Updated October 12, 2026. Rank says Muse Wonder 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
Muse Wonder has the higher public point estimate, 70.44 versus 59.87. Their conditional score ranges overlap. These ranges do not establish rank confidence. 1 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
Muse Wonder has the higher public coding point estimate, 69.8 to 54.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Muse Wonder
Muse Wonder has the larger documented context window.
1K fresh input + 500 output tokens
GPT-5.3 Codex
GPT-5.3 Codex has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Muse Wonder
Muse Wonder has the lower estimated token cost for this stated workload. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
50K fresh input + 3K output tokens
GPT-5.3 Codex
GPT-5.3 Codex has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Muse Wonder is not ranked on the public lane for agentic, so no winner is named for agentic.
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 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.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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 | GPT-5.3 Codex | Muse Wonder | Basis | Reading |
|---|---|---|---|---|
| Coding | 54.8Supported · #31/148 | 69.8Supported · #10/148 | Like-for-likeBenchAlign v5.8 lane · 7 vs 3 public rows | Muse Wonder leads · intervals overlap |
| Knowledge | 65.4Estimated · #29/180 | 74.0Supported · #12/180 | Directional onlyBenchAlign v5.8 lane · 0 vs 1 public rows | Directional only |
| Agentic | 55.6Estimated · #35/123 | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 2 public rows | Not comparable |
| Reasoning | 83.1Unranked · 2 rankable rows | 81.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 80.1Unranked · 1 rankable row | 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 | 92.6#18/127 | 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
GPT-5.3 Codex has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-5.3 Codex has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Muse Wonder has the lower modeled cost
GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input 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.
GPT-5.3 Codex
Muse Wonder
GPT-5.3 Codex
gpt-5.3-codex
OpenAI GPT-5.3 Codex model documentationMuse Wonder
muse-wonder-1.0
Meta: Muse Wonder models and pricingA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.3 Codex
Not published
Muse Wonder
$0.2 per 1M cached input tokens
Meta: Muse Wonder models and pricingGPT-5.3 Codex
text, image
OpenAI model catalogMuse Wonder
Not sourced
GPT-5.3 Codex
Muse Wonder
Not sourced
GPT-5.3 Codex
Generally Available · OpenAI Responses API
OpenAI model catalogMuse Wonder
Not sourced
GPT-5.3 Codex
Reasoning
Muse Wonder
Reasoning
GPT-5.3 Codex
Proprietary
Muse Wonder
Proprietary
GPT-5.3 Codex
Proprietary
Muse Wonder
Proprietary
GPT-5.3 Codex
2026-02-05
Muse Wonder
2026-10-12
Muse Wonder has the higher public point estimate, 70.44 versus 59.87. 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 has the higher public coding point estimate, 69.8 to 54.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Muse Wonder is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
For the stated presets, chat costs $0.00875 on GPT-5.3 Codex and $0.014 on Muse Wonder; repository review costs $0.1295 and $0.26; the cache-heavy agent loop costs $0.525 and $0.32. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
Muse Wonder has the larger documented context window: 1M, compared with 400K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
OSWorld-Verified
Not directly comparable
Gert Labs
Not directly comparable
JobBench
Muse Wonder leads this result
Finance Agent v2
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE-Rebench
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
DeepSWE
Not directly comparable
ProgramBench
Not directly comparable
sweMarathon
Not directly comparable
HealthBench Professional
Not directly comparable
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Last updated October 12, 2026
