
Muse Spark נגד Gemini 4 Argon: המחיר מוכפל ב-1 בדצמבר
- OrcaחדשOrca: OrcaCyber Zero 1.52026-10-10$3.00 / $7.50 לכל 1M טוקנים · 72 tok/s
- openaiOpenAI: GPT-6.1 Sol2026-09-2952אינטליגנציה
- anthropicAnthropic: Claude Sonnet 5.52026-09-2856אינטליגנציה
- typesafeTypeSafe: Jev 1.132026-09-24$0.04 / $0.00 לכל 1M טוקנים · 737 tok/s
- OpenAIOpenAI: GPT-6 Luna2026-09-2238אינטליגנציה
- OpenAIOpenAI: GPT-6 Sol2026-09-2248אינטליגנציה
- AnthropicAnthropic: Claude Opus 5.52026-09-2258אינטליגנציה
- xAIGrok 4.72026-09-2146אינטליגנציה
- OrcaOrca: OrcaCyber Zero 1.02026-09-17$3.00 / $7.50 לכל 1M טוקנים · 35 tok/s
- OrcaOrca: OrcaVerify Text 1.02026-09-16$2.00 / $0.00 לכל 1M טוקנים · 821 tok/s
- DeepSeekDeepSeek: DeepSeek V4.1 Flash2026-09-1040אינטליגנציה
- OpenAIOpenAI: GPT-6 Astra2026-09-0453אינטליגנציה77כתיבת קוד
- GoogleGoogle: Gemini 3.8 Flash2026-09-0241אינטליגנציה76כתיבת קוד
- AlibabaQwen: Qwen3.8 Max (0902)2026-09-0245אינטליגנציה76כתיבת קוד
- AnthropicAnthropic: Claude Fable 5.12026-09-0153אינטליגנציה82כתיבת קוד
- TencentTencent: Hy4 preview2026-08-28$0.83 / $2.50 לכל 1M טוקנים · 52 tok/s
- AlibabaQwen: Qwen3.8 Flash2026-08-26$0.15 / $0.47 לכל 1M טוקנים · 108 tok/s
- z-aiZ.ai: GLM 5.3 Flash2026-08-2642אינטליגנציה72כתיבת קוד
- DeepSeekDeepSeek: DeepSeek V4 Flash Vision (Exp)2026-08-21$0.22 / $0.66 לכל 1M טוקנים · 184 tok/s
- z-aiZ.ai: GLM 5.32026-08-1845אינטליגנציה75כתיבת קוד
Almost every Muse Spark versus Gemini 4 Argon comparison you can find today compares $1.25 against $2.00 per million input tokens, and that comparison expires in six weeks. Google's own pricing page prices Gemini 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. Meta'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 Gemini 4 Argon this month survives that date.
שני מודלים, שני דפוסי פרסום שונים מאוד
Gemini 4 Argon is Google'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. Google's model page marks it "Stable".
Muse Spark 1.3 is the current checkpoint of Meta 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.
לוח התוצאות העצמאי
שתי הרשומות מגיעות מ-Artificial Analysis ושתיהן מודדות תצורת חישוב עתירת משאבים, כך שזהו אחד מהמקרים הבודדים שבהם ההשוואה בין מודל הסקה למודל הסקה היא תפוחים לתפוחים על המדד עצמו. כל מה שבסעיף זה הוא מדידה של Artificial Analysis, ולא טענה של ספק.
• Intelligence Index — Gemini 4 Argon (High): 53, rank 8 of 217. Muse Spark 1.3 (max): 48, rank 24 of 217.
• Cost per index task — Gemini 4 Argon: $1.99. Muse Spark 1.3: $1.60.
• Output speed — Gemini 4 Argon: not measured; the listing shows no figure. Muse Spark 1.3: 121.9 tokens per second, ranked 43 of 186.
• Verbosity — Gemini 4 Argon: 110M output tokens during the index. Muse Spark 1.3: 170M. Argon is the more concise of the two.
• List pricing as measured — Argon at the introductory $2.00 / $10.00; Muse Spark 1.3 at $1.25 / $4.25 with cache reads at $0.15.
פער האינטליגנציה של חמש נקודות הוא אמיתי ומתון. פער העלויות הוא זה שמתהפך, והוא מתהפך בחריפות: בתעריף ההיכרות Argon עולה כ-24% יותר לכל משימה שהושלמה; בתעריף של דצמבר, אותה השוואה בתמחור קלט ופלט מכפילה בקירוב את צד הקלט ומכפילה את צד הפלט, כך שהפרמיה לכל משימה מפסיקה להיות שולית. זו העובדה היחידה הרלוונטית ביותר לקבלת החלטות בהשוואה הזו, והיא אינה מופיעה באף אחד מסיקורי ההשקה.

מה כל אחד מהם יקבל בפועל
כאן שני מקורות התיעוד חלוקים זה על זה, וראוי להציג את המחלוקת ולא לבחור צד.
Google's own model page for Gemini 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 is the narrower model on paper: text, image and video in, text out, with no audio or PDF path in the documented modality row and a reasoning ladder that runs to max.
• Inputs — Gemini 4 Argon: text, image, video, audio, PDF per Google; text and image per the independent listing. Muse Spark 1.3: text, image, video.
• פלט — טקסט בלבד, שניהם.
• חלון הקשר — 1,048,576 טוקנים לשניהם, שתואר על ידי Google כחלון מאוחד המשותף לקלט, לחשיבה ולפלט.
• Max output — Gemini 4 Argon: 65,536 tokens on default synchronous calls, extensible toward the full window with long decode. Meta does not publish a max-output figure for Muse Spark 1.3 that we could verify.
• Reasoning control — Argon: low / medium / high, no off switch. Muse Spark 1.3: minimal through max, and Meta's tooling notes reasoning cannot be turned off either.
טענות ספק, נשמרות בצד הספק של הקו
אף אחת מהחברות לא פרסמה טבלה ניתנת להשוואה, ולכן מדובר ברשימת טענות ולא בלוח תוצאות, ושום דבר ממנה אינו משוחזר על ידינו.
Google's published figures for Gemini 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. Meta'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 Meta 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 Gemini 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.

הפרטים ההנדסיים שמכריעים בכך מהר יותר ממבחני ביצועים
• Sampling parameters are ignored — Google's documentation states that temperature, top_p and top_k have no effect on Gemini 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.
• פסקי זמן — Google ממליצה על פסקי זמן של לקוח של לפחות חמש עשרה דקות עבור המודל הזה. אם ברירת המחדל של השער אצלכם היא 60 שניות, תראו כשלים שנראים כמו שגיאות מודל אך אינם כאלה.
• Conversation state — Argon uses a server-side interaction identifier for multi-turn state; Muse Spark's family is documented around the standard message-array pattern. Porting between them is not a find-and-replace.
• Cache reads — Muse Spark 1.3 returns cached input at $0.15 against $1.25 standard, an 88% discount. Argon's cached input is $0.10 against $2.00 introductory, rising to $0.20 against $4.00. In a long agent loop with heavy reuse this is the line that matters most, and it is one of the few places where the two stay close after December.
היכן OrcaRouter מתאים, והיכן לא
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 Gemini 4 Argon are both absent from our catalogue today — Argon is reachable through Google'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 Meta's pricing has been static across three checkpoints while Google's has a published expiry date.

מה לעשות לפני דצמבר
If you are already building on Gemini 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.
מה שהמספרים לא יכולים לספר לך הוא אם משהו מזה השתבש בשטח: תקריות ה-AI המתועדות וכשלי סוכנים סביב מודלים מהסוג הזה נאספים בOrcaRouter AI Incident Archive, שבו כל ערך נושא מקורות משלו, חומרה, דירוג ביטחון וסימון מחלוקת.
השוואות במאמר הזה1
זוהה מתוך המאמר הזה · בנצ'מרקים: Artificial Analysis · מתעדכן יומית
