Model comparison
Ling 2.6 Flash vs Qwen3.7 Max
Head-to-head evidence from 18 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); Qwen3.7 Max #10 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Ling 2.6 Flash and Qwen3.7 Max share 18 comparable benchmark results. 3 of 8 categories are comparable. 0 results are unique to Ling 2.6 Flash; 40 to Qwen3.7 Max.
Updated July 23, 2026- Shared results
- 18
- Ling 2.6 Flash only
- 0
- Qwen3.7 Max only
- 40
- Comparable categories
- 3 / 8
Pick Qwen3.7 Max 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 18 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
Qwen3.7 Max is clearly ahead on the BenchAlign aggregate, 72.84 to 43.87. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.7 Max's sharpest advantage is in coding, where it averages 77.9 against 27. The single biggest benchmark swing on the page is GPQA, 59% to 92.4%.
Qwen3.7 Max 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. Qwen3.7 Max gives you the larger context window at 1M, compared with 262K for Ling 2.6 Flash.
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 | Qwen3.7 Max |
|---|---|---|---|
| Coding | Ling 2.6 Flash27.0 | Margin→ 50.9 | Qwen3.7 Max77.9 |
| Inst. Following | Ling 2.6 Flash57.0 | Margin→ 27.4 | Qwen3.7 Max84.4 |
| Knowledge | Ling 2.6 Flash59.0 | Margin→ 5.2 | Qwen3.7 Max64.2 |
| Agentic | Ling 2.6 FlashNot measured | MarginNo overlap | Qwen3.7 Max69.7 |
| Reasoning | Ling 2.6 FlashNot measured | MarginNo overlap | Qwen3.7 Max90.4 |
| Math | Ling 2.6 FlashNot measured | MarginNo overlap | Qwen3.7 Max97.1 |
| Multilingual | Ling 2.6 FlashNot measured | MarginNo overlap | Qwen3.7 Max87.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 59%B 92.4%Winner: Qwen3.7 MaxΔ 33.4GPQA: Ling 2.6 Flash scored 59%; Qwen3.7 Max scored 92.4%. Qwen3.7 Max wins this benchmark. - Source ↗
SciCode
CodingA 27%B 53.5%Winner: Qwen3.7 MaxΔ 26.5SciCode: Ling 2.6 Flash scored 27%; Qwen3.7 Max scored 53.5%. Qwen3.7 Max wins this benchmark. - Source ↗
IFBench
Inst. FollowingA 57%B 79.1%Winner: Qwen3.7 MaxΔ 22.1IFBench: Ling 2.6 Flash scored 57%; Qwen3.7 Max scored 79.1%. Qwen3.7 Max 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 | Qwen3.7 Max | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Ling 2.6 FlashNot available | Qwen3.7 MaxNot available | A complete price comparison is not available. |
| Generation speedtokens per second | Ling 2.6 Flash209.5 tok/s | Qwen3.7 MaxNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Ling 2.6 Flash1.07 s | Qwen3.7 MaxNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Ling 2.6 Flash262K | Qwen3.7 Max1M | Qwen3.7 Max lists the larger context window. |
Benchmark Deep Dive
Agentic21 benchmarks
| Benchmark | Ling 2.6 Flash | Qwen3.7 Max | Result |
|---|---|---|---|
| τ²-bench resultsSource | 86% | 94.7% | Qwen3.7 Max leads |
| GDPval-AASource | 2.2% | 38.7% | Qwen3.7 Max leads |
| GDPval-AASource | 545 | 1273 | Qwen3.7 Max leads |
| AA Agentic IndexSource | 2.3% | 30.6% | Qwen3.7 Max leads |
| Terminal-Bench 2.0Source | — | 69.7% | Not comparable |
| QwenClawBenchSource | — | 64.3% | Not comparable |
| QwenWebBenchSource | — | 1568 | Not comparable |
| Claw-EvalSource | — | 65.2% | Not comparable |
| BFCL v4Source | — | 75.0% | Not comparable |
| MCP AtlasSource | — | 76.4% | Not comparable |
| VITA-BenchSource | — | 47.9% | Not comparable |
| HLE w/ toolsSource | — | 53.5% | Not comparable |
| Gert LabsSource | — | 64.27% | Not comparable |
| ResearchClawBenchSource | — | 18.7% | Not comparable |
| AA BriefcaseSource | — | 908 | Not comparable |
| AA AutomationBenchSource | — | 25.6% | Not comparable |
| AA EnterpriseOps-GymSource | — | 45.0% | Not comparable |
| AA ITBenchSource | — | 42.5% | Not comparable |
| terminalBenchHardSource | — | 50.8% | Not comparable |
| aaTerminalBench21Source | — | 74.5% | Not comparable |
| AA Harvey LABSource | — | 83.4% | Not comparable |
CodingQwen3.7 Max wins9 benchmarks
| Benchmark | Ling 2.6 Flash | Qwen3.7 Max | Result |
|---|---|---|---|
| SciCodeSource | 27% | 53.5% | Qwen3.7 Max leads |
| AA Coding IndexSource | 25.3% | 66.0% | Qwen3.7 Max leads |
| AA-SciCodeSource | 27.1% | 48.8% | Qwen3.7 Max leads |
| SWE-bench VerifiedSource | — | 80.4% | Not comparable |
| SWE-bench ProSource | — | 60.6% | Not comparable |
| SWE MultilingualSource | — | 78.3% | Not comparable |
| NL2RepoSource | — | 47.2% | Not comparable |
| LiveCodeBenchSource | — | 91.6% | Not comparable |
| Terminal-Bench 2.0Source | — | 69.7% | Not comparable |
Reasoning3 benchmarks
KnowledgeQwen3.7 Max wins13 benchmarks
| Benchmark | Ling 2.6 Flash | Qwen3.7 Max | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 14.1% | 46.0% | Qwen3.7 Max leads |
| GPQASource | 59% | 92.4% | Qwen3.7 Max leads |
| AA-GPQA DiamondSource | 59.3% | 92.3% | Qwen3.7 Max leads |
| AA-HLESource | 6.2% | 38.1% | Qwen3.7 Max leads |
| AA-Omniscience IndexSource | -65.7% | 14.1% | Qwen3.7 Max leads |
| AA-Omniscience AccuracySource | 15.4% | 30.1% | Qwen3.7 Max leads |
| AA-Omniscience Hallucination RateSource | 95.8% | 22.9% | Qwen3.7 Max leads |
| GPQA-DSource | — | 92.4% | Not comparable |
| HLESource | — | 41.4% | Not comparable |
| MMLU-ProSource | — | 89.6% | Not comparable |
| MMLU-ReduxSource | — | 95% | Not comparable |
| SuperGPQASource | — | 73.6% | Not comparable |
| MMMLUSource | — | 90.3% | Not comparable |
Math3 benchmarks
Multilingual5 benchmarks
Multimodal1 benchmarks
| Benchmark | Ling 2.6 Flash | Qwen3.7 Max | Result |
|---|---|---|---|
| Design Arena WebsiteSource | — | 1293 | Not comparable |
Frequently Asked Questions (4)
Which is better, Ling 2.6 Flash or Qwen3.7 Max?
Qwen3.7 Max is ahead on BenchLM's BenchAlign leaderboard, 72.84 to 43.87. The biggest single separator in this matchup is GPQA, where the scores are 59% and 92.4%.
Which is better for knowledge tasks, Ling 2.6 Flash or Qwen3.7 Max?
Qwen3.7 Max has the edge for knowledge tasks in this comparison, averaging 64.2 versus 59. 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 Qwen3.7 Max?
Qwen3.7 Max has the edge for coding in this comparison, averaging 77.9 versus 27. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Which is better for instruction following, Ling 2.6 Flash or Qwen3.7 Max?
Qwen3.7 Max has the edge for instruction following in this comparison, averaging 84.4 versus 57. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.
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