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 point estimate, 65.41 versus 45.33. Their conditional score ranges overlap. These ranges do not establish rank confidence. 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 32.2, 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 34.3, 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
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 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 6.3Each 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 | Ling 3.0 Flash | Muse Spark 1.2 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 34.3Supported · #94/135 | 59.9Supported · #31/135 | Like-for-likeBenchAlign v5.8 lane · 9 vs 3 public rows | Muse Spark 1.2 leads · intervals overlap |
| Coding | 32.2Supported · #97/156 | 52.8Supported · #37/156 | Like-for-likeBenchAlign v5.8 lane · 6 vs 5 public rows | Muse Spark 1.2 leads · intervals overlap |
| Knowledge | 42.1Supported · #108/179 | 64.7Supported · #33/179 | Like-for-likeBenchAlign v5.8 lane · 5 vs 1 public rows | Muse Spark 1.2 leads |
| Reasoning | 75.1Unranked · 2 rankable rows | 79.3Unranked · 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 |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 75.2#72/157 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | 81.1Unranked · 3 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
Ling 3.0 Flash has no comparable published API token rate.
50K fresh input + 3K output tokens
Ling 3.0 Flash has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Ling 3.0 Flash 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.
Ling 3.0 Flash
Muse Spark 1.2
Ling 3.0 Flash
Not sourced
Muse 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.
Ling 3.0 Flash
No comparable hosted API rate
InclusionAI Ling 3.0 Flash model cardMuse Spark 1.2
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.2 model pageLing 3.0 Flash
Not sourced
Muse Spark 1.2
Not sourced
Ling 3.0 Flash
Not sourced
Muse Spark 1.2
Not sourced
Ling 3.0 Flash
Not sourced
Muse Spark 1.2
Not sourced
Ling 3.0 Flash
Reasoning
Muse Spark 1.2
Reasoning
Ling 3.0 Flash
Open Weight
Muse Spark 1.2
Proprietary
Ling 3.0 Flash
Open Weight
Muse Spark 1.2
Proprietary
Ling 3.0 Flash
2026-07-23
Muse Spark 1.2
2026-08-05
Muse Spark 1.2 has the higher public point estimate, 65.41 versus 45.33. 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 Spark 1.2 has the higher public coding point estimate, 52.8 to 32.2, 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 34.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Muse Spark 1.2 has the larger documented context window: 1M, compared with 262K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
MCP Atlas
Not directly comparable
skillsBench
Not directly comparable
BFCL v4
Not directly comparable
WideResearch
Not directly comparable
BrowseComp
Not directly comparable
DRACO
Not directly comparable
Terminal-Bench 2.1 (Vals)
Muse Spark 1.2 leads this result
Hermes Index
Not directly comparable
Hermes Bench
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
MazeBench
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
LiveCodeBench v5
Not directly comparable
SciCode
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
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Muse Spark 1.2 leads this result
IFBench
Not directly comparable
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Last updated November 4, 2026
