Coding work
Code generation, repair, and software-engineering tasks
Gemini 3.7 Flash
Gemini 3.7 Flash leads on the public coding lane, 60.3 to 56.3, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Gemini 3.7 Flash vs Muse Spark 1.1
Updated September 27, 2026. Rank says Gemini 3.7 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
Gemini 3.7 Flash has the higher public score estimate, 67.66 versus 65.92, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 9 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 9
- Gemini 3.7 Flash only
- 16
- Muse Spark 1.1 only
- 17
- Like-for-like categories
- 3 / 8
Supported: Gemini 3.7 Flash and Muse Spark 1.1How the comparison works
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Agentic work
Tool use, computer use, and multi-step task completion
Gemini 3.7 Flash
Gemini 3.7 Flash leads on the public agentic lane, 58.6 to 57.7, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Show secondary and unsupported calls
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedChat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
60.3Gemini 3.7 Flash56.3Muse Spark 1.1
Like-for-like · BenchAlign v5.7
Gemini 3.7 Flash leads the like-for-like coding row, although the 90% intervals overlap.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
16 Gemini 3.7 Flash only
9 Shared
17 Muse Spark 1.1 only
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
OSWorld 2.0Agentic
Normalized gap 33.7Gemini 3.7 Flash:47.9%Muse Spark 1.1:14.2%LiveCodeBench (Vals)Coding
Normalized gap 2.8Gemini 3.7 Flash:88.7%Muse Spark 1.1:85.9%MMLU-Pro (Vals)Knowledge
Normalized gap 1.4Gemini 3.7 Flash:90.1%Muse Spark 1.1:88.7%CharXivMultimodal
Normalized gap 0.3Gemini 3.7 Flash:88.7%Muse Spark 1.1:88.4%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Agentic
Like-for-like- Gemini 3.7 Flash
- 58.6
- Supported · #20/105
- Muse Spark 1.1
- 57.7
- Supported · #22/105
- Basis
- BenchAlign v5.7 lane · 7 vs 14 public rows
- Reading
- Gemini 3.7 Flash leads · intervals overlap
Coding
Like-for-like- Gemini 3.7 Flash
- 60.3
- Supported · #17/135
- Muse Spark 1.1
- 56.3
- Supported · #23/135
- Basis
- BenchAlign v5.7 lane · 6 vs 4 public rows
- Reading
- Gemini 3.7 Flash leads · intervals overlap
Knowledge
Like-for-like- Gemini 3.7 Flash
- 71.1
- Supported · #10/158
- Muse Spark 1.1
- 68.2
- Supported · #15/158
- Basis
- BenchAlign v5.7 lane · 6 vs 5 public rows
- Reading
- Gemini 3.7 Flash leads · intervals overlap
Reasoning
Not comparable- Gemini 3.7 Flash
- 77.8
- Unranked · 5 rankable rows
- Muse Spark 1.1
- 75.5
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3.7 Flash
- 82.6
- #10/50
- Muse Spark 1.1
- 77.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.7 Flash
- Not ranked
- Muse Spark 1.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.7 Flash
- Not ranked
- Muse Spark 1.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.7 Flash
- Not ranked
- Muse Spark 1.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.7 Flash | Muse Spark 1.1 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 58.6Supported · #20/105 | 57.7Supported · #22/105 | Like-for-likeBenchAlign v5.7 lane · 7 vs 14 public rows | Gemini 3.7 Flash leads · intervals overlap |
| Coding | 60.3Supported · #17/135 | 56.3Supported · #23/135 | Like-for-likeBenchAlign v5.7 lane · 6 vs 4 public rows | Gemini 3.7 Flash leads · intervals overlap |
| Knowledge | 71.1Supported · #10/158 | 68.2Supported · #15/158 | Like-for-likeBenchAlign v5.7 lane · 6 vs 5 public rows | Gemini 3.7 Flash leads · intervals overlap |
| Reasoning | 77.8Unranked · 5 rankable rows | 75.5Unranked · 3 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 82.6#10/50 | 77.3Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- Gemini 3.7 Flash
- $0.00262
- Fits in one request
- Muse Spark 1.1
- API rate not published
- Fits in one request
Muse Spark 1.1 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemini 3.7 Flash
- $0.04875
- Fits in one request
- Muse Spark 1.1
- API rate not published
- Fits in one request
Muse Spark 1.1 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemini 3.7 Flash
- $0.0675
- Fits in one request
- Muse Spark 1.1
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Muse Spark 1.1 has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemini 3.7 FlashMuse Spark 1.1
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.7 Flash
Muse Spark 1.1
1M
API model ID
Gemini 3.7 Flash
gemini-3.7-flash
Google Gemini 3.7 Flash API documentationMuse Spark 1.1
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.7 Flash
$0.075 per 1M cached input tokens
Google Gemini API pricingMuse Spark 1.1
No comparable hosted API rate
Documented inputs
Gemini 3.7 Flash
text, image, video, audio, pdf
Google Gemini 3.7 Flash API documentationMuse Spark 1.1
Not sourced
Documented outputs
Gemini 3.7 Flash
Muse Spark 1.1
Not sourced
Provider availability
Gemini 3.7 Flash
Generally Available · Gemini API, Google AI Studio, Gemini App - Spark, Gemini Enterprise App, Gemini Enterprise Agent Platform, Google Antigravity
Google DeepMind Gemini 3.7 Flash model cardMuse Spark 1.1
Not sourced
Reasoning profile
Gemini 3.7 Flash
Reasoning
Muse Spark 1.1
Reasoning
Weight access
Gemini 3.7 Flash
Proprietary
Muse Spark 1.1
Proprietary
License
Gemini 3.7 Flash
Proprietary
Muse Spark 1.1
Proprietary
Release date
Gemini 3.7 Flash
2026-08-13
Muse Spark 1.1
2026-07-09
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- Gemini 3.7 Flash has the higher public score estimate, 67.66 versus 65.92, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Gemini 3.7 Flash or Muse Spark 1.1?
Gemini 3.7 Flash has the higher public score estimate, 67.66 versus 65.92, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Gemini 3.7 Flash or Muse Spark 1.1?
Gemini 3.7 Flash leads the public coding lane, 60.3 to 56.3, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Gemini 3.7 Flash or Muse Spark 1.1?
Gemini 3.7 Flash leads the public agentic tasks lane, 58.6 to 57.7, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Gemini 3.7 Flash or Muse Spark 1.1?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, Gemini 3.7 Flash or Muse Spark 1.1?
Both models list the same context window, 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence42 rows
Agentic
Terminal-Bench 2.1
Gemini 3.7 Flash85.8%SourceMuse Spark 1.180.0%SourceGemini 3.7 Flash leads this result
Terminal-Bench 3.0
Gemini 3.7 Flash14.9%SourceMuse Spark 1.1—Not directly comparable
AutomationBench
Gemini 3.7 Flash30.4%SourceMuse Spark 1.1—Not directly comparable
OSWorld 2.0
Gemini 3.7 Flash47.9%SourceMuse Spark 1.114.2%SourceGemini 3.7 Flash leads this result
Agents' Last Exam
Gemini 3.7 Flash26.3%SourceMuse Spark 1.1—Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.7 Flash77.5%SourceMuse Spark 1.169.3%SourceGemini 3.7 Flash leads this result
ApprenticeBench
Gemini 3.7 Flash16%SourceMuse Spark 1.1—Not directly comparable
MCP Atlas
Gemini 3.7 Flash—Muse Spark 1.188.1%SourceNot directly comparable
Toolathlon
Gemini 3.7 Flash—Muse Spark 1.175.6%SourceNot directly comparable
OSWorld-Verified
Gemini 3.7 Flash—Muse Spark 1.180.8%SourceNot directly comparable
WebArena-Verified
Gemini 3.7 Flash—Muse Spark 1.169%SourceNot directly comparable
DeepSearchQA
Gemini 3.7 Flash—Muse Spark 1.184.9%SourceNot directly comparable
CyberGym
Gemini 3.7 Flash—Muse Spark 1.159.0%SourceNot directly comparable
Finance Agent v2
Gemini 3.7 Flash—Muse Spark 1.157.2%SourceNot directly comparable
deepSwe
Gemini 3.7 Flash—Muse Spark 1.153.3%SourceNot directly comparable
JobBench
Gemini 3.7 Flash—Muse Spark 1.154.7%SourceNot directly comparable
Cybench
Gemini 3.7 Flash—Muse Spark 1.192.9%SourceNot directly comparable
ExploitGym
Gemini 3.7 Flash—Muse Spark 1.10.8%SourceNot directly comparable
Coding
FrontierCode 1.1 Main
Gemini 3.7 Flash43.6%SourceMuse Spark 1.1—Not directly comparable
DeepSWE
Gemini 3.7 Flash65.3%SourceMuse Spark 1.1—Not directly comparable
Terminal-Bench 2.1
Gemini 3.7 Flash85.8%SourceMuse Spark 1.180.0%SourceGemini 3.7 Flash leads this result
FrontierSWE v2
Gemini 3.7 Flash20.3%SourceMuse Spark 1.1—Not directly comparable
LiveCodeBench (Vals)
Gemini 3.7 Flash88.7%SourceMuse Spark 1.185.9%SourceGemini 3.7 Flash leads this result
SWE-bench (Vals)
Gemini 3.7 Flash80.8%SourceMuse Spark 1.182.0%SourceMuse Spark 1.1 leads this result
SWE-bench Pro
Gemini 3.7 Flash—Muse Spark 1.161.5%SourceNot directly comparable
Reasoning
MRCR v2 64K-128K
Gemini 3.7 Flash97%SourceMuse Spark 1.1—Not directly comparable
ARC-AGI-1
Gemini 3.7 Flash95.50%SourceMuse Spark 1.1—Not directly comparable
ARC-AGI-2
Gemini 3.7 Flash84.6%SourceMuse Spark 1.1—Not directly comparable
MRCR 1M
Gemini 3.7 Flash—Muse Spark 1.154.1%SourceNot directly comparable
Multimodal
CharXiv w/o tools
Gemini 3.7 Flash84.5%SourceMuse Spark 1.1—Not directly comparable
CharXiv
Gemini 3.7 Flash88.7%SourceMuse Spark 1.188.4%SourceGemini 3.7 Flash leads this result
LVBench
Gemini 3.7 Flash85.4%SourceMuse Spark 1.1—Not directly comparable
BabyVision
Gemini 3.7 Flash—Muse Spark 1.176.3%SourceNot directly comparable
Knowledge
HLE-Verified
Gemini 3.7 Flash53.6%SourceMuse Spark 1.1—Not directly comparable
LABBench2
Gemini 3.7 Flash82.1%SourceMuse Spark 1.1—Not directly comparable
BioMysteryBench (human-solvable)
Gemini 3.7 Flash87.1%SourceMuse Spark 1.1—Not directly comparable
BioMysteryBench (human-difficult)
Gemini 3.7 Flash43.5%SourceMuse Spark 1.1—Not directly comparable
GPQA Diamond (Vals)
Gemini 3.7 Flash93.9%SourceMuse Spark 1.191.2%SourceGemini 3.7 Flash leads this result
MMLU-Pro (Vals)
Gemini 3.7 Flash90.1%SourceMuse Spark 1.188.7%SourceGemini 3.7 Flash leads this result
HLE
Gemini 3.7 Flash—Muse Spark 1.162.1%SourceNot directly comparable
HLE w/o tools
Gemini 3.7 Flash—Muse Spark 1.152.2%SourceNot directly comparable
HealthBench Professional
Gemini 3.7 Flash—Muse Spark 1.159.3%SourceNot directly comparable
42 public results · 9 shared
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Last updated September 27, 2026