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 Open
Updated October 15, 2026. Rank says Muse Spark Open 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
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Decision reading
Muse Spark Open has the higher public point estimate, 62.66 versus 61.17. Their conditional score ranges overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- Gemini 3 Pro only
- 25
- Muse Spark Open only
- 5
- Like-for-like categories
- 0 / 8
Supported: Gemini 3 Pro · Estimated: Muse Spark Open. Conditional ranges do not establish rank confidence.How 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Gemini 3 Pro is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 3 Pro is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat 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: rate-fallbackRepository 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.
43.0Gemini 3 Pro56.1Muse Spark Open
Directional only · BenchAlign v5.8
Muse Spark Open has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
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.
25 Gemini 3 Pro only
2 Shared
5 Muse Spark Open only
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Coding
Directional only- Gemini 3 Pro
- 43.0
- Estimated · #59/150
- Muse Spark Open
- 56.1
- Supported · #29/150
- Basis
- BenchAlign v5.8 lane · 1 vs 0 public rows
- Reading
- Directional only
Knowledge
Directional only- Gemini 3 Pro
- 60.7
- Estimated · #47/184
- Muse Spark Open
- 64.6
- Supported · #38/184
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Directional only
Agentic
Not comparable- Gemini 3 Pro
- Not ranked
- Muse Spark Open
- 60.2
- Estimated · #27/127
- Basis
- BenchAlign v5.8 lane · 2 vs 4 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 3 Pro
- 50.1
- Unranked · 4 rankable rows
- Muse Spark Open
- 79.6
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3 Pro
- 80.0
- #18/54
- Muse Spark Open
- 83.6
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3 Pro
- Not ranked
- Muse Spark Open
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3 Pro
- 86.1
- #43/146
- Muse Spark Open
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3 Pro
- Not ranked
- Muse Spark Open
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3 Pro | Muse Spark Open | Basis | Reading |
|---|---|---|---|---|
| Coding | 43.0Estimated · #59/150 | 56.1Supported · #29/150 | Directional onlyBenchAlign v5.8 lane · 1 vs 0 public rows | Directional only |
| Knowledge | 60.7Estimated · #47/184 | 64.6Supported · #38/184 | Directional onlyBenchAlign v5.8 lane · 0 vs 0 public rows | Directional only |
| Agentic | Not ranked | 60.2Estimated · #27/127 | Not comparableBenchAlign v5.8 lane · 2 vs 4 public rows | Not comparable |
| Reasoning | 50.1Unranked · 4 rankable rows | 79.6Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 80.0#18/54 | 83.6Unranked · 4 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 86.1#43/146 | 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 v5.8) 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.
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 Pro
- $0.008
- Fits in one request
- Muse Spark Open
- API rate not published
- Fits in one request
Muse Spark Open has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemini 3 Pro
- $0.136
- Fits in one request
- Muse Spark Open
- API rate not published
- Fits in one request
Muse Spark Open has no comparable published API token rate.
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 Open
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Gemini 3 Pro has no published cached-input rate, so cached tokens use its listed input rate. Muse Spark Open 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 ProMuse Spark Open
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3 Pro
2M
Muse Spark Open
API model ID
Gemini 3 Pro
Not sourced
Muse Spark Open
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 Pro
Not published
Muse Spark Open
No comparable hosted API rate
Meta AI Research blogDocumented inputs
Gemini 3 Pro
Not sourced
Muse Spark Open
text, image, video, audio
Meta Muse Spark Open model cardDocumented outputs
Gemini 3 Pro
Not sourced
Muse Spark Open
Provider availability
Gemini 3 Pro
Not sourced
Muse Spark Open
Open Weights · Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, Microsoft Foundry Models, self-hosted inference
Meta AI Research: Muse Spark OpenReasoning profile
Gemini 3 Pro
Non-Reasoning
Muse Spark Open
Reasoning
Weight access
Gemini 3 Pro
Proprietary
Muse Spark Open
Open Weight
License
Gemini 3 Pro
Proprietary
Muse Spark Open
Open Weight
Release date
Gemini 3 Pro
2025-11-18
Muse Spark Open
2026-10-15
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 Open has the higher public point estimate, 62.66 versus 61.17. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- 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 Open?
Muse Spark Open has the higher public point estimate, 62.66 versus 61.17. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Gemini 3 Pro or Muse Spark Open?
Muse Spark Open scores higher for coding on the public lane, 56.1 to 43. Gemini 3 Pro is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Gemini 3 Pro or Muse Spark Open?
Gemini 3 Pro is 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 Open?
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 Pro or Muse Spark Open?
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 evidence32 rows
Agentic
Gert Labs
Gemini 3 Pro63.23%SourceMuse Spark Open—Not directly comparable
JobBench
Gemini 3 Pro11.4%SourceMuse Spark Open61.8%SourceMuse Spark Open leads this result
MCP Atlas
Gemini 3 Pro—Muse Spark Open89.2%SourceNot directly comparable
HLE w/ tools
Gemini 3 Pro—Muse Spark Open61.0%SourceNot directly comparable
Toolathlon-Verified
Gemini 3 Pro—Muse Spark Open72.8%SourceNot directly comparable
Coding
Vibe Code Bench
Gemini 3 Pro14.30%SourceMuse Spark Open—Not directly comparable
Reasoning
Multimodal
MMMU
Gemini 3 Pro87.2%SourceMuse Spark Open—Not directly comparable
MMMU-Pro
Gemini 3 Pro81%SourceMuse Spark Open—Not directly comparable
RealWorldQA
Gemini 3 Pro83.3%SourceMuse Spark Open—Not directly comparable
OmniDocBench 1.5
Gemini 3 Pro88.5%SourceMuse Spark Open—Not directly comparable
Video-MME (with subtitle)
Gemini 3 Pro88.4%SourceMuse Spark Open—Not directly comparable
Video-MME (w/o subtitle)
Gemini 3 Pro87.7%SourceMuse Spark Open—Not directly comparable
MathVision
Gemini 3 Pro86.6%SourceMuse Spark Open—Not directly comparable
We-Math
Gemini 3 Pro86.9%SourceMuse Spark Open—Not directly comparable
DynaMath
Gemini 3 Pro85.1%SourceMuse Spark Open—Not directly comparable
SimpleVQA
Gemini 3 Pro73.2%SourceMuse Spark Open73.6%SourceMuse Spark Open leads this result
MMLongBench-Doc
Gemini 3 Pro60.5%SourceMuse Spark Open—Not directly comparable
CC-OCR
Gemini 3 Pro79.0%SourceMuse Spark Open—Not directly comparable
AI2D_TEST
Gemini 3 Pro94.1%SourceMuse Spark Open—Not directly comparable
CountBench
Gemini 3 Pro97.3%SourceMuse Spark Open—Not directly comparable
RefCOCO (avg)
Gemini 3 Pro84.1%SourceMuse Spark Open—Not directly comparable
ODINW13
Gemini 3 Pro46.3%SourceMuse Spark Open—Not directly comparable
ERQA
Gemini 3 Pro70.5%SourceMuse Spark Open—Not directly comparable
VideoMMMU
Gemini 3 Pro87.6%SourceMuse Spark Open—Not directly comparable
MLVU (M-Avg)
Gemini 3 Pro83.0%SourceMuse Spark Open—Not directly comparable
ScreenSpot Pro
Gemini 3 Pro72.7%SourceMuse Spark Open—Not directly comparable
CharXiv
Gemini 3 Pro81.4%SourceMuse Spark Open—Not directly comparable
V*
Gemini 3 Pro88.0%SourceMuse Spark Open—Not directly comparable
ZeroBench
Gemini 3 Pro—Muse Spark Open50.0%SourceNot directly comparable
BabyVision
Gemini 3 Pro—Muse Spark Open80.2%SourceNot directly comparable
32 public results · 2 shared
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Last updated October 15, 2026