Model comparison
Ling 2.6 Flash vs Qwen3.5-27B
Head-to-head evidence from 12 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); Qwen3.5-27B #45 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Ling 2.6 Flash and Qwen3.5-27B share 12 comparable benchmark results. 3 of 8 categories are comparable. 6 results are unique to Ling 2.6 Flash; 16 to Qwen3.5-27B.
Updated July 21, 2026- Shared results
- 12
- Ling 2.6 Flash only
- 6
- Qwen3.5-27B only
- 16
- Comparable categories
- 3 / 8
Pick Qwen3.5-27B if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 5 evidence categories; 3 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Qwen3.5-27B is clearly ahead on the BenchAlign aggregate, 60.7 to 43.87. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-27B's sharpest advantage is in instruction following, where it averages 95 against 57. The single biggest benchmark swing on the page is GPQA, 59% to 85.5%.
Qwen3.5-27B 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 | Qwen3.5-27B |
|---|---|---|---|
| Inst. Following | Ling 2.6 Flash57.0 | Margin→ 38.0 | Qwen3.5-27B95.0 |
| Coding | Ling 2.6 Flash27.0 | Margin→ 37.9 | Qwen3.5-27B64.9 |
| Knowledge | Ling 2.6 Flash59.0 | Margin→ 23.7 | Qwen3.5-27B82.7 |
| Agentic | Ling 2.6 FlashNot measured | MarginNo overlap | Qwen3.5-27B52.0 |
| Reasoning | Ling 2.6 FlashNot measured | MarginNo overlap | Qwen3.5-27B60.6 |
| Multilingual | Ling 2.6 FlashNot measured | MarginNo overlap | Qwen3.5-27B82.2 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 59%B 85.5%Winner: Qwen3.5-27BΔ 26.5GPQA: Ling 2.6 Flash scored 59%; Qwen3.5-27B scored 85.5%. Qwen3.5-27B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Ling 2.6 Flash | Qwen3.5-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Ling 2.6 FlashNot available | Qwen3.5-27B$0 input / $0 output | A complete price comparison is not available. |
| Generation speedtokens per second | Ling 2.6 Flash209.5 tok/s | Qwen3.5-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Ling 2.6 Flash1.07 s | Qwen3.5-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Ling 2.6 Flash262K | Qwen3.5-27B262K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | Ling 2.6 Flash | Qwen3.5-27B | Result |
|---|---|---|---|
| τ²-bench resultsSource | 86% | 93.9% | Qwen3.5-27B leads |
| GDPval-AASource | 2.2% | — | Not comparable |
| GDPval-AASource | 545 | — | Not comparable |
| AA Agentic IndexSource | 2.3% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 41.6% | Not comparable |
| BrowseCompSource | — | 61% | Not comparable |
| OSWorld-VerifiedSource | — | 56.2% | Not comparable |
| Gert LabsSource | — | 39.41% | Not comparable |
CodingQwen3.5-27B wins5 benchmarks
Reasoning3 benchmarks
KnowledgeQwen3.5-27B wins9 benchmarks
| Benchmark | Ling 2.6 Flash | Qwen3.5-27B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 14.1% | 33.8% | Qwen3.5-27B leads |
| GPQASource | 59% | 85.5% | Qwen3.5-27B leads |
| AA-GPQA DiamondSource | 59.3% | 85.8% | Qwen3.5-27B leads |
| AA-HLESource | 6.2% | 22.2% | Qwen3.5-27B leads |
| AA-Omniscience IndexSource | -65.7% | -42.0% | Qwen3.5-27B leads |
| AA-Omniscience AccuracySource | 15.4% | 21.0% | Qwen3.5-27B leads |
| AA-Omniscience Hallucination RateSource | 95.8% | 79.7% | Qwen3.5-27B leads |
| MMLU-ProSource | — | 86.1% | Not comparable |
| SuperGPQASource | — | 65.6% | Not comparable |
Multilingual1 benchmarks
| Benchmark | Ling 2.6 Flash | Qwen3.5-27B | Result |
|---|---|---|---|
| MMLU-ProXSource | — | 82.2% | Not comparable |
Multimodal5 benchmarks
Frequently Asked Questions (4)
Which is better, Ling 2.6 Flash or Qwen3.5-27B?
Qwen3.5-27B is ahead on BenchLM's BenchAlign leaderboard, 60.7 to 43.87. The biggest single separator in this matchup is GPQA, where the scores are 59% and 85.5%.
Which is better for knowledge tasks, Ling 2.6 Flash or Qwen3.5-27B?
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 82.7 versus 59. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for coding, Ling 2.6 Flash or Qwen3.5-27B?
Qwen3.5-27B has the edge for coding in this comparison, averaging 64.9 versus 27. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for instruction following, Ling 2.6 Flash or Qwen3.5-27B?
Qwen3.5-27B has the edge for instruction following in this comparison, averaging 95 versus 57. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.
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