Long documents
Prompts that approach the documented context limit
Muse Wonder
Muse Wonder has the larger documented context window.
Confidence: documented
Ling 3.0 Flash Sante vs Muse Wonder
Updated October 20, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Share or export
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
- Ling 3.0 Flash Sante only
- 5
- Muse Wonder only
- 8
- Like-for-like categories
- 0 / 8
Estimated: Muse Wonder. 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
Ling 3.0 Flash Sante 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
Ling 3.0 Flash Sante and Muse Wonder are 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: 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.
—Ling 3.0 Flash Sante70.1Muse Wonder
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.
5 Ling 3.0 Flash Sante only
1 Shared
8 Muse Wonder 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- Ling 3.0 Flash Sante
- Not ranked
- Muse Wonder
- Not ranked
- Basis
- BenchAlign v5.8 lane · 4 vs 3 public rows
- Reading
- Not comparable
Coding
Not comparable- Ling 3.0 Flash Sante
- Not ranked
- Muse Wonder
- 70.1
- Supported · #10/153
- Basis
- BenchAlign v5.8 lane · 0 vs 4 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Ling 3.0 Flash Sante
- 73.4
- Unranked · 1 rankable row
- Muse Wonder
- 80.4
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Ling 3.0 Flash Sante
- Not ranked
- Muse Wonder
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Ling 3.0 Flash Sante
- Not ranked
- Muse Wonder
- 74.9
- Supported · #12/187
- Basis
- BenchAlign v5.8 lane · 2 vs 1 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Ling 3.0 Flash Sante
- Not ranked
- Muse Wonder
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Ling 3.0 Flash Sante
- Not ranked
- Muse Wonder
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Ling 3.0 Flash Sante
- Not ranked
- Muse Wonder
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Ling 3.0 Flash Sante | Muse Wonder | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 3 public rows | Not comparable |
| Coding | Not ranked | 70.1Supported · #10/153 | Not comparableBenchAlign v5.8 lane · 0 vs 4 public rows | Not comparable |
| Reasoning | 73.4Unranked · 1 rankable row | 80.4Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 74.9Supported · #12/187 | Not comparableBenchAlign v5.8 lane · 2 vs 1 public 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 v5.8) 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
- Ling 3.0 Flash Sante
- Self-hosted; infrastructure cost varies
- Fits in one request
- Muse Wonder
- $0.014
- Fits in one request
Ling 3.0 Flash Sante has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Ling 3.0 Flash Sante
- Self-hosted; infrastructure cost varies
- Fits in one request
- Muse Wonder
- $0.26
- Fits in one request
Ling 3.0 Flash Sante has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Ling 3.0 Flash Sante
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- Muse Wonder
- $0.32
- Fits in one request
Ling 3.0 Flash Sante 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.
SpecificationLing 3.0 Flash SanteMuse Wonder
Context window
Maximum documented context; output-token limits may be lower.
Ling 3.0 Flash Sante
Muse Wonder
API model ID
Ling 3.0 Flash Sante
Not sourced
Muse Wonder
muse-wonder-1.0
Meta: Muse Wonder models and pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Ling 3.0 Flash Sante
No comparable hosted API rate
InclusionAI Ling-3.0-flash-Sante model cardMuse Wonder
$0.2 per 1M cached input tokens
Meta: Muse Wonder models and pricingDocumented inputs
Ling 3.0 Flash Sante
Not sourced
Muse Wonder
Not sourced
Documented outputs
Ling 3.0 Flash Sante
Not sourced
Muse Wonder
Not sourced
Provider availability
Ling 3.0 Flash Sante
Not sourced
Muse Wonder
Not sourced
Reasoning profile
Ling 3.0 Flash Sante
Reasoning
Muse Wonder
Reasoning
Weight access
Ling 3.0 Flash Sante
Open Weight
Muse Wonder
Proprietary
License
Ling 3.0 Flash Sante
Open Weight
Muse Wonder
Proprietary
Release date
Ling 3.0 Flash Sante
2026-10-19
Muse Wonder
2026-10-12
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
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Muse Wonder has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Ling 3.0 Flash Sante or Muse Wonder?
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, Ling 3.0 Flash Sante or Muse Wonder?
Ling 3.0 Flash Sante is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Ling 3.0 Flash Sante or Muse Wonder?
Ling 3.0 Flash Sante and Muse Wonder are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Ling 3.0 Flash Sante or Muse Wonder?
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, Ling 3.0 Flash Sante or Muse Wonder?
Muse Wonder has the larger documented context window: 1M, compared with 262K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Ling 3.0 Flash Sante
API / mo$0
Self-host / mo$3,495
Break-even—
Muse Wonder
API / mo$18,000
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence14 rows
Agentic
BrowseComp
Ling 3.0 Flash Sante80.2%SourceMuse Wonder—Not directly comparable
DeepSearchQA
Ling 3.0 Flash Sante90.8%SourceMuse Wonder—Not directly comparable
HLE w/ tools
Ling 3.0 Flash Sante57.1%SourceMuse Wonder—Not directly comparable
MCP Atlas
Ling 3.0 Flash Sante75.6%SourceMuse Wonder—Not directly comparable
JobBench
Ling 3.0 Flash Sante—Muse Wonder70.5%SourceNot directly comparable
Finance Agent v2
Ling 3.0 Flash Sante—Muse Wonder56.6%SourceNot directly comparable
MazeBench
Ling 3.0 Flash Sante—Muse Wonder0%SourceNot directly comparable
Coding
DeepSWE
Ling 3.0 Flash Sante—Muse Wonder78.8%SourceNot directly comparable
ProgramBench
Ling 3.0 Flash Sante—Muse Wonder50.0%SourceNot directly comparable
sweMarathon
Ling 3.0 Flash Sante—Muse Wonder53.1%SourceNot directly comparable
Bug Hunt Bench
Ling 3.0 Flash Sante—Muse Wonder31.7 fixesSourceNot directly comparable
Reasoning
STEMBench
Ling 3.0 Flash Sante—Muse Wonder2.68SourceNot directly comparable
Knowledge
14 public results · 1 shared
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Last updated October 20, 2026