ever seen.

The avg coding session has hundreds or thousands of tool calls. Even a 5% failure rate noticeably notches up token use and cost. See Gemini.

Meta isn’t right now on the radar for most folks picking models.

If they have a really good model, it makes sense to subsidise it, to gain users, before they align prices with competitors.

this is not subsidizing. this is way too expensive for a no-name model.

Cheaper than Qwen 3.7 Max. Second indication, after Grok 4.5 ($2 in / $6 out), that the BigLabs are feeling the GLM 5.2 heat.

Weren't they caught multiple times gaming the benchmark even more so then the rest?

Yes and Zuck effectively disbanded the entire team that did that. Not saying we shouldn't cast a critical eye on it, but it probably does warrant a second chance.

Let me assure you, literally everybody does this

they likely got the Peter Theil newsletter proclaiming open source models are the antichrist

> No wonder we still can’t get climate change under control

This is was historically a money issue, being green used to be wildly more expensive.

Now being green is cheaper, the limiting factor is how fast PV and batteries can be made or imported.

Recent reports of the sum of all US data centres currently in planning, has a power demand exceeding the (capacity-factor-adjusted!) global annual supply of PV.

This would be less of a problem if Trump wasn't trying to get in the way of anything green, or if the companies building data centres decided to also support factories to make more PV.

* Planned new demand: 300 GW; PV factory capacity ~ 600 GW nameplate, but the capacity factor is 14% so that's really 84 GW on average.

this is not subsidizing. this is way too expensive for a no-name model.

Yes and Zuck effectively disbanded the entire team that did that. Not saying we shouldn't cast a critical eye on it, but it probably does warrant a second chance.

Zuck was part of that team.

Let me assure you, literally everybody does this

> On the one hand, because it is easy to build products, more and more people will build.

And those people won't need to be software engineers.

> but they get stuck, and then they will need engineers

You've implicitly assumed here that the AI systems will always be worse than the average engineer. That is IMO myopic. I'm not sure that it's even true now let alone in the nebulous future.

> And those people won't need to be software engineers....You've implicitly assumed here that the AI systems will always be worse than the average engineer.

Most of what we do as engineers is precisely describe or analyze the behavior we want or the behavior we don't want. All other engineering skills that are useful are ultimately downstream from understanding the behavior of software enough to know which parts to keep, improve, or jettison. Chatbots can take care, somewhat, of analysis or expansion of instructions.... but they can't read minds. I don't see that changing any time soon.

At least in China a lot of software developers are now struggling.

I think for a lot of type of software we have now reached peak employment.

Someone payed a few k just for a normal website.

> At least in China a lot of software developers are now struggling.

Do you think that Chinese software industry is that relevant to the kind of software market talked about on HN? I.e. lots of enterprise b2b and infra companies.

Chinese companies have always had a very low willingness to pay for software which kinda breaks the flywheel of B2B SaaS companies and companies to service those companies all the way down.

GLM 5.2 is great, but is so rate limited now I no longer recommend it

Aren't there multiple providers for it? is it rate limited in all providers?

I'm looking ahead to the next wave of open-weight models that are as efficient as DSv4 (which is really efficient), and have been heavily distilled on GLM 5.2 (which is trivial, given it is open weight)

A bit much describing our tech leadership as smartest people we've ever seen.

I would call the founders of DeepMind (Demis Hassabis, Mustafa Suleyman, Shane Legg) very smart people. Im pretty sure with the amount of funding everyone of these companies have, they have a long list of very smart researchers in their companies.

I do not mean Suckerberg or Eric Schmidt.

The avg coding session has hundreds or thousands of tool calls. Even a 5% failure rate noticeably notches up token use and cost. See Gemini.

Yes, but each tool call has a different failure %. The tool calls that make up the majority of volume like grep are going to have nowhere near a 5% failure. A custom user-defined skill having a 5% failure rate is probably fine.

Aren't there multiple providers for it? is it rate limited in all providers?

I do not mean Suckerberg or Eric Schmidt.

Zuck was part of that team.

> And those people won't need to be software engineers....You've implicitly assumed here that the AI systems will always be worse than the average engineer.

> but they can't read minds

I don't know who needs to hear this, but neither can humans.

You've implicitly assumed here that AI systems will always be worse at contextualizing and framing questions than the average engineer. I'm not sure that it's even true now let alone in the nebulous future.

You haven't narrowed the fundamental myopia of the assumption here, just dressed it in slightly different clothing.

I see some similarities to 3D printing here. It’s great that everyone can make their own toothbrush holder (or whatever) but I’m probably not going to pay for someone’s weekend project.

I’m “seeing” more devs stepping into the SendCutSend stage where they’re cleaning up/fixing/productizing vibe coded projects so maybe there will be some new demand in that space?

3D printing is a good comparison - it allows almost anyone to make things, but in the end very few do.

Another example is when the WWW first became available, and suddenly everyone COULD be a publisher (browsers even included built-in HTML editors), and for a while MySpace pages proliferated until the excitement died down and people went back to being media consumers.

I expect we'll see the same thing with consumer use of generative AI. Suddendly everyone is generating 3-D worlds/games with Fable because they can, but I expect that just as with the web the novelty will wear off and they'll leave it up to the pros.

Professional use of GenAI, and coding in particular, is certainly here to stay, but it seems we're still in the early experimental/hype phase. At least tokenmaxxing has passed, and it seems most companies are now paying attention to, and limiting, how much they are spending, but it doesn't seem we've yet progressed to the stage where companies are paying attention to what they are actually getting out of it - is the money spent showing up on the bottom line in the form of increased revenues.

A comparison I find useful here is Excel (and spreadsheets in general). Those enabled huge numbers of non-programmers to build software-like things, while the demand for expert developers grew enormously at the same time.

I'm hoping vibe-coding plays out the same way.

It’s terrible and depressing work to take vibe coded garbage and make it a real product. There will be demand, but good engineers won’t want to touch it. And people paying will think they did the hard work so why pay a good rate?

> At least in China a lot of software developers are now struggling.

Do you think that Chinese software industry is that relevant to the kind of software market talked about on HN? I.e. lots of enterprise b2b and infra companies.

Chinese companies have always had a very low willingness to pay for software which kinda breaks the flywheel of B2B SaaS companies and companies to service those companies all the way down.

Its a signal. They were earning well and AI crashed the market in China.

> Chinese companies have always had a very low willingness to pay for software

Are we still left with this mindset? Maybe once upon a time but it has definitely been changing.

There's plenty of B2B and enterprise SaaS companies in China serving the Chinese market. Maybe not as many, but no longer the very low of the past.

I also would not say enterprise were not willing to pay, even many years ago. It's the SME that refused to pay. Large CRM, ERPs etc have always existed.

> but they can't read minds

I don't know who needs to hear this, but neither can humans.

You haven't narrowed the fundamental myopia of the assumption here, just dressed it in slightly different clothing.

Its a signal. They were earning well and AI crashed the market in China.

> You've implicitly assumed here that AI systems will always be worse at contextualizing and framing questions than the average engineer.

How would they know what to ask or contextualize if they don't know what the user wants?

> Chinese companies have always had a very low willingness to pay for software

Are we still left with this mindset? Maybe once upon a time but it has definitely been changing.

There's plenty of B2B and enterprise SaaS companies in China serving the Chinese market. Maybe not as many, but no longer the very low of the past.

I also would not say enterprise were not willing to pay, even many years ago. It's the SME that refused to pay. Large CRM, ERPs etc have always existed.

> You've implicitly assumed here that AI systems will always be worse at contextualizing and framing questions than the average engineer.

How would they know what to ask or contextualize if they don't know what the user wants?

By asking the user to explain what they want whenever there's ambiguity.

Plus all the other things that software engineers generally have not learned to a professional level even if they picked up the basics on the job by osmosis, because figuring out the customer's needs (and what they'll pay you for which may be different) is the job of a business analyst, a PM, or a UX researcher, and those are different skills and two of them may come with a Business Informatics degree rather than a CompSci one.

Are you suggesting that psychic mindreading powers are real?

> How would they know

How would you? The answer is the same.

By asking the user to explain what they want whenever there's ambiguity.

Are you suggesting that psychic mindreading powers are real?

> How would they know

How would you? The answer is the same.

I don't understand what you mean. I can't build software I can't describe.

If you're implying chatbots can ask their "client" what to build, good luck with that—contractors are at least liable for what they produce and have extreme incentives to ensure that their clients are happy. To the extent of refusing to build anything if they don't know what they want....

I don't understand what you mean. I can't build software I can't describe.

3D printing is a good comparison - it allows almost anyone to make things, but in the end very few do.

I'm hoping vibe-coding plays out the same way.

Today, we’re excited to introduce Muse Spark 1.1, the latest model from Meta Superintelligence Labs and a significant upgrade from Muse Spark. Muse Spark 1.1 is a multimodal reasoning model built for agentic tasks, with major gains in tool and computer use, coding, and multimodal understanding.

With these improvements, Muse Spark 1.1 advances the performance-efficiency frontier. Together with this week’s launch of Muse Image, this release brings us closer to our vision of personal superintelligence: models that help you pursue your goals, create what you imagine, deepen your relationships, and take action on what you value most.

Along with this release, we are launching a public preview of the new Meta Model API where developers can access Muse Spark 1.1. The model is available now in "Thinking" mode in the Meta AI app and on meta.ai.

Evaluations

Agents

Muse Spark 1.1 delivers exceptional performance in personal agentic tasks that require planning and orchestration across a range of external apps and services. It zero-shot generalizes to new native tools, MCP servers, and custom skills.

Text-to-image arena leaderboard chart

Single-image edit arena leaderboard chart

Multi-image edit arena leaderboard chart

It tackles complex projects significantly faster than Muse Spark, as it is trained to orchestrate multi-agent systems to optimize end-to-end latency. As the main agent, it can gather context, make a plan, and delegate execution across parallel subagents. As a subagent, it adheres to its job, understands available tools, and knows when to escalate back to the main agent.

Muse Spark 1.1 can actively manage its context window of 1 million tokens. It remembers actions, retrieves information from much earlier work, and compacts in a way that keeps the critical steps needed for later work.

Inference-time compute scaling chart for Muse Image

Computer Use

Inference-time compute scaling chart for Muse Image

Muse Spark 1.1 excels at computer-use workflows that unfold across multiple applications with information changing on-the-fly. It maintains context across extended sessions, adapts to evolving requirements, and navigates unfamiliar interfaces with minimal human intervention.

Rather than reasoning through every desktop step one click at a time, Muse Spark 1.1 understands when to automate and when to use the interface directly. We trained the model to write scripts when automation is faster, click when direct interaction is simpler, and generate batches of actions at each step.

Agentic dinner party organization: In real-world applications, new context arises that changes the task. Muse Spark 1.1 notices these changes when placing the dinner order and makes necessary updates without user intervention.

Coding

Coding performance for Muse Spark 1.1 improved substantially on real-world tasks involving large, complex codebases. It can diagnose and fix complex bugs, implement new features in enterprise-grade systems, and execute large code migrations. In use cases like creating web applications and end-to-end question answering, Muse Spark 1.1 shows large gains over our first model.

Inference-time compute scaling chart for Muse Image

We trained our model to smoothly adapt to diverse harnesses and reliably handle complex multi-turn dynamics. Muse Spark 1.1 performs well with popular agentic coding setups, supporting common features like planning mode, goal conditioning, subagent delegation, and context compaction.

Debugging demo in OpenCode: Muse Spark 1.1 builds a chat web app, takes automated screenshots to identify user-visible failures, traces issues back to relevant code to implement fixes, and validates these changes. The model seamlessly combines coding, multimodal understanding, and tool calling.

Across Meta, developers and researchers are using Muse Spark 1.1 daily to build faster and work smarter. On our primary internal coding evaluation, Meta Internal Coding Bench, Muse Spark 1.1 significantly improves upon Muse Spark and is competitive with leading alternatives.

Inference-time compute scaling chart for Muse Image

Researchers are now also automating model development and evaluation tasks by leveraging Muse Spark 1.1 in their workflows.

DeepSWE evaluation in OpenCode: Muse Spark 1.1 evaluates itself on a subset of DeepSWE tasks across different reasoning strengths and produces an analysis dashboard based on the results.

Multimodal

Inference-time compute scaling chart for Muse Image

Along with coding and agentic capabilities, Muse Spark 1.1 excels in perception, multimodal reasoning, and tool use. It can interact with real environments and produce grounded outputs with strengths in visual-to-code artifact generation, ultra-descriptive image and video captioning, and agentic workflow execution for multimodal use cases.

Muse Spark 1.1’s multimodal capabilities are especially valuable when perception and action need to happen together. The model can inspect visual and audio, preserve details across a long workflow, and use those details while operating computers on the user’s behalf.

Facebook Marketplace agent: Using video shot from a smartphone, Muse Spark 1.1 extracts useful photos and reasons about the product to operate a user's browser and make a Facebook Marketplace listing on the user's behalf.

Safety

We conducted extensive safety evaluations before deployment, following the Advanced AI Scaling Framework, which defines evaluations, threat models, and deployment thresholds for our most advanced models.

Across all frontier risk categories — Chemical & Biological, Cybersecurity, and Loss of Control — our evaluations show Muse Spark 1.1 operates within safe margins. Muse Spark 1.1 demonstrates strong resistance to direct jailbreaks and indirect attacks from untrusted data, prompt injection, and developer-prompt attacks. Consequently, it shows better adversarial robustness, lower hallucination rates, and reduced sycophancy.

Our full safety posture for 1.1 is documented in our Muse Spark 1.1 Evaluation Report.

Availability

For the first time, developers can begin building with Muse Spark 1.1 via the new Meta Model API, now in public preview. Early partners of Muse Spark 1.1 praise the model as a complete agentic foundation, pairing long context handling with strong coding and reasoning capabilities to handle large-scale agentic workloads.

“What’s most impressive about Muse Spark is how much it packs into one model: massive million-token context, full multimodal support (images, video, PDFs), built-in search with citations, strong reasoning, top-tier coding abilities (particularly frontend and design), structured output, and parallel tool calling — all in a clean OpenAI-compatible package. A complete agentic foundation."

— Amjad Masad, CEO of Replit

“Meta is clearly building for serious agentic coding – strong tool use at a price point that makes it viable to run real coding workloads at scale. That combination is rare, and it’s exactly why we wanted Cline developers to have access early.”

— Saoud Rizwan, CEO of Cline

“When tested against Box’s enterprise work evaluation set, Muse Spark delivered enterprise capabilities competitive with today's leading frontier models. That level of intelligence, combined with its strengths in structured, procedural workflows across industries such as professional services, public sector, and industrial operations, makes it a compelling choice for organizations.”

— Yashodha Bhavnani, VP of AI Products at Box

We're thrilled to be releasing Muse Spark 1.1, a testament to our research momentum. We have even more capable models in training and look forward to sharing what’s to come.

Written by:

Meta Superintelligence Labs

Hacker Times

Hacker Times

Introducing Muse Spark 1.1

Discussion

Discussion