Model comparison
Ling 2.6 Flash vs Muse Spark
Head-to-head evidence from 15 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Ling 2.6 Flash #154 (Estimated); Muse Spark #13 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Ling 2.6 Flash and Muse Spark share 15 comparable benchmark results. 2 of 8 categories are comparable. 3 results are unique to Ling 2.6 Flash; 24 to Muse Spark.
Updated July 21, 2026- Shared results
- 15
- Ling 2.6 Flash only
- 3
- Muse Spark only
- 24
- Comparable categories
- 2 / 8
Pick Muse Spark if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 15 shared benchmark results across 5 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Muse Spark is clearly ahead on the BenchAlign aggregate, 71.04 to 43.87. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Muse Spark's sharpest advantage is in coding, where it averages 67.8 against 27. Ling 2.6 Flash does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Muse Spark is the reasoning model in the pair, while Ling 2.6 Flash is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Ling 2.6 Flash | Δ | Muse Spark |
|---|---|---|---|
| Coding | Ling 2.6 Flash27.0 | Margin→ 40.8 | Muse Spark67.8 |
| Knowledge | Ling 2.6 Flash59.0 | Margin← 8.6 | Muse Spark50.4 |
| Agentic | Ling 2.6 FlashNot measured | MarginNo overlap | Muse Spark59.0 |
| Reasoning | Ling 2.6 FlashNot measured | MarginNo overlap | Muse Spark42.5 |
| Math | Ling 2.6 FlashNot measured | MarginNo overlap | Muse Spark32.9 |
| Multimodal | Ling 2.6 FlashNot measured | MarginNo overlap | Muse Spark82.5 |
| Inst. Following | Ling 2.6 Flash57.0 | MarginNo overlap | Muse SparkNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Ling 2.6 Flash | Muse Spark | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Ling 2.6 FlashNot available | Muse SparkNot available | A complete price comparison is not available. |
| Generation speedtokens per second | Ling 2.6 Flash209.5 tok/s | Muse SparkNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Ling 2.6 Flash1.07 s | Muse SparkNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Ling 2.6 Flash262K | Muse Spark262K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | Ling 2.6 Flash | Muse Spark | Result |
|---|---|---|---|
| τ²-bench resultsSource | 86% | 91.5% | Muse Spark leads |
| GDPval-AASource | 2.2% | 32.2% | Muse Spark leads |
| GDPval-AASource | 545 | 1144 | Muse Spark leads |
| AA Agentic IndexSource | 2.3% | 28.7% | Muse Spark leads |
| Terminal-Bench 2.0Source | — | 59% | Not comparable |
| DeepSearchQASource | — | 74.8% | Not comparable |
| CyberGymSource | — | 43.5% | Not comparable |
| Claw-EvalSource | — | 63.8% | Not comparable |
CodingMuse Spark wins7 benchmarks
| Benchmark | Ling 2.6 Flash | Muse Spark | Result |
|---|---|---|---|
| SciCodeSource | 27% | — | Not comparable |
| AA Coding IndexSource | 25.3% | 58.6% | Muse Spark leads |
| AA-SciCodeSource | 27.1% | 51.5% | Muse Spark leads |
| SWE-bench VerifiedSource | — | 77.4% | Not comparable |
| SWE-bench ProSource | — | 52.4% | Not comparable |
| LiveCodeBench ProSource | — | 80.0% | Not comparable |
| Vibe Code BenchSource | — | 19.67% | Not comparable |
Reasoning3 benchmarks
KnowledgeLing 2.6 Flash wins12 benchmarks
| Benchmark | Ling 2.6 Flash | Muse Spark | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 14.1% | 43.1% | Muse Spark leads |
| GPQASource | 59% | — | Not comparable |
| AA-GPQA DiamondSource | 59.3% | 88.4% | Muse Spark leads |
| AA-HLESource | 6.2% | 39.9% | Muse Spark leads |
| AA-Omniscience IndexSource | -65.7% | 4.1% | Muse Spark leads |
| AA-Omniscience AccuracySource | 15.4% | 44.6% | Muse Spark leads |
| AA-Omniscience Hallucination RateSource | 95.8% | 73.2% | Muse Spark leads |
| GPQA-DSource | — | 89.5% | Not comparable |
| HLESource | — | 50.4% | Not comparable |
| HLE w/o toolsSource | — | 42.8% | Not comparable |
| HealthBench HardSource | — | 42.8% | Not comparable |
| MedXpertQA (Text)Source | — | 52.6% | Not comparable |
Math2 benchmarks
Multimodal8 benchmarks
| Benchmark | Ling 2.6 Flash | Muse Spark | Result |
|---|---|---|---|
| CharXivSource | — | 86.4% | Not comparable |
| MMMU-ProSource | — | 80.4% | Not comparable |
| ERQASource | — | 64.7% | Not comparable |
| SimpleVQASource | — | 71.3% | Not comparable |
| ScreenSpot ProSource | — | 84.1% | Not comparable |
| ZeroBenchSource | — | 33.0% | Not comparable |
| MedXpertQA (MM)Source | — | 78.4% | Not comparable |
| AA-MMMU-ProSource | — | 80.5% | Not comparable |
Frequently Asked Questions (3)
Which is better, Ling 2.6 Flash or Muse Spark?
Muse Spark is ahead on BenchLM's BenchAlign leaderboard, 71.04 to 43.87.
Which is better for knowledge tasks, Ling 2.6 Flash or Muse Spark?
Ling 2.6 Flash has the edge for knowledge tasks in this comparison, averaging 59 versus 50.4. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for coding, Ling 2.6 Flash or Muse Spark?
Muse Spark has the edge for coding in this comparison, averaging 67.8 versus 27. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
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