Muse Spark vs Grok 4.7: donde el Index y la Arena discrepan

On paper this is the least ambiguous matchup in the current field. M​use Spark 1.3, the current checkpoint of M​eta Superintelligence Labs' M​use Spark line, scores 48 on Artificial Analysis' Intelligence Index at its max reasoning setting and costs $1.60 to complete an index task. Grok 4.7, released September 21, 2026, scores 46 at its xhigh setting and costs $3.74 for the same task. M​use Spark wins the index, wins price per token on both sides of the invoice, wins context window, wins output speed, and emits fewer tokens doing it. Then you open a second independent board and the picture changes: Grok 4.7 sits at rank 89 of LMArena's text leaderboard with 1444 Elo from nearly twelve thousand votes, and M​use Spark 1.3 is not on that leaderboard at all. That divergence — not the two-point index gap — is the thing worth understanding before you pick, because whichever board your users resemble is the board that will decide whether your migration looks like an upgrade.

Dos modelos que hacen la misma apuesta

Grok 4.7 and M​use Spark 1.3 are near-identical in design philosophy. Both are positioned as flagship reasoning models that run on a long-horizon agentic pitch. Both ship their headline benchmark numbers at an extreme effort setting rather than the default. Both are, by the independent measure, unusually talkative. And both changed nothing about pricing when they released — Grok 4.7 was served at the same price and speed as Grok 4.6, and M​use Spark has held $1.25 in and $4.25 out across three consecutive checkpoints.

Grok 4.7's own documentation describes it as x​AI's flagship for "code and everything else: agentic tool calling, minimal hallucinations, configurable reasoning", which is a claim about reliability rather than raw capability. M​eta's line for M​use Spark 1.3 is "trained for agentic workflows and optimized for competitive coding performance". Two vendors, describing the same job, with numbers that mostly favour one of them.

One naming note before the numbers, because it bites people: M​use Spark is a family. Muse Spark 1.1 (July 2026) and Muse Spark 1.2 (August 2026) share the same modality row and context window as 1.3 and differ only in capability. The unversioned "M​use Spark" refers to the original April 2026 model. Comparing Grok 4.7 to anything other than 1.3 is comparing it to a re-pointed older checkpoint.

Los números independientes, uno al lado del otro

Todo lo que aparece a continuación es una medición propia alojada de Artificial Analysis, realizada con la configuración de alto cómputo de cada modelo. Ninguna de las filas es una afirmación del proveedor.

• Intelligence Index — M​use Spark 1.3 (max): 48, rank 24 of 217. Grok 4.7 (xhigh): 46, rank 30 of 217.

• Cost per index task — M​use Spark 1.3: $1.60. Grok 4.7: $3.74. M​use Spark is 2.3x cheaper per completed task.

• Output tokens during the index — M​use Spark 1.3: 170M. Grok 4.7: 240M. Both are flagged as very verbose; G​rok is 41% more so.

• Output speed — M​use Spark 1.3: 121.9 tokens per second, rank 43 of 186. Grok 4.7: 90.8 tokens per second, rank 87 of 186.

• Time to first token — Grok 4.7 averages 37.26 seconds, which the listing places at the high end against a tier median of 3.70s. No comparable figure is published for M​use Spark 1.3, which is a gap rather than a win; we are not going to treat a missing number as a good one.

• List pricing — Grok 4.7: $2.00 in / $6.00 out with cached input at $0.50 for prompts under 200,000 tokens. M​use Spark 1.3: $1.25 in / $4.25 out with cached input at $0.15, no threshold.

• Context — Grok 4.7: 500,000 tokens. M​use Spark 1.3: 1,048,576.

• Modalities — both accept text and image and return text; M​use Spark 1.3 adds video input, which Grok 4.7's documentation does not list.

El umbral de precios que nadie menciona

Grok 4.7 tiene precios por niveles, y el umbral se sitúa en los 200,000 tokens de prompt: por debajo de él pagas $2.00 de entrada y $6.00 de salida; en el umbral o por encima, toda la solicitud se factura a $4.00 de entrada y $12.00 de salida, con la entrada en caché pasando de $0.50 a $1.00. La regla no es una tarificación por uso marginal: cruzar la línea recalcula el precio de todo en la solicitud. Frente a una ventana de contexto de 500,000 tokens, eso es una trampilla situada al 40% de la profundidad de la ventana, y cualquier canalización de recuperación que acumule documentos en un prompt de Grok se topará con ella el mes después de que se lance.

M​use Spark 1.3 publishes no comparable tier structure: one rate across the whole 1M-token window. That is worth more than the two-point index gap to anyone whose prompts are large by design. It also compounds with the cache difference — G​rok's cached input is $0.50 per million against M​use Spark's $0.15 — so in a heavily cached agent loop running near the threshold, the effective cost gap between these two is wider than the headline rates suggest, not narrower.

Dónde Grok 4.7 tiene un caso genuino

La tabla desequilibrada de arriba no es todo el argumento, y tres cosas del lado de G​rok merecen decirse sin rodeos.

First, latency tiers. x​AI ships a Grok 4.7 Fast variant at double the speed and double the price, distributed through Cursor and G​rok Build rather than the public API. If your constraint is interactive responsiveness during peak hours, having a vendor-sanctioned fast tier for the same model is a real operational option. M​eta publishes no equivalent for M​use Spark 1.3.

Second, documentation completeness. Grok 4.7 publishes a knowledge cutoff of May 2026 and a full rate-limit and regional-availability picture; M​use Spark 1.3's published record does not include a knowledge cutoff or a max-output figure we could verify. When two models are close, the one whose limits are written down is the one you can plan against, and a vendor being silent about a ceiling is not the same as the ceiling not existing.

Third, and most importantly for the framing of this piece: the human-preference signal points the other way. Grok 4.7 holds rank 89 on LMArena's text leaderboard with 11,591 votes behind a 1444 Elo — a modest placement, but a measured one. M​use Spark 1.3 is not on that board. An index score and an arena score are answering different questions: the index asks whether the model can complete a set of hard tasks, the arena asks whether people prefer its answers. Grok 4.7's index rank of 30 against an arena rank of 89 says something specific — that its capability does not translate into preference at the same rate as its peers'. M​use Spark 1.3's absence says something less specific but not neutral: the evidence for how people react to its output is simply missing.

Afirmaciones de proveedores, mantenidas por separado

Ninguno de los dos conjuntos de cifras que figuran a continuación es reproducido por nosotros, y las dos listas no comparten suficientes evaluaciones como para restarlas entre sí.

El anuncio de xAI informa que Grok 4.7 obtiene 46.3% en CursorBench 4.0, 37.6% en Terminal-Bench 4.0, 64.0% en EEBench, 71.0% en DeepSWE v1.1 con esfuerzo alto, 19.6% en Harvey Legal Agent, 56.7% en HealthBench Professional y 1695 de Elo en GDPval con xhigh.

M​eta's scorecard for M​use Spark 1.3 reports DeepSWE v1.1 at 75.4, OSWorld 2.0 at 66.9, Terminal-Bench 2.1 at 76.4 with the Terminus 2 agent, MCP-Atlas at 89.2, GDPval-AA v2 at 1,754, SciCode at 58.5, HLE-Diamond without tools at 26.1 and JobBench at 61.8.

The temptation is to read 75.4 against 71.0 on DeepSWE and call it a four-point M​eta win. Do not: x​AI publishes its figure at high effort while M​eta's best score belongs to the max setting, and the two companies ran different harnesses. What the two lists genuinely establish is the shape of the disagreement, not its size.

¿Qué cambia la decisión en la práctica?

• Tamaño del prompt — si tus prompts superan los 200.000 tokens, el reajuste de precios por umbral de Grok 4.7 es la partida más grande de esta comparación, y es la que hace que la tabla por token resulte engañosa.

• Retrieval and long context — M​use Spark 1.3's 1M window against Grok 4.7's 500K is a real capability difference for whole-repository or whole-document work, not a spec-sheet flourish.

• Output volume — both models are verbose; Grok 4.7 more so at 240M tokens on the index against 170M. If your bill is dominated by output tokens, you are choosing between two expensive options, and the cheaper one is M​use Spark.

• Reasoning controls — Grok 4.7 exposes low, medium, high and xhigh, defaulting to high. M​use Spark 1.3 runs from minimal to max. Neither can be turned off entirely.

• Streaming and structured output — Grok 4.7 supports function calling and structured outputs, and its Responses API always returns encrypted reasoning content; batch processing is not supported. If you depend on batch pricing, that is a disqualifier on G​rok's side and worth checking against M​use Spark's own API terms before you assume the same.

• Preference risk — if your product's quality bar is set by user judgement rather than by benchmark completion, Grok 4.7 has a measured — if unflattering — arena position and M​use Spark 1.3 has none. Neither number is a reason to migrate; the absence of one is a reason to run your own comparison.

Poniendo ambos en un solo endpoint

Grok 4.7 is on OrcaRouter, along with the two Muse Spark checkpoints we serve, Muse Spark 1.2 and Muse Spark 1.1, at each provider's list price with 0% markup passed through — one API across 200+ models, automatic failover between them, and a routing DSL for splitting traffic by rule. That combination is the cheapest way to answer the question this article cannot answer for you. The 200K threshold on G​rok's side makes a particularly good candidate for a routing rule: cap G​rok at the threshold, send the oversized requests to a model with a flat rate, and let failover handle the day one of them degrades.

Two disclosures. M​use Spark 1.3 itself is not in our catalogue, so the M​use Spark side of any test you run here starts at 1.2 — the checkpoint Artificial Analysis measures one point below M​use Spark Open, and one that now carries a deprecation notice pointing at 1.3. And the only numbers in this piece we would defend as measurements are the Artificial Analysis rows; the vendor figures on both sides are claims, and the LMArena placement is preliminary by its own marking.

¿Cuál, hoy?

If you are choosing on published, independent evidence and your workload is long-context, video-in, cache-heavy or cost-sensitive, M​use Spark 1.3 is the stronger model on nearly every line and the cheaper one per completed task by a factor of 2.3. If your workload lives under 200,000 tokens, depends on structured output and function calling, benefits from a vendor-run fast tier, or is judged by users rather than by benchmark completion, Grok 4.7 has a coherent case that the index comparison does not capture — and it is the only one of the two with a human-preference number at all. The honest summary is that this matchup is decided by prompt size and by who is grading the output, and by almost nothing else on either scorecard.

Esa brecha entre lo que mide un marcador y lo que encuentra un despliegue es la razón por la que el registro importa: los fallos de IA documentados y los incidentes de agentes en torno a modelos de esta clase se recopilan en el OrcaRouter AI Incident Archive, donde cada entrada incluye sus propias fuentes, gravedad, grado de confianza e indicador de disputa.