Muse Wonder vs GPT-6 Astra: The Model With the 2.5x Price Tag Is the Cheaper One

GPT-6 Astra charges $10.00 per million input tokens and $50.00 per million output tokens. Meta Muse Wonder charges $4.00 and $20.00. On the two documents that matter most to a procurement decision — the rate card and the benchmark table — the model costing two and a half times as much wins, and it wins by a margin that is not close: on Artificial Analysis's own harness, GPT-6 Astra (max) finishes an Intelligence Index task for about $3.26 while Meta Muse Wonder (max) finishes the same task for about $4.52, while also scoring a point higher. Meta Superintelligence Labs shipped Muse Wonder on October 12, 2026 at a million-token window and mandatory reasoning. The vendor shipped GPT-6 Astra in early September 2026 with 1,050,000 tokens of context and 128,000 tokens of maximum output. If you are choosing between them on price per token, you are reading the wrong number.

Two rate cards, and what they predict

Here is what each vendor publishes, side by side, before anything measured enters the picture.

• Input — GPT-6 Astra $10.00 per million tokens; Muse Wonder $4.00. Astra is 2.5x.

• Output — GPT-6 Astra $50.00 per million tokens; Muse Wonder $20.00. Also 2.5x.

• Cached input — GPT-6 Astra $1.00 per million; Muse Wonder $0.20. Astra is 5x on the line that dominates repeated-prefix agentic traffic.

• The second tier — requests above 272,000 prompt tokens bill at $20.00 input and $75.00 output. Muse Wonder has no published tier; the $4.00 and $20.00 apply across the window.

• Context window — GPT-6 Astra 1,050,000 tokens; Muse Wonder 1,048,576. Call it identical.

• Maximum output — GPT-6 Astra 128,000 tokens; Muse Wonder documents 943,718 against its 1,048,576-token window.

• Input surface — GPT-6 Astra takes text, images and files; Muse Wonder advertises text, image, speech and video.

• Weights — neither is open. Both are proprietary, both are closed to self-hosting.

Every line of that points one way. Astra is a premium-priced model and Muse Wonder is a mid-priced one, and the 272,000-token tier makes the gap steeper still for anyone working at long context — a 400,000-token prompt bills at $20.00 per million on Astra, five times Muse Wonder's input rate. There is no reading of the two rate cards under which Muse Wonder is not the cheaper model.

What a completed job costs, which is the other number

Artificial Analysis publishes both the token volume each model burns on its Intelligence Index and the resulting cost per task. That pair is where this comparison inverts.

• Cost per Intelligence Index task — GPT-6 Astra (max) about $3.26; Muse Wonder (max) about $4.52

• Output tokens consumed on that run — GPT-6 Astra about 60M; Muse Wonder about 130M, against a board median near 85M

• The evaluator's own wording — GPT-6 Astra is described as "fairly concise"; Muse Wonder as "somewhat verbose"

• Intelligence Index — GPT-6 Astra (max) 53; Muse Wonder (max) 52

• Speed on the same run — GPT-6 Astra about 66 tokens per second; Muse Wonder not documented by the evaluator in this pairing

This is the whole argument in five lines. Astra emits roughly 60 million output tokens to finish a task that costs Muse Wonder 130 million, a ratio of better than two to one, and that ratio is more than enough to absorb a 2.5x difference in the output price. Put differently: the price per token is a property of the vendor's marketing, and the tokens per task is a property of how the model is trained to behave. The second one is doing the work here.

That is also the reason this pairing reads differently from the usual "expensive model is expensive" story. Astra is not a budget model by any measure — it is the most expensive listing on our own catalogue among the current GPT-6 flagships, and its input rate sits at five times the board median. It is a model whose cost profile has been engineered toward deliberation rather than volume: fewer tokens out, higher price per token, lower bill per completed job. The word the evaluator uses is "concise," and it is the single most financially relevant thing about the model.

Where Astra's advantage stops

The gap is real but it is not uniform, and three qualifiers belong next to it.

First, the index is a composite of ten evaluations, and token appetite varies by task. A workload dominated by long-form generation — drafting, documentation, code with heavy commentary — is one where Muse Wonder's 943,718-token output ceiling is worth having and where Astra's concision is not an advantage so much as a limit; 128,000 tokens of maximum output is a hard stop, and it is a smaller one than most people assume when they read "1.05M context window" and mentally extend it to the response.

Second, the second pricing tier is a trap for exactly the workloads that look most attractive. Deep research over a 300,000-token corpus, a repo-wide review, a full contract set — these are Astra's stated strengths and they are also the requests that cross 272,000 prompt tokens and reprice the whole request at $20.00 in and $75.00 out. Muse Wonder's flat $4.00 and $20.00 across the window means a long-context agent that re-sends a large prefix pays a predictable rate on both sides, and its $0.20 cache read against Astra's $1.00 matters most in precisely that shape, where the same context is replayed on every turn.

Third, the benchmark gap is one point on a composite whose field median is 27, and it comes from a vendor-versus-independent split worth naming. On the GPQA Diamond question set, our own catalogue lists GPT-6 Astra at 96.1 — by some distance the highest number it carries — while Artificial Analysis puts its Humanity's Last Exam result level with Claude Opus 5's and its Terminal-Bench 4.0 score at 59.1 against Muse Wonder's 85.6. Meta's own table, meanwhile, is vendor-reported in Meta's harness and unreproduced, which is why Meta prints Muse Wonder Full at 59.0 on the index where the evaluator itself publishes 56. Treat every one of those as somebody's claim about their own model, and treat the $3.26 and $4.52 as the only figures in this article that were produced the same way for both.

Which of the two to reach for

The decision rule that falls out of the numbers is narrower than "pick the cheaper one."

• Choose GPT-6 Astra when the job is analysis, reasoning and professional deliverables where the model finishes in fewer tokens than the alternative — its measured cost advantage comes entirely from concision, so a workload that forces long output gives it up.

• Choose Muse Wonder when the input is speech or video, which Astra does not accept at all, or when a single response needs to run past 128,000 tokens.

• Choose Muse Wonder when the workload is long-context and cache-heavy on both sides of the ledger, because $0.20 against $1.00 cached and a flat rate above 272,000 tokens reverse part of the per-task gap.

• Choose neither if the workload is high-volume and text-only; a cheaper open-weight model will do the same work for a fraction of either rate card, and the seven-to-eight index points you give up are invisible on tasks both models complete.

Both are one key away, and that is the practical part

GPT-6 Astra is on OrcaRouter, so the comparison above is something you can run rather than read. It is listed at vendor list rates by default — $10.00 in and $50.00 out with a $1.00 cache read — with the 272,000-token tier belt carried through faithfully, because a pass-through that ignored the second tier would produce a bill that does not match the vendor's. It speaks both the GPT chat-completions format and the native Responses API, so an existing GPT integration moves across with a base-URL change rather than a rewrite.

Muse Wonder is not on our catalogue and we are not going to describe it as if it were: reaching it means Meta's Model API, and the higher-scoring Full checkpoint is not sold per token at all. Muse Spark 1.2 is what we carry from Meta — a multimodal reasoning checkpoint at $1.25 in and $4.25 out across 1,048,576 tokens — and it is the honest cheap leg if you want the lab rather than the flagship.

What the pair sharing one endpoint buys you is a measurement rather than an argument. Swap the model ID, hold the prompt and the effort setting, and compare your own tokens-out on both. The evaluator's 60-million against 130-million split is an average over ten benchmarks you probably do not run; yours is the number that decides whether you are paying a 2.5x price for a cheaper job or the other way round. If the concision does not reproduce on your workload, you have learned something the rate cards cannot tell you — and automatic failover across providers means you can find that out on live traffic without a second integration or a second contract.

The short version

Meta Muse Wonder is the cheaper model per token and the more expensive one per completed task. GPT-6 Astra is the reverse: a premium rate card attached to a model that finishes in less than half the output tokens, which lands it about 28% cheaper per index task and one point higher on the index. Anyone choosing on sticker price will pick Muse Wonder and pay more; anyone choosing on the index alone will pick Astra and miss that its long-context tier and its 128,000-token output ceiling are the two situations where the cheaper model is the better one.

The pairing worth flagging as genuinely unresolved is the third one neither vendor puts on a slide: both are closed, both are priced in the band the evaluator calls expensive, and both are losing ground on cost-per-task to open-weight models charging a fifth of either rate. That is the comparison a budget will eventually force, and it does not appear in either launch post.