Coding
Like-for-like- Gemini 3.5 Flash-Lite
- 43.2
- Supported · #104/154
- Muse Spark 1.2
- 60.0
- Supported · #21/154
- Basis
- BenchAlign lane · 4 vs 5 public rows
- Reading
- Muse Spark 1.2 leads · intervals overlap
Gemini 3.5 Flash-Lite vs Muse Spark 1.2
Decision reading
Muse Spark 1.2 has the higher public score estimate, 70.28 versus 58.78, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Updated September 18, 2026. Rank says Muse Spark 1.2 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
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.
Coding work
Code generation, repair, and software-engineering tasks
Muse Spark 1.2
Muse Spark 1.2 leads on the public coding lane, 60 to 43.2, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 3.5 Flash-Lite is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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.
43.2Gemini 3.5 Flash-Lite60.0Muse Spark 1.2
Like-for-like · BenchAlign
Muse Spark 1.2 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.
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
7 Gemini 3.5 Flash-Lite only
3 Shared
5 Muse Spark 1.2 only
- Shared results
- 3
- Gemini 3.5 Flash-Lite only
- 7
- Muse Spark 1.2 only
- 5
- Like-for-like categories
- 1 / 8
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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
Directional only- Gemini 3.5 Flash-Lite
- 43.2
- Estimated · #103/154
- Muse Spark 1.2
- 61.0
- Supported · #16/154
- Basis
- BenchAlign lane · 3 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- Gemini 3.5 Flash-Lite
- 52.0
- Supported · #69/184
- Muse Spark 1.2
- 70.7
- Estimated · #10/184
- Basis
- BenchAlign lane · 2 vs 1 public rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 3.5 Flash-Lite
- 60.1
- Unranked · 3 rankable rows
- Muse Spark 1.2
- 75.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- Muse Spark 1.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- Muse Spark 1.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3.5 Flash-Lite
- 76.4
- #16/48
- Muse Spark 1.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- Muse Spark 1.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.5 Flash-Lite | Muse Spark 1.2 | Basis | Reading |
|---|---|---|---|---|
| Coding | 43.2Supported · #104/154 | 60.0Supported · #21/154 | Like-for-likeBenchAlign lane · 4 vs 5 public rows | Muse Spark 1.2 leads · intervals overlap |
| Agentic | 43.2Estimated · #103/154 | 61.0Supported · #16/154 | Directional onlyBenchAlign lane · 3 vs 2 public rows | Directional only |
| Knowledge | 52.0Supported · #69/184 | 70.7Estimated · #10/184 | Directional onlyBenchAlign lane · 2 vs 1 public rows | Directional only |
| Reasoning | 60.1Unranked · 3 rankable rows | 75.3Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 76.4#16/48 | 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 |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
MMLU-Pro (Vals)
Knowledge
Gemini 3.5 Flash-Lite: 85.8%Muse Spark 1.2: 88.3%Normalized gap 2.5Gemini 3.5 Flash-Lite source Muse Spark 1.2 source
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 3.5 Flash-Lite
- $0.00155
- Fits in one request
- Muse Spark 1.2
- $0.00338
- Fits in one request
Gemini 3.5 Flash-Lite has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 3.5 Flash-Lite
- $0.0225
- Fits in one request
- Muse Spark 1.2
- $0.07525
- Fits in one request
Gemini 3.5 Flash-Lite has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemini 3.5 Flash-Lite
- $0.037
- Fits in one request
- Muse Spark 1.2
- $0.0975
- Fits in one request
Gemini 3.5 Flash-Lite has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemini 3.5 Flash-LiteMuse Spark 1.2
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.5 Flash-Lite
Muse Spark 1.2
API model ID
Gemini 3.5 Flash-Lite
gemini-3.5-flash-lite
Google Gemini 3.5 Flash-Lite model documentationMuse Spark 1.2
muse-spark-1.2
Meta: Muse Spark 1.2 model pageCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.5 Flash-Lite
$0.03 per 1M cached input tokens
Google Gemini API pricingMuse Spark 1.2
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.2 model pageDocumented inputs
Gemini 3.5 Flash-Lite
text, image, video, audio, pdf
Google Gemini 3.5 Flash-Lite model documentationMuse Spark 1.2
Not sourced
Documented outputs
Gemini 3.5 Flash-Lite
Muse Spark 1.2
Not sourced
Provider availability
Gemini 3.5 Flash-Lite
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideMuse Spark 1.2
Not sourced
Reasoning profile
Gemini 3.5 Flash-Lite
Reasoning
Muse Spark 1.2
Reasoning
Weight access
Gemini 3.5 Flash-Lite
Proprietary
Muse Spark 1.2
Proprietary
License
Gemini 3.5 Flash-Lite
Proprietary
Muse Spark 1.2
Proprietary
Release date
Gemini 3.5 Flash-Lite
2026-07-21
Muse Spark 1.2
2026-08-05
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
- Muse Spark 1.2 has the higher public score estimate, 70.28 versus 58.78, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0225 vs $0.07525. Cache-heavy agent loop: $0.037 vs $0.0975.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence15 rows
Agentic
Terminal-Bench 2.0
Gemini 3.5 Flash-Lite54%SourceMuse Spark 1.2—Not directly comparable
OSWorld-Verified
Gemini 3.5 Flash-Lite74%SourceMuse Spark 1.2—Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.5 Flash-Lite50.2%SourceMuse Spark 1.269.7%SourceMuse Spark 1.2 leads this result
Terminal-Bench 2.1
Gemini 3.5 Flash-Lite—Muse Spark 1.282.9%SourceNot directly comparable
Coding
Terminal-Bench 2.0
Gemini 3.5 Flash-Lite54.0%SourceMuse Spark 1.2—Not directly comparable
SWE-bench Pro
Gemini 3.5 Flash-Lite54.2%SourceMuse Spark 1.2—Not directly comparable
LiveCodeBench (Vals)
Gemini 3.5 Flash-Lite79.0%SourceMuse Spark 1.2—Not directly comparable
SWE-bench (Vals)
Gemini 3.5 Flash-Lite75.0%SourceMuse Spark 1.286.6%SourceMuse Spark 1.2 leads this result
Terminal-Bench 2.1
Gemini 3.5 Flash-Lite—Muse Spark 1.282.9%SourceNot directly comparable
DeepSWE
Gemini 3.5 Flash-Lite—Muse Spark 1.259.3%SourceNot directly comparable
VulcanBench v3
Gemini 3.5 Flash-Lite—Muse Spark 1.287.0%SourceNot directly comparable
FrontierSWE v2
Gemini 3.5 Flash-Lite—Muse Spark 1.212.0%SourceNot directly comparable
Reasoning
MRCRv2
Gemini 3.5 Flash-Lite72.2%SourceMuse Spark 1.2—Not directly comparable
Knowledge
Questions
Which is better, Gemini 3.5 Flash-Lite or Muse Spark 1.2?
Muse Spark 1.2 has the higher public score estimate, 70.28 versus 58.78, 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 3.5 Flash-Lite or Muse Spark 1.2?
Muse Spark 1.2 leads the public coding lane, 60 to 43.2, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Gemini 3.5 Flash-Lite or Muse Spark 1.2?
Muse Spark 1.2 scores higher for agentic tasks on the public lane, 61 to 43.2. Gemini 3.5 Flash-Lite is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Gemini 3.5 Flash-Lite or Muse Spark 1.2?
For the stated presets, chat costs $0.00155 on Gemini 3.5 Flash-Lite and $0.00337 on Muse Spark 1.2; repository review costs $0.0225 and $0.07525; the cache-heavy agent loop costs $0.037 and $0.0975. Costs use the listed standard API rates.
Which has the larger context window, Gemini 3.5 Flash-Lite or Muse Spark 1.2?
Both models list the same context window, 1M.
Related comparisons
Last updated September 18, 2026
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