Updated November 4, 2026. Rank says Muse Spark 1.2 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Muse Spark 1.2 has the higher public score, 65.41 versus 46.15, and the 90% score intervals do not overlap. 3 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 Spark 1.2
Muse Spark 1.2 has the higher public coding point estimate, 52.8 to 30.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Muse Spark 1.2
Muse Spark 1.2 has the higher public agentic point estimate, 59.9 to 33.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Muse Spark 1.2
Muse Spark 1.2 has the larger documented context window.
1K fresh input + 500 output tokens
GPT-5.4 nano
GPT-5.4 nano 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
GPT-5.4 nano
GPT-5.4 nano has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-5.4 nano
GPT-5.4 nano has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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 Spark 1.2 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.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
MMLU-Pro (Vals)Knowledge
Normalized gap 11.1Each 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-5.4 nano | Muse Spark 1.2 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 33.7Supported · #97/135 | 59.9Supported · #31/135 | Like-for-likeBenchAlign v5.8 lane · 6 vs 3 public rows | Muse Spark 1.2 leads |
| Coding | 30.1Supported · #100/156 | 52.8Supported · #37/156 | Like-for-likeBenchAlign v5.8 lane · 4 vs 5 public rows | Muse Spark 1.2 leads · intervals overlap |
| Knowledge | 39.4Supported · #117/179 | 64.7Supported · #33/179 | Like-for-likeBenchAlign v5.8 lane · 5 vs 1 public rows | Muse Spark 1.2 leads |
| Reasoning | 40.6Unranked · 4 rankable rows | 79.3Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 52.6#51/58 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 93.3#12/157 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 52.3Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 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-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
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-5.4 nano
Muse Spark 1.2
GPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationMuse Spark 1.2
muse-spark-1.2
Meta: Muse Spark 1.2 model pageA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.4 nano
$0.02 per 1M cached input tokens
OpenAI pricingMuse Spark 1.2
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.2 model pageGPT-5.4 nano
text, image
OpenAI model catalogMuse Spark 1.2
Not sourced
GPT-5.4 nano
Muse Spark 1.2
Not sourced
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogMuse Spark 1.2
Not sourced
GPT-5.4 nano
Reasoning
Muse Spark 1.2
Reasoning
GPT-5.4 nano
Proprietary
Muse Spark 1.2
Proprietary
GPT-5.4 nano
Proprietary
Muse Spark 1.2
Proprietary
GPT-5.4 nano
2026-03-17
Muse Spark 1.2
2026-08-05
Muse Spark 1.2 has the higher public score, 65.41 versus 46.15, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Muse Spark 1.2 has the higher public coding point estimate, 52.8 to 30.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Muse Spark 1.2 has the higher public agentic tasks point estimate, 59.9 to 33.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.00082 on GPT-5.4 nano and $0.00337 on Muse Spark 1.2; repository review costs $0.01375 and $0.07525; the cache-heavy agent loop costs $0.0205 and $0.0975. Costs use the listed standard API rates.
Muse Spark 1.2 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
MCP Atlas
Not directly comparable
Toolathlon
Not directly comparable
τ²-bench results
Not directly comparable
Terminal-Bench 2.1 (Vals)
Muse Spark 1.2 leads this result
Terminal-Bench 2.1
Not directly comparable
MazeBench
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Muse Spark 1.2 leads this result
Terminal-Bench 2.1
Not directly comparable
DeepSWE
Not directly comparable
VulcanBench v3
Not directly comparable
FrontierSWE v2
Not directly comparable
ExtractBench (LlamaIndex)
Not directly comparable
ParseBench
Not directly comparable
MMMU-Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
GPQA
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Muse Spark 1.2 leads this result
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Last updated November 4, 2026
