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Gemini 3.7 Flash vs Muse Spark

Full profiles: Gemini 3.7 Flash (Google) and Muse Spark (Meta): every benchmark, price and rank.

Updated · Data from 10 sources

As of 25 Oct 2026, Gemini 3.7 Flash (high) and Muse Spark are neck and neck on quality (63.5 vs 64.3, inside the ±4.7 margin of error) and close on price, so pick on the category that matters to you. Pick Gemini 3.7 Flash for knowledge (68 vs 63); pick Muse Spark for agents & tools (66 vs 48).

Gemini 3.7 Flash benchmarks high Google Muse Spark benchmarks MetaOpen in the comparison tool

Which to pick for…

The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.

Use casePickWhyConfidence
CodingMuse Spark65 vs 63 category score; Arena Coding 1529 vs 1520Medium
Agents & tool useMuse Spark66 vs 48 category scoreHigh
MathsGemini 3.7 Flash63 vs 59 category score; Arena Maths 1505 vs 1467Medium
ReasoningGemini 3.7 Flash67 vs 65 category score; GPQA Diamond 94.8% vs 89.8%Medium
KnowledgeGemini 3.7 Flash68 vs 63 category score; AA-Omniscience 26.5 vs 7.2Medium
Long documentsGemini 3.7 Flash66 vs 64 category score; AA-LCR 81.7% vs 78.0%Medium
Images and other mediaGemini 3.7 Flash68 vs 65 category score; Arena Vision 1314 vs 1306Medium
Chat quality (human preference)Either68 vs 68 on Arena votesEither
High volume on a budgetNeither flagshipA smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs–

Key differences

  • Gemini 3.7 Flash (high) and Muse Spark are neck and neck on quality (63.5 vs 64.3, within the ±4.7 margin).
  • Gemini 3.7 Flash (high) is stronger in composite, human preference, knowledge, long context, maths, multimodal, reasoning.
  • Muse Spark is stronger in agents & tools, coding, instruction following.

At a glance

SpecGemini 3.7 FlashMuse Spark
DeveloperGoogleMeta
Released13 Aug 20268 Apr 2026
Context window (tokens)1,048,576262,144
Compared atGemini 3.7 Flash (high)Muse Spark
Open weightsNoNo
Price in / out / cached input per 1M$0.750 / $3.75 / $0.075–

Category by category

Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.

CategoryGemini 3.7 Flash (high)Muse SparkLeader
Reasoning67.365.3Gemini 3.7 Flash
Coding62.965.0Muse Spark
Agents & tools47.666.1Muse Spark
Maths63.359.1Gemini 3.7 Flash
Knowledge68.063.3Gemini 3.7 Flash
Instruction following–77.3not comparable
Human preference68.468.3level
Multimodal67.664.5Gemini 3.7 Flash
Long context65.964.0Gemini 3.7 Flash
Composite73.264.5Gemini 3.7 Flash

Where sources disagree

Across the 29 benchmarks both have, Gemini 3.7 Flash leads 25, Muse Spark leads 4. Every overall index that rates both puts Gemini 3.7 Flash ahead.

Overall indexGemini 3.7 Flash (high)Muse SparkLeader
BenchLeader Index63.564.3neck and neck
AA Intelligence Index v4.3.239.131.3Gemini 3.7 Flash
Arena Text14901489Gemini 3.7 Flash

Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).

Price, speed and latency

List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page; suite cost is what Artificial Analysis paid to run its whole Intelligence Index.

MeasureGemini 3.7 Flash (high)Muse Spark
Blended price per 1M tokens$1.50–
Input / output per 1M$0.750 / $3.75– / –
Cached input per 1M$0.075–
Price per task: chat reply$0.0014–
Price per task: summarize a 30-page report$0.011–
Price per task: code edit$0.010–
Price per task: agentic coding session$0.060–
Price per task: structured extraction$0.0023–
Output speed88 tok/s–
Time to first answer2.5 s–

Each model is compared at its best-ranked reasoning-effort setting. Gemini 3.7 Flash (high) scores 63.5 and takes 2 s to its first answer token; Gemini 3.7 Flash (medium) scores 63.1 in 2 s, Gemini 3.7 Flash (low) scores 60.3 in 2 s. Muse Spark scores 64.3. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.

Every benchmark

117 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.

MetricPublisherGemini 3.7 Flash (high)Muse Spark
BenchLeader IndexBenchLeader63.564.3
Agents & tools scoreBenchLeader47.666.1
Coding scoreBenchLeader62.965.0
Composite scoreBenchLeader73.264.5
Human preference scoreBenchLeader68.468.3
Knowledge scoreBenchLeader68.063.3
Long context scoreBenchLeader65.964.0
Maths scoreBenchLeader63.359.1
Multimodal scoreBenchLeader67.664.5
Reasoning scoreBenchLeader67.365.3
Instruction following scoreBenchLeader–77.3
Blended price $/MBenchLeader$1.50–
Output speedBenchLeader88 tok/s–
Time to first answerBenchLeader2.5 s–
Context windowBenchLeader1,048,576 tokens262,144 tokens
Reasoning
GPQA DiamondEpoch AI Benchmarking Hub94.8%89.8%
Humanity's Last ExamScale AI / CAIS–40.6%
Arena Hard PromptsArena (LMArena)15091506
LiveBench 2.0 ReasoningLiveBench87.8%†–
LiveBench 1.0 ReasoningLiveBench87.8%–
GPQA Diamond (AA)Artificial Analysis94.5%88.4%
Humanity's Last Exam (AA)Artificial Analysis47.9%40.7%
GPQA Diamond (Vals)Vals AI93.9%89.7%
ARC-AGI-1ARC Prize95.5%–
ARC-AGI-2ARC Prize84.6%–
CritPtArtificial Analysis14.3%11.3%
Chess PuzzlesEpoch AI Benchmarking Hub66.0%–
Mystery Game PuzzlesEpoch AI Benchmarking Hub37.0%–
LMCAEpoch AI Benchmarking Hub50.4%–
DTBenchEpoch AI Benchmarking Hub96.8%–
ForecastBenchForecastBench66.2%–
Coding
SciCodeSciCode57.2%51.5%
FrontierCodeCognition43.6%†–
Arena CodingArena (LMArena)15201529
Arena WebDevArena (LMArena)1590–
LiveBench 2.0 CodingLiveBench80.5%†–
LiveBench 1.0 CodingLiveBench78.9%–
SciCode (AA)Artificial Analysis57.2%–
LiveCodeBenchVals AI88.7%–
IOIVals AI67.8%–
Show all 117 benchmarks
MetricPublisherGemini 3.7 Flash (high)Muse Spark
Coding
SWE-bench (Vals)Vals AI80.8%74.4%
SWE-Bench ProScale AI SEAL–55.0%
Code MigrationVals AI34.8%–
ProgramBenchVals AI0.0%–
Vibe Code Bench v1.1Vals AI70.4%19.7%
SWE-Bench Pro (private)Scale AI SEAL–55.0%
SWE Atlas: Codebase QnAScale AI SEAL–24.2%
SWE Atlas: Test WritingScale AI SEAL–31.1%
DeepSWE v1.1Epoch AI Benchmarking Hub65.3%–
ALE-BenchEpoch AI Benchmarking Hub904.3–
FrontierSWEEpoch AI Benchmarking Hub20.3%–
Terminal-Bench 4.0 (AA)Artificial Analysis13.6%–
Terminal-Bench 2.1 (AA)Artificial Analysis85.8%62.2%
Agents & tools
Terminal-BenchTerminal-Bench11.2%–
Remote Labor IndexScale AI / CAIS5.0%†–
APEX-AgentsMercor67.8%†–
Arena AgentArena (LMArena)-1.3–
LiveBench Long AgenticLiveBench28.3%†–
LiveBench BrowserLiveBench56.0%†–
LiveBench Agentic CodingLiveBench58.3%–
Terminal-Bench HardArtificial Analysis–45.5%
τ²-Bench Telecom (AA)Artificial Analysis–91.5%
Terminal-Bench 2.1 (Vals)Vals AI77.5%–
MCP AtlasScale AI SEAL–82.2%
τ³-Banking (AA)Artificial Analysis32.8%–
Analyst Agent (AA)Artificial Analysis60.0%–
CyberBenchVals AI43.8%–
Finance Agent v2Vals AI59.0%–
Harvey's Legal Agent Benchmark v1Vals AI8.8%–
Legal Research BenchVals AI34.6%–
SkillsBenchVals AI65.9%–
SREBenchVals AI4.6%–
Tax Agent BenchVals AI22.8%–
Terminal-Bench 2.0 (Vals)Vals AI–59.5%
Terminal-Bench 4.0 (Vals)Vals AI12.1%–
Terminal-Bench ScienceVals AI7.1%–
AutomationBenchArtificial Analysis62.0%–
GDP.pdf (AA)Artificial Analysis23.6%–
EnterpriseOps-GymArtificial Analysis50.4%†–
FACTS SearchGoogle80.9%†–
Maths
FrontierMath Tiers 1–3Epoch AI Benchmarking Hub71.6%–
FrontierMath Tier 4Epoch AI Benchmarking Hub36.6%–
OTIS Mock AIMEEpoch AI Benchmarking Hub97.2%88.9%
ProofBenchVals AI58.0%†17.0%
LiveBench MathematicsLiveBench93.5%–
AIME (Vals)Vals AI–96.9%
Arena MathsArena (LMArena)15051467
Knowledge
SimpleQA VerifiedEpoch AI Benchmarking Hub69.2%–
LiveBench 2.0 Data AnalysisLiveBench68.0%†–
LiveBench ResearchLiveBench76.5%†–
LiveBench 1.0 Data AnalysisLiveBench68.0%–
AA-OmniscienceArtificial Analysis26.57.2
MMLU-ProVals AI90.1%87.3%
LegalBenchVals AI87.3%84.2%
CorpFinVals AI–65.1%
TaxEvalVals AI74.7%77.7%
PRBench FinanceScale AI SEAL–52.4%
PRBench LegalScale AI SEAL–52.3%
MultiNRCScale AI SEAL–59.0%
CaseLaw v2Vals AI–63.1%
Excel Modeling BenchmarkVals AI71.3%–
MedCodeVals AI53.4%51.3%
MedScribeVals AI83.9%85.9%
GDP.pdfEpoch AI Benchmarking Hub23.8%–
AA-Omniscience: accuracyArtificial Analysis55.3%49.6%
AA-Omniscience: non-hallucinationArtificial Analysis35.5%15.8%
Instruction following
LiveBench LanguageLiveBench85.5%–
LiveBench Instruction FollowingLiveBench79.9%–
IFBenchArtificial Analysis–75.9%
MultiChallengeScale AI SEAL–75.5%
TutorBenchScale AI SEAL–68.5%
Arena Instruction FollowingArena (LMArena)14871465
Human preference
Arena TextArena (LMArena)14901489
Arena Creative WritingArena (LMArena)14921464
Arena Multi-turnArena (LMArena)14891494
Arena Longer QueriesArena (LMArena)15011476
Multimodal
Arena VisionArena (LMArena)13141306
LiveBench DesignLiveBench73.2%†–
MMMU-ProArtificial Analysis85.5%80.5%
MMMU-Pro (official)MMMU–80.4%†
MMMU-Pro (Vals)Vals AI89.0%87.4%
MortgageTaxVals AI66.7%–
SAGEVals AI49.2%–
Long context
AA-LCRArtificial Analysis81.7%78.0%
Arena DocumentArena (LMArena)–1467
MLCRArtificial Analysis12.2%–
Composite
Epoch Capabilities IndexEpoch AI Benchmarking Hub158.0†152.0
LiveBench 2.0LiveBench67.2%†–
LiveBench 1.0LiveBench78.8%–
AA Intelligence Index v4.3.2Artificial Analysis39.131.3
Vals RSI IndexVals AI18.3%–
Safety & honesty
FORTRESSScale AI SEAL–20.2%

Data as of 2026-10-25. Best configuration of each model; every score links to its source on the model pages.

What changed

  • Gemini 3.7 Flash (high): 2 new results (Harvey's Legal Agent Benchmark v1, Vals RSI Index).
  • Gemini 3.7 Flash (high): input price moved from $0.38/M to $0.75/M (97% up); output price moved from $1.88/M to $3.75/M (99% up).
  • Gemini 3.7 Flash (high): 1 score revision; input price moved from $0.75/M to $0.38/M (49% down); output price moved from $3.75/M to $1.88/M (50% down).
  • Gemini 3.7 Flash (high): 1 new result (ForecastBench).
  • Gemini 3.7 Flash (high): BenchLeader Index 62.9 → 63.5.
  • Gemini 3.7 Flash (high): BenchLeader Index 60.8 → 62.9.
  • Gemini 3.7 Flash (high): 18 new results (AA Intelligence Index v4.3.2, GPQA Diamond (AA), Humanity's Last Exam (AA)…).
  • Gemini 3.7 Flash (high): BenchLeader Index 63.2 → 60.8.
  • Gemini 3.7 Flash (high): 2 score revisions.
  • Gemini 3.7 Flash (high): 1 score revision.

Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs

Gemini 3.7 Flash vs Muse Spark: questions

Is Gemini 3.7 Flash better than Muse Spark?
Gemini 3.7 Flash (high) and Muse Spark are neck and neck on quality (63.5 vs 64.3, within the ±4.7 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 2026-10-25, so check the category scores for your use.
Is Gemini 3.7 Flash better than Muse Spark for coding?
Muse Spark scores higher in coding (65 vs 63 on the category index, where 50 is average). On SciCode, the top shared coding benchmark, it is 57.2% for Gemini 3.7 Flash against 51.5% for Muse Spark.
Is Gemini 3.7 Flash better than Muse Spark for agents and tool use?
Muse Spark scores higher in agentic tasks (66 vs 48 on the category index, where 50 is average).
Which is better for long documents?
Gemini 3.7 Flash scores higher on long-context benchmarks (66 vs 64). Gemini 3.7 Flash accepts more context: 1,048,576 against 262,144 tokens for Muse Spark.
Do independent benchmarks agree on Gemini 3.7 Flash vs Muse Spark?
Across the 29 benchmarks both have, Gemini 3.7 Flash leads 25, Muse Spark leads 4. Every overall index that rates both puts Gemini 3.7 Flash ahead.
When was Gemini 3.7 Flash released?
Gemini 3.7 Flash was released on 13 Aug 2026.