Muse Spark 1.3とGemini 4 Argonの比較用ヒーロータイトルカード。タイトルは「Muse Spark vs Gemini 4 Argon」、サブタイトルは「12月1日に倍になる価格」。水平タイムラインには2つのラベル付き停止点「11月30日まで: $2.00 / $10.00」と「12月1日から: $4.00 / $20.00」があり、その間に上向きのステップ矢印がある。その横には「Muse Spark 1.3」とラベルされた細いカードがあり、「$1.25 / $4.25、変更なし」と書かれている。OrcaRouterのロゴを右下に合成。
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Muse Spark vs Gemini 4 Argon:12月1日に価格が2倍に

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Rowan Sterling

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ベンチマーク:Artificial Analysis · 毎日更新
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Almost every Muse Spark versus G​emini 4 Argon comparison you can find today compares $1.25 against $2.00 per million input tokens, and that comparison expires in six weeks. G​oogle's own pricing page prices G​emini 4 Argon at $2.00 in and $10.00 out with cached input at $0.10 on an introductory basis "through November 30, 2026"; from December 1, 2026 the same model is listed at $4.00 in, $20.00 out and $0.20 cached. M​eta's Muse Spark family has not moved off $1.25 in and $4.25 out across Muse Spark 1.1, 1.2 and 1.3. So the interesting question about this matchup is not which model is smarter in October — it is what the arithmetic looks like on the first of December, and whether whatever you build on G​emini 4 Argon this month survives that date.

2つのモデル、大きく異なる2つの出版パターン

G​emini 4 Argon is G​oogle's newest frontier model and it went generally available on October 13, 2026 — the changelog entry is a single line, and the developer documentation is dated the same week. Artificial Analysis' listing carries a September 2026 release note, which we read as a pre-GA or preview origin rather than a second launch; we could not find a formal preview announcement to confirm that, so the GA date is the one we treat as solid. G​oogle's model page marks it "Stable".

Muse Spark 1.3 is the current checkpoint of M​eta Superintelligence Labs' Muse Spark line, released September 2, 2026, with its "max" reasoning level opened to developers on September 4. The unversioned "Muse Spark" name refers to the original April 2026 model in the same family; version numbers are doing real work here, and 1.3 is the one to compare.

独立したスコアボード

どちらのリストもArtificial Analysisによるもので、どちらも高コンピュート構成を測定しているため、これは推論モデル対推論モデルの比較がインデックス自体で同一条件で行える数少ない対戦の一つです。このセクションの内容はすべてArtificial Analysisの測定であり、ベンダーの主張ではありません。

• Intelligence Index — G​emini 4 Argon (High): 53, rank 8 of 217. Muse Spark 1.3 (max): 48, rank 24 of 217.

• Cost per index task — G​emini 4 Argon: $1.99. Muse Spark 1.3: $1.60.

• Output speed — G​emini 4 Argon: not measured; the listing shows no figure. Muse Spark 1.3: 121.9 tokens per second, ranked 43 of 186.

• Verbosity — G​emini 4 Argon: 110M output tokens during the index. Muse Spark 1.3: 170M. Argon is the more concise of the two.

• 実測に基づくリスト価格 — Argon は導入価格で $2.00 / $10.00、Muse Spark 1.3 は $1.25 / $4.25、キャッシュ読み取りは $0.15。

5ポイントの知能差は実在するが、控えめだ。逆転するのはコスト差であり、しかも大きく逆転する。導入価格では、Argon は完了したタスクあたり約24%高くつく。12月の価格では、入力と出力の価格について同じ比較をすると、入力側はおよそ2倍、出力側も2倍になるため、タスクあたりの割高分はもはやわずかではない。これがこの比較で最も意思決定に関わる単一の事実であり、どのローンチ報道にも含まれていない。

A two-column scoreboard titled "Muse Spark 1.3 vs Gemini 4 Argon — the scoreboard". The left column, Muse Spark 1.3, reads: Intelligence Index 48; list price in / out $1.25 / $4.25; cost per index task $1.60; output speed 121.9 tokens/s; context window 1M tokens; video input Yes. The right column, Gemini 4 Argon, reads: Intelligence Index 53; list price in / out $2.00 / $10.00 to Nov 30; cost per index task $1.99; output speed not measured; context window 1M tokens; video input Yes. A footer reads "Argon list price rises to $4.00 / $20.00 on Dec 1, 2026. Index figures per Artificial Analysis." OrcaRouter logo composited bottom-right.

それぞれが実際に受け入れるもの

ここで記録上の二つの情報源が食い違っており、どちらかの側を取るよりも、その不一致を示す価値がある。

G​oogle's own model page for G​emini 4 Argon lists text, image, video, audio and PDF as inputs with text output and thinking effort at low, medium or high, defaulting to medium. Artificial Analysis' summary sentence for the same model says it "supports text and image input" and outputs text. We checked both because that gap is exactly the kind of thing that gets copied forward; the vendor's own page is the broader and more specific list, and it is the one to test against. Treat the vendor list as the claim and verify it on your own files.

Muse Spark 1.3 は、スペック上はより狭いモデルだ。テキスト、画像、動画を入力し、テキストを出力する。文書化されたモダリティ行には音声や PDF の経路がなく、推論ラダーは max まで及ぶ。

• Inputs — G​emini 4 Argon: text, image, video, audio, PDF per G​oogle; text and image per the independent listing. Muse Spark 1.3: text, image, video.

• 出力 — テキストのみ、両方とも。

• コンテキストウィンドウ — 両方とも1,048,576トークンで、G​oogleは、入力・思考・出力にまたがって共有される統合ウィンドウと説明している。

• Max output — G​emini 4 Argon: 65,536 tokens on default synchronous calls, extensible toward the full window with long decode. M​eta does not publish a max-output figure for Muse Spark 1.3 that we could verify.

• 推論制御 — Argon:低/中/高、オフスイッチなし。Muse Spark 1.3:最小〜最大、そして M​eta のツーリングに関する注記でも、推論はオフにできないとされている。

ベンダーの主張は、一線を越えずベンダー側に留められる

どちらの企業も比較可能な表を公表していないため、これはスコアボードではなく主張の一覧であり、そのいずれも当社が再現したものではありません。

G​oogle's published figures for G​emini 4 Argon run 77.9% on DeepSWE v1.1, 57.4% on Terminal-bench 4.0, 69.2% on the OSWorld 2.0 offline subset, 51.3% on AutomationBench, 68.9% on the Vals Index, 71.6% on Chartography and 91.7% on LVBench, among others. M​eta's published figures for Muse Spark 1.3 centre on 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 and GDPval-AA v2 at 1,754. Note where the two overlap and where they do not: DeepSWE is shared and close, OSWorld is shared and about two points apart on different revisions, and Terminal-Bench is reported by M​eta on an older version with a named agent harness. Subtracting across those differences is not arithmetic, it is fortune-telling.

One independent signal worth carrying alongside the vendor rows: LMArena's text leaderboard currently places a G​emini 4 Argon configuration first overall at 1524 Elo, marked preliminary. Muse Spark 1.3 has no entry on that board. Preliminary or not, that is the strongest evidence in the record that Argon's five-point index lead understates its standing with human voters.

Screenshot of the Artificial Analysis page for Gemini 4 Argon, labelled "Google — Proprietary model - Released September 2026" and headed "Gemini 4 Argon (High) Intelligence, Performance & Price Analysis". Visible figures: Intelligence Index 53; $2.00 input and $10.00 output per million tokens with a 95% cache discount; $1.99 per task; 110M output tokens, described as somewhat verbose against a median of 87M; and the summary sentence that the model supports text and image input and outputs text with a 1M token context window.

ベンチマークよりも早くそれを決めるエンジニアリングの詳細

• Sampling parameters are ignored — G​oogle's documentation states that temperature, top_p and top_k have no effect on G​emini 4 Argon, and that explicit caching is not supported, leaving implicit caching only. If your stack tunes temperature, that knob silently stops working rather than erroring. Muse Spark 1.3 exposes a reasoning-effort parameter but no equivalent published restriction on sampling.

• タイムアウト — Google はこのモデルに対して、少なくとも 15 分のクライアントタイムアウトを推奨しています。ゲートウェイのデフォルトが 60 秒の場合、モデルのエラーのように見えて実際はそうではない失敗が発生します。

• 会話状態 — Argon はマルチターンの状態管理にサーバー側のインタラクション識別子を使用します。Muse Spark のファミリーは、標準的なメッセージ配列パターンを中心に文書化されています。両者の間の移植は、単純な検索と置換ではありません。

• キャッシュ読み取り — Muse Spark 1.3 はキャッシュ済み入力を $0.15 で返し、標準の $1.25 に対して 88% 割引となります。Argon のキャッシュ済み入力は $0.10 で、導入価格の $2.00 に対して、その後 $0.20 に上昇し、$4.00 に対してとなります。大量に再利用する長いエージェントループでは、これが最も重要な項目であり、12月以降も両者が近い水準にとどまる数少ない点の一つです。

OrcaRouterが適する場面と、適さない場面

Being precise about our own coverage: OrcaRouter routes Muse Spark 1.2 and Muse Spark 1.1 at Meta's list price with 0% markup passed through, alongside 200+ other models on one API with automatic failover and a routing DSL for splitting traffic between them. Muse Spark 1.3 and G​emini 4 Argon are both absent from our catalogue today — Argon is reachable through G​oogle's own API and several third-party platforms, and we are not one of them. So the honest framing is not "run both here". It is that the Muse Spark side of this comparison is available to you on a key you may already hold, which makes a cost test against Argon on your own prompts a same-week exercise rather than a procurement one, and that M​eta's pricing has been static across three checkpoints while G​oogle's has a published expiry date.

Screenshot of the OrcaRouter model catalogue at orcarouter.ai/models, headed "Models / 210 models - 16 providers - one API key, one bill", with a filter sidebar (input modalities, context length, input price, API docs, status, access tier, series, supported parameters), modality tabs, a sort control, a "How to call any model" panel showing POST https://api.orcarouter.ai/v1/chat/completions with a model id and a messages payload, credit tiers from $50 to $1000, and model cards including OrcaCyber Zero 1.5, Claude Sonnet 5.5 and GPT-6.1 Sol.

12月までにやるべきこと

If you are already building on G​emini 4 Argon, the useful thing to do this month is not a benchmark run — it is a cost model at $4.00 and $20.00, because that is the price that will apply to everything you ship and leave running. If the answer at that price still works, Argon is the better model on the current evidence: higher index, more concise, broader documented input list, and the only one of the two with a top-of-board human-preference placement. If it does not, the substitution to evaluate is Muse Spark 1.3 at a flat $1.25 / $4.25, which trades five index points for a price that has not moved in three releases and roughly three times the output speed. The one thing we would not do is treat October's advertised rates as the rates — in this matchup, the number in the title is the one with an end date.

数字からは分からないのは、それが現場で何か問題を起こしていないかどうかだ。このクラスのモデルに関する、記録されたAIインシデントやエージェントの障害は、OrcaRouter AIインシデントアーカイブに集められており、各項目にはそれぞれの情報源、深刻度、信頼度グレード、異議ありフラグが付されている。

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