Updated October 12, 2026. Rank says Muse Wonder Full is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty. This is a same-family comparison, so migration details appear when the source data supports them.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Muse Wonder Full has the higher public point estimate, 73.81 versus 70.44. Their conditional score ranges overlap. These ranges do not establish rank confidence. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
Muse Wonder Full
Muse Wonder Full has the higher public coding point estimate, 74 to 69.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Muse Wonder and Muse Wonder Full are not ranked on the public lane for agentic, so no winner is named for agentic.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
Like-for-like · BenchAlign v5.8
Muse Wonder Full 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.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
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.
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.
| Category | Muse Wonder | Muse Wonder Full | Basis | Reading |
|---|---|---|---|---|
| Coding | 69.8Supported · #10/148 | 74.0Supported · #5/148 | Like-for-likeBenchAlign v5.8 lane · 3 vs 3 public rows | Muse Wonder Full leads · intervals overlap |
| Knowledge | 74.0Supported · #12/180 | 77.9Supported · #10/180 | Like-for-likeBenchAlign v5.8 lane · 1 vs 1 public rows | Muse Wonder Full leads · intervals overlap |
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 2 public rows | Not comparable |
| Reasoning | 81.3Unranked · 2 rankable rows | 83.4Unranked · 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 |
| 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.
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.
1K fresh input + 500 output tokens
Muse Wonder Full has no comparable published API token rate.
50K fresh input + 3K output tokens
Muse Wonder Full has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Muse Wonder Full has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Muse Wonder
Muse Wonder Full
Muse Wonder
muse-wonder-1.0
Meta: Muse Wonder models and pricingMuse Wonder Full
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Muse Wonder
$0.2 per 1M cached input tokens
Meta: Muse Wonder models and pricingMuse Wonder Full
No comparable hosted API rate
Meta: Muse Wonder models and pricingMuse Wonder
Not sourced
Muse Wonder Full
Not sourced
Muse Wonder
Not sourced
Muse Wonder Full
Not sourced
Muse Wonder
Not sourced
Muse Wonder Full
Not sourced
Muse Wonder
Reasoning
Muse Wonder Full
Reasoning
Muse Wonder
Proprietary
Muse Wonder Full
Proprietary
Muse Wonder
Proprietary
Muse Wonder Full
Proprietary
Muse Wonder
2026-10-12
Muse Wonder Full
2026-10-12
Muse Wonder Full has the higher public point estimate, 73.81 versus 70.44. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Muse Wonder Full has the higher public coding point estimate, 74 to 69.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Muse Wonder and Muse Wonder Full are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Both models list the same context window, 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
JobBench
Shared sourceMuse Wonder Full leads this result
Finance Agent v2
Shared sourceMuse Wonder Full leads this result
DeepSWE
Shared sourceMuse Wonder Full leads this result
ProgramBench
Shared sourceMuse Wonder Full leads this result
sweMarathon
Shared sourceMuse Wonder Full leads this result
HealthBench Professional
Shared sourceMuse Wonder Full leads this result
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Last updated October 12, 2026
