Muse Spark vs Grok 4.7: Index와 Arena가 엇갈리는 지점

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.

같은 베팅을 하는 두 모델

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.

독립적인 숫자들, 나란히

아래의 모든 내용은 각 모델의 고연산 구성에서 수행된 Artificial Analysis 자체 호스팅 측정치입니다. 어느 행도 공급업체의 주장이 아닙니다.

• 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.

아무도 언급하지 않는 가격 임계점

Grok 4.7은 가격을 구간별로 책정하며, 기준점은 프롬프트 토큰 200,000개에 있습니다. 그 아래에서는 입력 $2.00, 출력 $6.00을 내고, 기준 이상에서는 요청 전체가 입력 $4.00, 출력 $12.00으로 청구되며, 캐시된 입력은 $0.50에서 $1.00으로 올라갑니다. 이 규칙은 한계 사용량 가격 책정이 아닙니다 — 경계를 넘으면 요청의 모든 항목 가격이 다시 매겨집니다. 500,000토큰 컨텍스트 창을 기준으로 보면, 이는 창 깊이의 40% 지점에 놓인 함정 문이며, 문서를 Grok 프롬프트에 잔뜩 밀어 넣는 모든 검색 파이프라인은 출시된 다음 달에 이 사실을 발견하게 될 것입니다.

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.

Grok 4.7이 진정한 근거를 갖는 경우

위의 한쪽으로 치우친 표가 논증의 전부는 아니며, Grok 쪽의 세 가지 점은 분명히 말할 가치가 있다.

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.

공급업체 주장, 별도로 유지

아래 두 수치 집합 중 어느 것도 우리가 재현한 것이 아니며, 두 목록은 서로 빼기에 충분할 만큼 공통된 평가를 공유하지 않습니다.

xAI의 발표는 Grok 4.7이 CursorBench 4.0에서 46.3%, Terminal-Bench 4.0에서 37.6%, EEBench 64.0%, high effort에서 DeepSWE v1.1 71.0%, Harvey Legal Agent 19.6%, HealthBench Professional 56.7%, 그리고 xhigh에서 GDPval Elo 1695를 기록했다고 보고합니다.

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.

유혹은 DeepSWE에서 75.4를 71.0과 견주어 4점 차 Meta 승리라고 부르고 싶어지는 것이다. 그러지 마라: xAI는 높은 노력 설정에서 수치를 공개하는 반면 Meta의 최고 점수는 최대 설정에 속하며, 두 회사는 서로 다른 하네스를 사용했다. 두 목록이 진정으로 입증하는 것은 그 규모가 아니라 불일치의 형태다.

실제로 결정을 바꾸는 것은 무엇인가

• 프롬프트 크기 — 프롬프트가 200,000 토큰을 초과한다면, Grok 4.7의 임계값 재가격은 이 비교에서 단일 최대 항목이며, 토큰당 표를 오해하게 만드는 바로 그 요인입니다.

• 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.

둘 다 하나의 엔드포인트에 배치하기

Grok 4.7이 OrcaRouter에 있습니다, 그리고 우리가 제공하는 두 개의 Muse Spark 체크포인트, Muse Spark 1.2와 Muse Spark 1.1도 함께, 각 제공업체의 정가에 0% 마크업을 그대로 적용한 가격으로 — 200개 이상의 모델을 아우르는 단일 API, 이들 간의 자동 장애 조치, 규칙별로 트래픽을 분할하는 라우팅 DSL. 그 조합은 이 글에서 대신 답해 줄 수 없는 질문에 답하는 가장 저렴한 방법입니다. G​rok 쪽의 200K 임계값은 라우팅 규칙의 후보로 특히 적합합니다: G​rok을 임계값에서 제한하고, 임계값을 초과한 요청은 정액제 모델로 보내고, 둘 중 하나의 성능이 저하되는 날에는 장애 조치가 처리하도록 하세요.

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.

오늘은 어느 것?

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.

스코어보드가 측정하는 것과 배포가 실제로 마주하는 것 사이의 그 간극이 바로 이 기록이 중요한 이유입니다: 이 계열의 모델과 관련된 문서화된 AI 실패 및 에이전트 사고가 수집되는 곳은 OrcaRouter AI Incident Archive이며, 각 항목은 자체 출처, 심각도, 신뢰도 등급 및 이의 제기 플래그를 갖추고 있습니다.