Long documents
Prompts that approach the documented context limit
Gemini 3 Pro
Gemini 3 Pro has the larger documented context window.
Confidence: documented
Gemini 3 Pro vs Muse Spark 1.3
Updated October 17, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
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Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- Gemini 3 Pro only
- 26
- Muse Spark 1.3 only
- 19
- Like-for-like categories
- 0 / 8
Supported: Gemini 3 ProHow 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.
Chat turn cost
1K fresh input + 500 output tokens
Muse Spark 1.3
Muse Spark 1.3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Muse Spark 1.3
Muse Spark 1.3 has the lower estimated token cost for this stated workload. Gemini 3 Pro has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Muse Spark 1.3
Muse Spark 1.3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Muse Spark 1.3 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 3 Pro and Muse Spark 1.3 are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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.
43.0Gemini 3 Pro—Muse Spark 1.3
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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.
26 Gemini 3 Pro only
1 Shared
19 Muse Spark 1.3 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 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
Not comparable- Gemini 3 Pro
- Not ranked
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 9 public rows
- Reading
- Not comparable
Coding
Not comparable- Gemini 3 Pro
- 43.0
- Estimated · #59/150
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 1 vs 6 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 3 Pro
- 50.1
- Unranked · 4 rankable rows
- Muse Spark 1.3
- 82.9
- #6/28
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3 Pro
- 80.0
- #18/54
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Gemini 3 Pro
- 60.8
- Estimated · #47/184
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3 Pro
- Not ranked
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3 Pro
- 86.1
- #45/148
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3 Pro
- Not ranked
- Muse Spark 1.3
- 78.5
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | Gemini 3 Pro | Muse Spark 1.3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 9 public rows | Not comparable |
| Coding | 43.0Estimated · #59/150 | Not ranked | Not comparableBenchAlign v5.8 lane · 1 vs 6 public rows | Not comparable |
| Reasoning | 50.1Unranked · 4 rankable rows | 82.9#6/28 | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 80.0#18/54 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Knowledge | 60.8Estimated · #47/184 | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 86.1#45/148 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 78.5Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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.
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 Pro
- $0.008
- Fits in one request
- Muse Spark 1.3
- $0.00338
- Fits in one request
Muse Spark 1.3 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 3 Pro
- $0.136
- Fits in one request
- Muse Spark 1.3
- $0.07525
- Fits in one request
Muse Spark 1.3 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 Pro
- $0.56
- Fits in one request
- Cached input priced at the published list-input rate
- Muse Spark 1.3
- $0.0975
- Fits in one request
Muse Spark 1.3 has the lower modeled cost
Gemini 3 Pro has no published cached-input rate, so cached tokens use its listed input 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 ProMuse Spark 1.3
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3 Pro
2M
Muse Spark 1.3
API model ID
Gemini 3 Pro
Not sourced
Muse Spark 1.3
muse-spark-1.3
Meta Muse Spark 1.3 model pageCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3 Pro
Not published
Muse Spark 1.3
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.3 model pageDocumented inputs
Gemini 3 Pro
Not sourced
Muse Spark 1.3
text, image, video, document
Meta Muse Spark 1.3 model pageDocumented outputs
Gemini 3 Pro
Not sourced
Muse Spark 1.3
Provider availability
Gemini 3 Pro
Not sourced
Muse Spark 1.3
Generally Available · Meta Model API, Muse Code
Meta AI Research Muse Spark 1.3 launchReasoning profile
Gemini 3 Pro
Non-Reasoning
Muse Spark 1.3
Reasoning
Weight access
Gemini 3 Pro
Proprietary
Muse Spark 1.3
Proprietary
License
Gemini 3 Pro
Proprietary
Muse Spark 1.3
Proprietary
Release date
Gemini 3 Pro
2025-11-18
Muse Spark 1.3
2026-09-02
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.136 vs $0.07525. Cache-heavy agent loop: $0.56 vs $0.0975.
- Context tradeoff
- Gemini 3 Pro has the larger documented window (2M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Gemini 3 Pro or Muse Spark 1.3?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Gemini 3 Pro or Muse Spark 1.3?
Muse Spark 1.3 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Gemini 3 Pro or Muse Spark 1.3?
Gemini 3 Pro and Muse Spark 1.3 are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Gemini 3 Pro or Muse Spark 1.3?
For the stated presets, chat costs $0.008 on Gemini 3 Pro and $0.00337 on Muse Spark 1.3; repository review costs $0.136 and $0.07525; the cache-heavy agent loop costs $0.56 and $0.0975. Gemini 3 Pro has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Gemini 3 Pro or Muse Spark 1.3?
Gemini 3 Pro has the larger documented context window: 2M, compared with 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 evidence46 rows
Agentic
Gert Labs
Gemini 3 Pro63.23%SourceMuse Spark 1.3—Not directly comparable
JobBench
Gemini 3 Pro11.4%SourceMuse Spark 1.364.9%SourceMuse Spark 1.3 leads this result
Terminal-Bench 2.1
Gemini 3 Pro—Muse Spark 1.388.8%SourceNot directly comparable
OSWorld 2.0
Gemini 3 Pro—Muse Spark 1.366.9%SourceNot directly comparable
DeepSearchQA
Gemini 3 Pro—Muse Spark 1.389.4%SourceNot directly comparable
AutomationBench
Gemini 3 Pro—Muse Spark 1.349.4%SourceNot directly comparable
ApprenticeBench
Gemini 3 Pro—Muse Spark 1.319%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3 Pro—Muse Spark 1.372.3%SourceNot directly comparable
CWE-bench v1
Gemini 3 Pro—Muse Spark 1.355.0%SourceNot directly comparable
MazeBench
Gemini 3 Pro—Muse Spark 1.30%SourceNot directly comparable
Coding
Vibe Code Bench
Gemini 3 Pro14.30%SourceMuse Spark 1.3—Not directly comparable
Terminal-Bench 2.1
Gemini 3 Pro—Muse Spark 1.388.8%SourceNot directly comparable
DeepSWE
Gemini 3 Pro—Muse Spark 1.375.4%SourceNot directly comparable
SWE-Atlas Codebase QnA
Gemini 3 Pro—Muse Spark 1.359.4%SourceNot directly comparable
CursorBench 4.0
Gemini 3 Pro—Muse Spark 1.341.6%SourceNot directly comparable
Bug Hunt Bench
Gemini 3 Pro—Muse Spark 1.332.2 fixesSourceNot directly comparable
FrontierSWE v2
Gemini 3 Pro—Muse Spark 1.325.8%SourceNot directly comparable
Reasoning
Multimodal
MMMU
Gemini 3 Pro87.2%SourceMuse Spark 1.3—Not directly comparable
MMMU-Pro
Gemini 3 Pro81%SourceMuse Spark 1.3—Not directly comparable
RealWorldQA
Gemini 3 Pro83.3%SourceMuse Spark 1.3—Not directly comparable
OmniDocBench 1.5
Gemini 3 Pro88.5%SourceMuse Spark 1.3—Not directly comparable
Video-MME (with subtitle)
Gemini 3 Pro88.4%SourceMuse Spark 1.3—Not directly comparable
Video-MME (w/o subtitle)
Gemini 3 Pro87.7%SourceMuse Spark 1.3—Not directly comparable
MathVision
Gemini 3 Pro86.6%SourceMuse Spark 1.3—Not directly comparable
We-Math
Gemini 3 Pro86.9%SourceMuse Spark 1.3—Not directly comparable
DynaMath
Gemini 3 Pro85.1%SourceMuse Spark 1.3—Not directly comparable
SimpleVQA
Gemini 3 Pro73.2%SourceMuse Spark 1.3—Not directly comparable
MMLongBench-Doc
Gemini 3 Pro60.5%SourceMuse Spark 1.3—Not directly comparable
CC-OCR
Gemini 3 Pro79.0%SourceMuse Spark 1.3—Not directly comparable
AI2D_TEST
Gemini 3 Pro94.1%SourceMuse Spark 1.3—Not directly comparable
CountBench
Gemini 3 Pro97.3%SourceMuse Spark 1.3—Not directly comparable
RefCOCO (avg)
Gemini 3 Pro84.1%SourceMuse Spark 1.3—Not directly comparable
ODINW13
Gemini 3 Pro46.3%SourceMuse Spark 1.3—Not directly comparable
ERQA
Gemini 3 Pro70.5%SourceMuse Spark 1.3—Not directly comparable
VideoMMMU
Gemini 3 Pro87.6%SourceMuse Spark 1.3—Not directly comparable
MLVU (M-Avg)
Gemini 3 Pro83.0%SourceMuse Spark 1.3—Not directly comparable
ScreenSpot Pro
Gemini 3 Pro72.7%SourceMuse Spark 1.3—Not directly comparable
CharXiv
Gemini 3 Pro81.4%SourceMuse Spark 1.3—Not directly comparable
V*
Gemini 3 Pro88.0%SourceMuse Spark 1.3—Not directly comparable
Instruction following
Gray Swan IPI (15 attempts)
Gemini 3 Pro—Muse Spark 1.315.9%SourceNot directly comparable
Math
46 public results · 1 shared
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Last updated October 17, 2026