Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Gemini 3.8 Flash Cyber is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Gemini 3.8 Flash Cyber vs Muse Wonder
Updated October 12, 2026. We do not rank this pair: at least one has no public score. 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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 0
- Gemini 3.8 Flash Cyber only
- 3
- Muse Wonder only
- 6
- 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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 3.8 Flash Cyber and Muse Wonder are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
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.
—Gemini 3.8 Flash Cyber69.8Muse 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.
3 Gemini 3.8 Flash Cyber only
6 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- Gemini 3.8 Flash Cyber
- Not ranked
- Muse Wonder
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 2 public rows
- Reading
- Not comparable
Coding
Not comparable- Gemini 3.8 Flash Cyber
- Not ranked
- Muse Wonder
- 69.8
- Supported · #10/148
- Basis
- BenchAlign v5.8 lane · 0 vs 3 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 3.8 Flash Cyber
- Not ranked
- Muse Wonder
- 81.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3.8 Flash Cyber
- Not ranked
- Muse Wonder
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Gemini 3.8 Flash Cyber
- Not ranked
- Muse Wonder
- 74.0
- Supported · #12/180
- Basis
- BenchAlign v5.8 lane · 0 vs 1 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.8 Flash Cyber
- Not ranked
- Muse Wonder
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.8 Flash Cyber
- Not ranked
- Muse Wonder
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.8 Flash Cyber
- Not ranked
- Muse Wonder
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.8 Flash Cyber | Muse Wonder | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 2 public rows | Not comparable |
| Coding | Not ranked | 69.8Supported · #10/148 | Not comparableBenchAlign v5.8 lane · 0 vs 3 public rows | Not comparable |
| Reasoning | Not ranked | 81.3Unranked · 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 | 74.0Supported · #12/180 | Not comparableBenchAlign v5.8 lane · 0 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
- Gemini 3.8 Flash Cyber
- API rate not published
- Fit state unavailable
- Muse Wonder
- $0.014
- Fits in one request
Gemini 3.8 Flash Cyber has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemini 3.8 Flash Cyber
- API rate not published
- Fit state unavailable
- Muse Wonder
- $0.26
- Fits in one request
Gemini 3.8 Flash Cyber has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemini 3.8 Flash Cyber
- API rate not published
- Fit state unavailable
- Cached-input rate unavailable
- Muse Wonder
- $0.32
- Fits in one request
Gemini 3.8 Flash Cyber 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 3.8 Flash CyberMuse Wonder
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.8 Flash Cyber
Muse Wonder
API model ID
Gemini 3.8 Flash Cyber
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.
Gemini 3.8 Flash Cyber
No comparable hosted API rate
Google Gemini 3.8 Flash launchMuse Wonder
$0.2 per 1M cached input tokens
Meta: Muse Wonder models and pricingDocumented inputs
Gemini 3.8 Flash Cyber
Not sourced
Muse Wonder
Not sourced
Documented outputs
Gemini 3.8 Flash Cyber
Not sourced
Muse Wonder
Not sourced
Provider availability
Gemini 3.8 Flash Cyber
Limited Access · Fairwind Program
Google Gemini 3.8 Flash and Gemini 3.8 Flash Cyber launchMuse Wonder
Not sourced
Reasoning profile
Gemini 3.8 Flash Cyber
Reasoning
Muse Wonder
Reasoning
Weight access
Gemini 3.8 Flash Cyber
Proprietary
Muse Wonder
Proprietary
License
Gemini 3.8 Flash Cyber
Proprietary
Muse Wonder
Proprietary
Release date
Gemini 3.8 Flash Cyber
2026-09-02
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Gemini 3.8 Flash Cyber or Muse Wonder?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Gemini 3.8 Flash Cyber or Muse Wonder?
Gemini 3.8 Flash Cyber is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Gemini 3.8 Flash Cyber or Muse Wonder?
Gemini 3.8 Flash Cyber and Muse Wonder are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Gemini 3.8 Flash Cyber 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, Gemini 3.8 Flash Cyber or Muse Wonder?
A complete documented context-window comparison is not available.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence9 rows
Agentic
CyberGym
Gemini 3.8 Flash Cyber86.2%SourceMuse Wonder—Not directly comparable
CWE-Bench
Gemini 3.8 Flash Cyber47.2%SourceMuse Wonder—Not directly comparable
JobBench
Gemini 3.8 Flash Cyber—Muse Wonder70.5%SourceNot directly comparable
Finance Agent v2
Gemini 3.8 Flash Cyber—Muse Wonder56.6%SourceNot directly comparable
Coding
Knowledge
HealthBench Professional
Gemini 3.8 Flash Cyber—Muse Wonder60.5%SourceNot directly comparable
Instruction following
Gray Swan IPI (15 attempts)
Gemini 3.8 Flash Cyber6.0%SourceMuse Wonder—Not directly comparable
9 public results · 0 shared
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Last updated October 12, 2026