Muse Spark 1.1 vs GPT-5.6 Luna: Same Intelligence, Different Access (2026)

Muse Spark 1.1 and GPT-5.6 Luna both score 51 on the Artificial Analysis Intelligence Index (v4.1), so measured intelligence is a genuine tie. GPT-5.6 Luna is cheaper on input at $1 per million tokens versus $1.25, carries a slightly larger context of 1.05 million tokens versus 1 million, and is generally available worldwide. Muse Spark 1.1 is cheaper on output at $4.25 per million tokens versus $6, but it ships only as a closed, United States-only preview. When intelligence is level, access becomes the deciding factor, and Luna wins it.

Quick Verdict

Same intelligence, different access — and access decides it. This is the rare comparison where the two models are tied on the number most people reach for first. Muse Spark 1.1 from Meta Superintelligence Labs and GPT-5.6 Luna from OpenAI both post a 51 on the Artificial Analysis Intelligence Index (v4.1). When the intelligence line is flat, the duel moves to the things that usually get treated as footnotes: the exact price of input tokens versus output tokens, the size of the context window, and, most decisively, whether you can actually run the model at all. We ran GPT-5.6 Luna hands-on through the OpenAI API and compared it against the public, independent record for Muse Spark 1.1, which is still a closed preview.

• 🏆 Muse Spark 1.1 wins for: output-heavy generation, where its $4.25 per million output tokens undercuts Luna, and for teams inside the United States preview who want Meta's multimodal, agentic model at a low output rate.
• 🏆 GPT-5.6 Luna wins for: availability today anywhere in the world, cheaper input tokens, retrieval-heavy and long-context pipelines, and any team that needs to ship on a model it can reliably access.
• 💰 Cheaper depends on the workload: Luna is cheaper on input ($1 versus $1.25 per million tokens); Muse Spark 1.1 is cheaper on output ($4.25 versus $6 per million tokens). Your token mix decides which one is cheaper per task.
• ⚡ Available right now: GPT-5.6 Luna, in general availability through the OpenAI API worldwide. Muse Spark 1.1 is a closed, United States-only preview behind a waitlist.

Muse Spark 1.1 vs GPT-5.6 Luna: Overview

Both models sit in the same commercial niche: fast, low-cost, high-context models built for high-volume production work rather than for topping a leaderboard. They arrive at that niche from opposite directions. Muse Spark 1.1 is Meta Superintelligence Labs pushing a closed, agentic, multimodal model into a controlled preview. GPT-5.6 Luna is OpenAI's cheapest, most economical tier of the GPT-5.6 family, already shipping to everyone. The fact that they land on the same independent intelligence score makes the comparison unusually clean: for once, you can hold intelligence constant and look at everything else.

What Is Muse Spark 1.1?

Muse Spark 1.1 is a closed-weights model released by Meta Superintelligence Labs on July 9, 2026. It is multimodal and agentic, built to handle tool use and long, structured tasks, and it ships with a 1,000,000-token context window. On the Artificial Analysis Intelligence Index (v4.1) it scores 51. Artificial Analysis also reports component results for it, including 58 percent on SciCode and 45 percent on Humanity's Last Exam. Its API pricing is $1.25 per million input tokens and $4.25 per million output tokens — the low output rate is its headline economic feature. The catch is availability: at the time of writing, Muse Spark 1.1 is a preview limited to the United States and gated behind a waitlist, so most teams cannot yet build on it. You can read our full breakdown in the Muse Spark 1.1 review.

What Is GPT-5.6 Luna?

GPT-5.6 Luna is the fastest and most economical tier in OpenAI's GPT-5.6 family, positioned for high-volume, routine work rather than frontier reasoning. It scores 51 on the Artificial Analysis Intelligence Index (v4.1) — the same figure as Muse Spark 1.1. Luna's API pricing is $1 per million input tokens and $6 per million output tokens, and it carries a 1,050,000-token context window, marginally larger than Muse Spark 1.1's. Its defining advantage is distribution: Luna is generally available through the OpenAI API worldwide, with no waitlist and no regional gate. Our GPT-5.6 Luna review covers the wider family context and where Luna sits against its own siblings.

Feature Comparison: Muse Spark 1.1 vs GPT-5.6 Luna

The table below lines up the two models on the facts that actually differ. Note that the intelligence row is a tie, not a rounding-off of a small gap: both models are measured at 51 on the same independent index, in the same version.

Tally: GPT-5.6 Luna takes three rows (availability, input price, context), Muse Spark 1.1 takes one (output price), and three rows are ties (intelligence, weights, maker). On paper that reads as a Luna lead, but the single Muse win — output price — is the row that can flip the economics for the right workload, which is why the pricing section below matters more than the tally.

The Intelligence Tie, Explained

It is worth being precise about what "tied on intelligence" means here, because it is easy to misread. The 51 for each model comes from the Artificial Analysis Intelligence Index at version 4.1 — the same benchmark suite, the same version, run by the same independent evaluator. That is a real equality, not a marketing rounding of a two-point gap. We are not aware of any independent number that separates the two models on aggregate intelligence at the time of writing.

What a matching aggregate score does not tell you is that the two models are interchangeable on every task. Artificial Analysis reports component results for Muse Spark 1.1, including 58 percent on SciCode and 45 percent on Humanity's Last Exam, which give a sense of its coding and hard-reasoning profile. We deliberately do not line those up against a single Luna number, because pairing one independent component score against a different one invites a false-precision comparison. The honest reading is narrower and more useful: on the headline independent measure of aggregate intelligence, these two models are level, so you should choose on the things that are not level — price mix, context, and access.

Pricing — Muse Spark 1.1 vs GPT-5.6 Luna in 2026

Both models use flat, per-token API pricing with no context-length tiers. The twist is that they split the two price lines: Luna is cheaper to feed, Muse Spark 1.1 is cheaper to generate. That single fact is the whole pricing story, and it means the "cheaper" label depends entirely on how many tokens you read in versus write out.

Muse Spark 1.1 Pricing

Muse Spark 1.1's output rate of $4.25 per million tokens is its standout number. It is about 29 percent cheaper than Luna's output rate, which is meaningful for any workload that generates far more text than it reads. The trade is a slightly higher input rate and, more importantly, an access gate we cover below.

GPT-5.6 Luna Pricing

Luna's $1 per million input tokens is the lowest input rate in this matchup, 20 percent under Muse Spark 1.1. That makes Luna the natural pick for input-dominated work — retrieval-augmented generation, long-document analysis, classification, and any pipeline that stuffs large prompts in and asks for short answers out.

Blended Cost — Where Each Model Wins

Because the two models invert the price lines, the only honest way to compare cost is by workload shape. The table below shows which model is cheaper for three common token mixes, holding volume constant.

Verdict on pricing: there is no single cheaper model. If you generate a lot of text — long-form drafting, code generation, synthetic data — Muse Spark 1.1's output rate wins on paper. If you read a lot and write a little — retrieval, extraction, classification — Luna's input rate wins. For a genuinely balanced mix the two are close enough that price should not be the tiebreaker; access and context should be. And access is exactly where the comparison stops being symmetric.

How We Compared Them (and What We Could Not Test)

Methodology and the Access Asymmetry

We owe you a plain disclosure, because it shapes how much weight to put on each half of this comparison. We ran GPT-5.6 Luna hands-on through the OpenAI API: it is generally available, so we could put it through real prompts and observe its behavior directly. Muse Spark 1.1 is a different situation. At the time of writing it is a closed preview, limited to the United States and gated behind a waitlist, so our read on it is research-led rather than hands-on. That read is built from Meta's published model documentation and from the independent benchmarks that Artificial Analysis has run on it, not from our own sustained production use.

That asymmetry is not a footnote — it is arguably the headline. When two models are tied on measured intelligence, the practical question is not "which is smarter" but "which can I actually deploy," and only one of them answers that question with a yes for teams outside a narrow preview. We have kept every factual claim about Muse Spark 1.1 anchored to its published numbers and avoided any experiential claim we could not stand behind.

Measured Intelligence: A Genuine Tie

On aggregate intelligence, the independent record puts these two models level at 51 on the Artificial Analysis Intelligence Index (v4.1). Nothing in Luna's hands-on behavior contradicts that placement for the kind of routine, high-volume work both models target — it is quick, coherent, and comfortable with long context. We cannot report the same first-hand impression for Muse Spark 1.1, but its independent score sits exactly where Luna's does, and its published component results (58 percent on SciCode, 45 percent on Humanity's Last Exam, per Artificial Analysis) suggest a capable, coding-and-reasoning-oriented profile. Treat intelligence as a wash and spend your decision budget elsewhere.

Price Behavior: Input vs Output

This is the most consequential difference after access. The two models do not simply differ in price; they invert it. Luna reads for less ($1 versus $1.25 per million input tokens) and writes for more ($6 versus $4.25 per million output tokens). In practice this means your architecture, not the sticker price, decides your bill. A summarization or extraction service that pushes long documents in and returns short answers will run cheaper on Luna. A drafting or code-generation service that emits far more than it ingests will run cheaper on Muse Spark 1.1. If you have not measured your own input-to-output ratio, that is the first number to pull before choosing.

Context Window: A Narrow Luna Edge

Both models are firmly in million-token territory, so for the overwhelming majority of prompts this row is a non-issue. Luna's 1,050,000-token window is 50,000 tokens larger than Muse Spark 1.1's 1,000,000, a roughly 5 percent edge. That margin only matters at the extreme tail — packing an entire large codebase or a very long document set into a single call — and even then it is a small buffer, not a category difference. We score it a narrow Luna win because more headroom is strictly better, but it should rarely be a deciding factor on its own.

Access: The Real Divider

Everything above is decided by the fact underneath it: you can call GPT-5.6 Luna today, from anywhere, through the OpenAI API, and you probably cannot call Muse Spark 1.1 at all unless you are inside its United States preview. A model you cannot access has an effective cost of infinity, no matter how good its output rate looks. This is why, despite the price lines splitting and the intelligence scores tying, our overall lean is toward Luna: availability is not a feature you can bolt on later, and for most readers it is the difference between shipping and waiting.

What Would Change This Verdict

We hold verdicts loosely when a preview is involved, and this one has a clear trigger. If Meta widens Muse Spark 1.1 beyond its United States preview to general, worldwide availability, the access advantage that carries Luna's win evaporates, and the comparison collapses back onto price mix and context — where the two are genuinely close and output-heavy teams would tilt to Muse Spark 1.1. Two other things could move it. If either vendor changes its per-token rates, the input-versus-output split that defines this matchup could widen or close; pricing is the most volatile variable here. And if a future revision of the Artificial Analysis Intelligence Index separates the two models — remember that a score only means something paired with its version — the intelligence tie that frames this entire piece would no longer hold. Until any of that happens, the read stands: tied on measured intelligence, split on price, and decided by who you can actually deploy today.

Winner per Category

🏆 Best Overall (for this niche): GPT-5.6 Luna, narrowly

With intelligence tied, Luna wins the overall call on the strength of availability, a cheaper input rate, and a marginally larger context. The margin is genuinely narrow, and it rests more on the fact that you can deploy Luna than on any capability gap. If Muse Spark 1.1 opens up broadly and your workload is output-heavy, revisit this — the verdict is a lean, not a landslide.

Best for Output-Heavy Generation: Muse Spark 1.1

For workloads that write far more than they read — long-form content, code generation, synthetic data — Muse Spark 1.1's $4.25 per million output tokens is the best rate in this matchup, roughly 29 percent under Luna. If you are inside the preview and generation-dominated, this is your model.

Best for Input-Heavy and RAG Workloads: GPT-5.6 Luna

Retrieval-augmented generation, long-document analysis, and classification all push large prompts in and short answers out. Luna's $1 per million input tokens is the cheapest input rate here, so input-dominated pipelines run cheaper on it, on top of being deployable today.

Best for Availability Today: GPT-5.6 Luna

This one is not close. Luna is generally available worldwide through the OpenAI API. Muse Spark 1.1 is a United States-only preview behind a waitlist. If you need to build now, and especially if you are outside the United States, Luna is the only one of the two you can actually use.

Best for the Longest Context: GPT-5.6 Luna

Luna's 1,050,000-token window edges Muse Spark 1.1's 1,000,000. Both are enormous, so this matters only at the extreme tail, but when it does, the extra 50,000 tokens of headroom go to Luna.

Best for Raw Intelligence: A Tie

Neither model wins this. Both sit at 51 on the Artificial Analysis Intelligence Index (v4.1). If your decision is driven purely by aggregate intelligence, this comparison gives you no reason to prefer one over the other — which is precisely why the other rows carry the verdict.

Pros and Cons

Muse Spark 1.1 Pros and Cons

What stands out about Muse Spark 1.1

• Cheapest output rate in this matchup. At $4.25 per million output tokens, it undercuts Luna by roughly 29 percent, which is decisive for generation-heavy work.
• Tied on independent intelligence. A 51 on the Artificial Analysis Intelligence Index (v4.1) puts it exactly level with Luna, so you give up nothing on measured aggregate capability.
• Multimodal and agentic by design. Meta positions it for tool use and structured, multi-step tasks, with a full 1,000,000-token context to work in.
• Backed by Meta Superintelligence Labs. It carries the research weight and roadmap of a major lab, which matters for longer-term bets if the preview opens up.

Where Muse Spark 1.1 falls short

• Closed, United States-only preview. Most teams cannot use it at all yet, and teams outside the United States are excluded entirely by the current gate.
• Higher input rate. At $1.25 per million input tokens it is 25 percent more expensive than Luna to feed, which hurts on retrieval and long-prompt workloads.
• Slightly smaller context. Its 1,000,000-token window is marginally under Luna's, a minor but real disadvantage at the extreme tail.
• Closed weights. There is no self-hosting path, so you are tied to Meta's endpoint and terms once the preview widens.

GPT-5.6 Luna Pros and Cons

What stands out about GPT-5.6 Luna

• Available worldwide right now. General availability through the OpenAI API, with no waitlist and no regional gate, means you can ship on it today.
• Cheapest input rate here. At $1 per million input tokens it beats Muse Spark 1.1 by 20 percent, ideal for input-dominated pipelines.
• Largest context in the matchup. Its 1,050,000-token window gives a little extra headroom over Muse Spark 1.1 for the biggest prompts.
• Tied on independent intelligence. A 51 on the Artificial Analysis Intelligence Index (v4.1) matches Muse Spark 1.1, so its availability advantage does not come at the cost of measured capability.

Where GPT-5.6 Luna falls short

• Most expensive output rate here. At $6 per million output tokens it is about 41 percent above Muse Spark 1.1, which stings on generation-heavy work.
• Economy tier, not a frontier model. Luna is OpenAI's fast, cheap tier; if you need peak reasoning you would step up within the GPT-5.6 family, not reach for Luna.
• Closed and proprietary. Like Muse Spark 1.1, there is no self-hosting option, so you are on OpenAI's endpoint and terms.
• No pricing edge on balanced workloads. Its input win and output loss cancel out for even token mixes, so it does not win on cost unless your work is input-heavy.

When to Pick Each Model

When to Pick Muse Spark 1.1

Pick Muse Spark 1.1 if you are inside its United States preview and your workload is generation-dominated — long-form drafting, bulk code generation, or synthetic data — where its $4.25 per million output tokens turns into real savings at volume. It is also the more natural fit if you want a multimodal, agentic model from Meta and you are comfortable betting on a preview that may widen. What you must not do is plan a production launch around a model you cannot yet reliably access; treat Muse Spark 1.1 as a strong option for the day the gate opens, and for output-heavy teams already inside it.

When to Pick GPT-5.6 Luna

Pick GPT-5.6 Luna if you need to ship now, if you are anywhere outside the United States preview, or if your workload reads more than it writes. Its $1 per million input tokens makes it the cost winner for retrieval, extraction, and classification, and its worldwide general availability makes it the only deployable choice of the two for most teams. For a startup that needs a cheap, capable, long-context model on tap this week, Luna is the safe default — you can always revisit Muse Spark 1.1 for output-heavy jobs once it opens up. If you are weighing this against other budget tiers, our roundup of the best AI coding tools of 2026 puts both in wider context, and our GPT-5.6 Luna vs Claude Sonnet 5 comparison covers Luna against a different rival. For how Luna sits inside its own lineup, see GPT-5.6 Sol vs GPT-5.6 Terra.

Frequently Asked Questions

Is Muse Spark 1.1 better than GPT-5.6 Luna in 2026?

Neither is clearly better overall, because they tie on measured intelligence at 51 on the Artificial Analysis Intelligence Index (v4.1). GPT-5.6 Luna wins the practical call for most teams because it is generally available worldwide, cheaper on input at $1 per million tokens, and carries a marginally larger context. Muse Spark 1.1 wins on output price at $4.25 per million tokens, but it is a closed, United States-only preview, so most teams cannot use it yet. If you can access both and your work is output-heavy, Muse Spark 1.1 can be the better value; otherwise Luna is the safer pick.

Do Muse Spark 1.1 and GPT-5.6 Luna have the same intelligence?

On the headline independent measure, yes. Both models score 51 on the Artificial Analysis Intelligence Index at version 4.1, run by the same independent evaluator. That is a genuine tie on aggregate intelligence, not a rounding of a small gap. A matching aggregate score does not guarantee identical results on every task, but it does mean you should not choose between them on intelligence alone.

Which is cheaper, Muse Spark 1.1 or GPT-5.6 Luna?

It depends on your token mix, because the two models invert the price lines. GPT-5.6 Luna is cheaper on input at $1 per million tokens versus $1.25 for Muse Spark 1.1. Muse Spark 1.1 is cheaper on output at $4.25 per million tokens versus $6 for Luna. Input-heavy workloads such as retrieval run cheaper on Luna; output-heavy workloads such as long-form generation run cheaper on Muse Spark 1.1. For a balanced mix the two are close.

Can I use Muse Spark 1.1 right now?

Only if you are inside its preview. At the time of writing, Muse Spark 1.1 is a closed preview limited to the United States and gated behind a waitlist, so most teams cannot access it yet, and teams outside the United States are excluded by the current gate. GPT-5.6 Luna, by contrast, is generally available worldwide through the OpenAI API with no waitlist.

What is the context window of Muse Spark 1.1 versus GPT-5.6 Luna?

Muse Spark 1.1 has a 1,000,000-token context window. GPT-5.6 Luna has a 1,050,000-token window, about 50,000 tokens larger. Both are firmly in million-token territory, so the difference matters only for the very largest prompts, such as packing an entire large codebase or document set into a single call. For everyday work the two are effectively equivalent on context.

Which model is better for output-heavy generation?

Muse Spark 1.1, on price. Its output rate of $4.25 per million tokens is about 29 percent cheaper than GPT-5.6 Luna's $6 per million tokens, so any workload that writes far more than it reads — long-form drafting, code generation, synthetic data — costs less on Muse Spark 1.1 at volume. The caveat is access: you can only take that saving if you are inside its United States preview.

Which model is better for input-heavy or RAG workloads?

GPT-5.6 Luna. Retrieval-augmented generation, long-document analysis, and classification push large prompts in and short answers out, so input price dominates the bill. Luna's $1 per million input tokens is the cheapest input rate in this matchup, 20 percent under Muse Spark 1.1, and Luna is also available worldwide today, which makes it the practical choice for these pipelines.

Is Muse Spark 1.1 open source?

No. Muse Spark 1.1 ships with closed weights, so there is no self-hosting path; you access it through Meta's endpoint. GPT-5.6 Luna is also closed and proprietary, available only through the OpenAI API. Neither model in this comparison is open-weight, so if self-hosting is a requirement, you would need to look outside this pairing.

Who makes Muse Spark 1.1 and GPT-5.6 Luna?

Muse Spark 1.1 is made by Meta Superintelligence Labs and was released on July 9, 2026. GPT-5.6 Luna is made by OpenAI as the fastest, most economical tier of its GPT-5.6 family. Both are commercial, closed models aimed at high-volume, low-cost production work rather than at topping frontier leaderboards.

Does the Artificial Analysis Intelligence Index of 51 mean they perform identically?

No. A tied aggregate score of 51 means the two models are level on the overall independent measure of intelligence, but the index is a composite. Individual tasks — coding, hard reasoning, long-context recall — can still favor one model or the other. The tie is a strong signal that neither is broadly smarter, so you should decide on price mix, context, and access rather than expecting identical behavior on every prompt.

Which model should a startup outside the United States choose?

GPT-5.6 Luna, without much debate. Muse Spark 1.1's preview is limited to the United States, so a startup elsewhere cannot access it at the time of writing. Luna is generally available worldwide through the OpenAI API, ties Muse Spark 1.1 on measured intelligence, and is cheaper on input, which suits most early-stage workloads. Revisit Muse Spark 1.1 only if and when it opens up in your region.

What is the final verdict on Muse Spark 1.1 versus GPT-5.6 Luna?

With intelligence tied at 51 and the price lines split, GPT-5.6 Luna takes a narrow overall win on availability, a cheaper input rate, and a slightly larger context. Muse Spark 1.1 is the better value only for output-heavy teams already inside its United States preview, thanks to its lower output rate. For everyone else — especially teams outside the United States or with input-heavy workloads — Luna is the deployable, safer default today. The verdict is a lean, not a landslide, and it could shift if Muse Spark 1.1 opens up broadly.

Final Verdict: Same Intelligence, Luna Wins on Access

This comparison is a useful reminder that the first number people quote — an intelligence score — is often the one that decides the least. Muse Spark 1.1 and GPT-5.6 Luna both measure 51 on the Artificial Analysis Intelligence Index (v4.1), so on the metric that usually settles these arguments, they are level. The real separation is elsewhere: Luna reads for less and writes for more, Muse Spark 1.1 reads for more and writes for less, and only one of them is available to you today. Because availability is a precondition for everything else, GPT-5.6 Luna takes the overall verdict for most readers, with the explicit carve-out that output-heavy teams inside Muse Spark 1.1's preview have a real reason to prefer it.

• Measured intelligence: tie — both at 51 on the Artificial Analysis Intelligence Index (v4.1).
• Input price: GPT-5.6 Luna, at $1 per million tokens versus $1.25.
• Output price: Muse Spark 1.1, at $4.25 per million tokens versus $6.
• Context window: GPT-5.6 Luna, at 1,050,000 tokens versus 1,000,000.
• Availability: GPT-5.6 Luna, generally available worldwide versus a United States-only preview.
• Overall: GPT-5.6 Luna, narrowly, on access — with Muse Spark 1.1 the value pick for output-heavy teams already inside the preview.

Final word: when two models tie on intelligence, buy the one you can actually run. For most teams that is GPT-5.6 Luna today. Keep Muse Spark 1.1 on your shortlist for output-heavy work and watch for the day its preview opens — if it does, this becomes a much closer call.