Muse Spark vs Grok 4.7: Di Mana Index dan Arena Tidak Sepakat

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.

Dua model yang membuat taruhan yang sama

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.

Angka-angka independen, berdampingan

Semua yang ada di bawah ini adalah pengukuran yang dihosting sendiri oleh Artificial Analysis, yang diambil pada konfigurasi komputasi tinggi masing-masing model. Tidak ada satu pun baris yang merupakan klaim vendor.

• 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.

Ambang batas harga yang tidak disebutkan siapa pun

Grok 4.7 menetapkan harga secara bertingkat, dan ambang batasnya berada pada 200.000 token prompt: di bawahnya Anda membayar $2,00 untuk input dan $6,00 untuk output; pada atau di atasnya, seluruh permintaan ditagih $4,00 untuk input dan $12,00 untuk output, dengan input yang di-cache bergeser dari $0,50 ke $1,00. Aturan ini bukan penetapan harga penggunaan marginal — melewati garis tersebut menghitung ulang harga semua hal dalam permintaan. Dibandingkan dengan jendela konteks 500.000 token, itu adalah pintu jebakan yang berada pada 40% kedalaman jendela, dan pipeline retrieval apa pun yang menjejalkan dokumen ke dalam prompt Grok akan menemukannya sebulan setelah diluncurkan.

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.

Di mana Grok 4.7 memiliki argumen yang kuat

Tabel yang tidak seimbang di atas bukanlah keseluruhan argumen, dan tiga hal di pihak G​rok patut dinyatakan dengan gamblang.

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.

Klaim vendor, dipisahkan

Tidak satu pun dari kedua kumpulan angka di bawah ini yang kami reproduksi, dan kedua daftar tersebut tidak memiliki cukup evaluasi yang sama untuk dapat dikurangkan satu sama lain.

Pengumuman xAI melaporkan Grok 4.7 pada CursorBench 4.0 46,3%, Terminal-Bench 4.0 37,6%, EEBench 64,0%, DeepSWE v1.1 71,0% pada upaya tinggi, Harvey Legal Agent 19,6%, HealthBench Professional 56,7% dan GDPval Elo 1695 pada 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.

Apa yang mengubah keputusan dalam praktik

• Ukuran prompt — jika prompt Anda melebihi 200.000 token, penetapan harga ulang ambang batas Grok 4.7 adalah pos baris tunggal terbesar dalam perbandingan ini, dan itulah yang membuat tabel per-token menyesatkan.

• 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.

Menempatkan keduanya pada satu 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.

Yang mana, hari ini

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.

Kesenjangan antara apa yang diukur oleh papan skor dan apa yang dihadapi oleh suatu deployment adalah alasan catatan ini penting: kegagalan AI dan insiden agen yang terdokumentasi seputar model di kelas ini dikumpulkan dalam OrcaRouter AI Incident Archive, di mana setiap entri mencantumkan sumbernya sendiri, tingkat keparahan, tingkat keyakinan, dan penanda disengketakan.