Muse Spark vs Claude Opus 5.5: Diez puntos de inteligencia y una factura 3,7x

The comparison between Muse Spark and Claude Opus 5.5 is usually written as a scoreboard, and the scoreboard does have a winner: on Artificial Analysis' Intelligence Index the current Muse Spark — Muse Spark 1.3 at its max reasoning setting — scores 48, while Claude Opus 5.5 at max effort with default fallback scores 58 and sits first on the board. That is the number everyone quotes. The number almost nobody quotes sits on the same page: completing one Intelligence Index task costs $1.60 on Muse Spark 1.3 and $5.98 on O​pus 5.5. Ten index points for roughly 3.7x the money per task, with Muse Spark also running about 28% faster and emitting a third fewer tokens to get there. Neither of those figures is ours — both come from Artificial Analysis' own hosted listings — and which one matters depends entirely on what you are doing, so this piece is about separating the two questions rather than picking a side.

Primero, lo que oculta el nombre del modelo

Muse Spark is not one model, and the matchup only makes sense once you fix a version. The line runs Muse Spark 1.1 (July 16, 2026), Muse Spark 1.2 (August 5, 2026) and Muse Spark 1.3 (September 2, 2026), all from M​eta Superintelligence Labs, all sharing a 1,048,576-token context window and a text-out modality. M​eta labels 1.3 current. Muse Spark 1.3 also gained its "max" reasoning level on September 4, two days after launch, after M​eta finished additional safety testing — and that matters here because every AI-lab scorecard, M​eta's included, was produced at that setting, while the model most developers actually called for the first two days was capped at xhigh.

Claude Opus 5.5 arrived on September 22, 2026, the first release in A​nthropic's 5.5 family, and it is a simpler object: one GA model, adaptive thinking always on, effort selectable from low to max. A​nthropic's own framing was cost — it says O​pus 5.5 costs 40% less to run than Claude Opus 5, with input and output at $4 and $20 per million tokens and cache reads at $0.20, a 60% cut on cache reads versus its predecessor. Claude Fable 5.1 still sits above it for the hardest reasoning work.

Entonces, la descripción honesta es una comparación entre un modelo al que se sigue redirigiendo bajo el mismo nombre de familia y un modelo que se lanzó una vez con una configuración fija y no se ha movido desde entonces.

Los números independientes, con su asterisco

Artificial Analysis es el único tercero con listados públicos para ambos, y no los compara en la misma categoría: los modelos de pesos abiertos se clasifican frente a modelos de pesos abiertos de la misma clase de tamaño, y los modelos propietarios frente al campo más amplio. Ambos son propietarios, así que esa distorsión concreta no entra en juego; lo que sí entra en juego es que ninguno de los listados es una repetición controlada del arnés del otro.

• Muse Spark 1.3 (max) — Índice de Inteligencia 48, puesto 24 de 217; velocidad de salida 121.9 tokens/segundo; $1.25 de entrada / $4.25 de salida por millón de tokens; $1.60 por tarea de índice; 170M tokens de salida generados durante el índice.

• Claude Opus 5.5 (max, respaldo predeterminado) — Índice de Inteligencia 58, puesto 1 de 217; velocidad de salida 95.2 tokens/segundo; $4.00 entrada / $20.00 salida; $5.98 por tarea de índice; 260M tokens de salida.

Read those two lists side by side and the shape of the trade is visible without any interpretation. O​pus 5.5 wins intelligence by 10 points and wins it while being more verbose, not less — 260M tokens against 170M — so it is not buying its score with economy. Muse Spark 1.3 is faster, cheaper per token, cheaper per completed task, and less talkative. On the price-per-task line is where the gap is widest: you can run roughly 3.7 Muse Spark 1.3 index tasks for the cost of one O​pus 5.5 index task.

Un dato independiente más, porque apunta en la dirección contraria y vale la pena mencionarlo: la tabla de clasificación de texto de LMArena sitúa una configuración de Claude Opus 5.5 en el puesto 3 de la clasificación general, con 1508 Elo, y Muse Spark 1.3 no aparece en absoluto en esa tabla. La votación por preferencia humana y un índice de benchmark no coinciden en la magnitud, lo cual es normal, pero sí significa que la diferencia de 10 puntos no debería interpretarse como "10% peor en todo".

Donde la brecha es más amplia, según los proveedores.

Las tarjetas de puntuación de los proveedores no son comparables entre sí y ninguno de los dos proveedores publicó los mismos benchmarks, pero la coincidencia es reveladora. Todo lo que figura a continuación es reportado por los proveedores y no ha sido reproducido por nosotros.

• Terminal-Bench 4.0 — O​pus 5.5: 66.4%, run at xhigh effort per A​nthropic's note. M​eta reports Muse Spark on Terminal-Bench 2.1 instead, where the family's published score is 76.4 with the Terminus 2 agent. Different benchmark versions, different agents — do not subtract them.

• GDPval-AA — O​pus 5.5: 1846 Elo on v2.1. Muse Spark 1.3: 1,754 on v2. The version moved between the two measurements, so the 92-point gap is directionally real and numerically soft.

• Agentic computer use — O​pus 5.5: 81.8% on OSWorld 2.1, marked "partial". Muse Spark 1.3: 66.9 on OSWorld 2.0. Again, different revisions of the same benchmark family.

• Terminal-Bench-Science 0.1 — O​pus 5.5: 58.7%. M​eta publishes no equivalent for Muse Spark 1.3.

A​nthropic is unusually explicit about the limits of its own table, noting that at this level of capability "benchmark margins have become a less reliable guide to real-world differences". That sentence is worth taking at face value in both directions: it undercuts the 10-point AA gap as much as it undercuts M​eta's DeepSWE placement claims.

Lo que sacrificas además de los puntos

• Modalidades de entrada — Muse Spark 1.3 acepta texto, imagen y vídeo. Claude Opus 5.5 acepta texto e imagen. Si tu pipeline introduce grabaciones de pantalla o vídeo directamente en el modelo, no es una decisión reñida y la brecha del índice es irrelevante.

• Maximum output — O​pus 5.5 returns up to 128K tokens synchronously, and 300K through the Batches API behind a beta header. M​eta does not publish a max-output figure for Muse Spark 1.3 that we could verify. If you generate long artifacts in a single call, an unpublished ceiling is a risk you should test rather than assume.

• Cache economics — O​pus 5.5's cache read is $0.20 per million, a 95% discount off input. Muse Spark 1.3's is $0.15, an 88% discount. For a heavily cached agent loop this narrows or inverts the per-task arithmetic versus the headline rates, and it is the single easiest thing to get wrong when comparing these two on list price alone.

• Contexto — ambos son 1M. Nada que separar.

• Release cadence — O​pus 5.5 is one model with one price and has not changed since September. Muse Spark has re-pointed three times in three months and then added a reasoning level on top. That is either healthy iteration or an integration hazard, depending on how much you like re-running evals.

Ejecutar ambos sin una segunda integración

Both sides of this comparison are on OrcaRouter — Claude Opus 5.5 and the two Muse Spark checkpoints we serve, Muse Spark 1.2 and Muse Spark 1.1 — through one API across 200+ models, at each provider's list price with 0% markup passed through. Which is the practical answer to the question this article poses: the way to find out whether your workload really needs the extra ten index points is to route a slice of production traffic to Muse Spark 1.2 at $1.25 in / $4.25 out and compare it against the same prompts on O​pus 5.5, on the same key, with automatic failover so a bad run degrades to the other model rather than to an error page. No second contract, no second SDK, no migration.

Two honest caveats about our own coverage. Muse Spark 1.3 is not on our catalogue today, so the routing comparison above starts from 1.2, which is the checkpoint Artificial Analysis measures one point below Muse Spark Open and which now carries a deprecation notice pointing at 1.3. And this comparison rests on A​nthropic's and M​eta's published figures plus Artificial Analysis' independent listings; we have not re-run either model's benchmarks, and the vendor rows above should be read as claims.

La versión que vale la pena volver a consultar

If the question you brought here is "is the cheap one good enough", the honest answer today is that the cheap one is 48 to O​pus 5.5's 58, faster, less verbose and about 3.7x cheaper per completed task — and that A​nthropic's own caveat about benchmark margins is the strongest argument for testing rather than deciding. If your work involves video in, extremely long single outputs, or the last few points of hard reasoning, O​pus 5.5 is the model that shipped with those things documented. The thing to watch is not the next Muse Spark version number but whether Muse Spark 1.3 arrives on a router you already use, and at what price — the family's pricing has been stable at $1.25 / $4.25 across 1.1, 1.2 and 1.3, which is itself a signal that M​eta is competing on capability per dollar rather than on the dollar.

One development belongs outside this matchup rather than inside it, and it is the reason this family is being written about this week at all. On October 15, 2026 — thirteen days after Muse Spark 1.3 shipped and the day before this article — M​eta published the weights and licence for Muse Spark Open, the family's separate open-weight member, whose repository had been sitting gated and unannounced since September 15. The 816B-total / 42B-active architecture currently circulating is that release, not the closed 1.3 measured above. Artificial Analysis does list it, at an intelligence index of 39 against the 48 this article attributes to Muse Spark 1.3, and M​eta has not published the evaluation report behind its own scorecard for it. So the artefact is confirmed and the performance claim is not; do not read this comparison as covering it.

Lo que los números no pueden decirte es si algo de esto ha salido mal sobre el terreno: los incidentes de IA documentados y los fallos de agentes en torno a los modelos de esta clase se recopilan en el Archivo de Incidentes de IA de OrcaRouter, donde cada entrada incluye sus propias fuentes, su gravedad, su grado de confianza y su indicador de disputa.