Model comparison

Ling 2.6 Flash vs Qwen3.5 397B

Data verified July 21, 2026

Head-to-head evidence from 16 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

Ling 2.6 Flash

InclusionAI

43.87/100

Margin

13.1pts

winning →

Qwen3.5 397B

Alibaba

57.01/100

1 category wins2 category wins

Public leaderboard positions: Ling 2.6 Flash #154 (Estimated); Qwen3.5 397B #71 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Ling 2.6 Flash and Qwen3.5 397B share 16 comparable benchmark results. 3 of 8 categories are comparable. 2 results are unique to Ling 2.6 Flash; 39 to Qwen3.5 397B.

Updated July 21, 2026

Shared results: 16
Ling 2.6 Flash only: 2
Qwen3.5 397B only: 39
Comparable categories: 3 / 8

Pick Qwen3.5 397B if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if knowledge is the priority or you need the larger 262K context window.

Confidence note. This is a partial-evidence comparison with 16 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 397B is clearly ahead on the BenchAlign aggregate, 57.01 to 43.87. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Qwen3.5 397B's sharpest advantage is in coding, where it averages 66.5 against 27. The single biggest benchmark swing on the page is GPQA, 59% to 88.4%. 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.

Ling 2.6 Flash gives you the larger context window at 262K, compared with 128K for Qwen3.5 397B.

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 scores and score margins for Ling 2.6 Flash and Qwen3.5 397B
Category	Ling 2.6 Flash	Δ	Qwen3.5 397B
Coding	Ling 2.6 Flash27.0	Margin→ 39.5	Qwen3.5 397B66.5
Inst. Following	Ling 2.6 Flash57.0	Margin→ 35.6	Qwen3.5 397B92.6
Knowledge	Ling 2.6 Flash59.0	Margin← 2.4	Qwen3.5 397B56.6
Agentic	Ling 2.6 FlashNot measured	MarginNo overlap	Qwen3.5 397B56.5
Reasoning	Ling 2.6 FlashNot measured	MarginNo overlap	Qwen3.5 397B63.2
Math	Ling 2.6 FlashNot measured	MarginNo overlap	Qwen3.5 397B90.6
Multilingual	Ling 2.6 FlashNot measured	MarginNo overlap	Qwen3.5 397B84.7
Multimodal	Ling 2.6 FlashNot measured	MarginNo overlap	Qwen3.5 397B79.6

Decisive benchmark drivers

The largest measured benchmark gaps in this matchup, with exact reported values.

A · Ling 2.6 FlashB · Qwen3.5 397B

GPQA
Knowledge
Source ↗
A 59%B 88.4%
Winner: Qwen3.5 397BΔ 29.4
GPQA: Ling 2.6 Flash scored 59%; Qwen3.5 397B scored 88.4%. Qwen3.5 397B 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 397B	Comparison
Input / output priceUSD per 1M tokens	Ling 2.6 FlashNot available	Qwen3.5 397B$0.6 input / $3.6 output	A complete price comparison is not available.
Generation speedtokens per second	Ling 2.6 Flash209.5 tok/s	Qwen3.5 397B96 tok/s	Ling 2.6 Flash has the higher measured throughput.
First-answer latencyseconds to first token	Ling 2.6 Flash1.07 s	Qwen3.5 397B2.44 s	Ling 2.6 Flash reaches the first token sooner.
Context windowmaximum listed tokens	Ling 2.6 Flash262K	Qwen3.5 397B128K	Ling 2.6 Flash lists the larger context window.

Benchmark Deep Dive

Agentic

18 benchmarks

Benchmark	Ling 2.6 Flash	Qwen3.5 397B	Result
τ²-bench resultsSource	86%	95.6%	Qwen3.5 397B leads
GDPval-AASource	2.2%	23.1%	Qwen3.5 397B leads
GDPval-AASource	545	962	Qwen3.5 397B leads
AA Agentic IndexSource	2.3%	19.9%	Qwen3.5 397B leads
Terminal-Bench 2.0Source	—	52.5%	Not comparable
BrowseCompSource	—	62%	Not comparable
Claw-EvalSource	—	56.8%	Not comparable
QwenClawBenchSource	—	51.8%	Not comparable
τ³-bench resultsSource	—	68.4%	Not comparable
VITA-BenchSource	—	43.7%	Not comparable
DeepPlanningSource	—	37.6%	Not comparable
ToolathlonSource	—	36.3%	Not comparable
MCP AtlasSource	—	46.1%	Not comparable
MCP-TasksSource	—	74.2%	Not comparable
WideResearchSource	—	74.0%	Not comparable
Gert LabsSource	—	46.76%	Not comparable
ResearchClawBenchSource	—	14.2%	Not comparable
APEX-Agents-AASource	—	15.3%	Not comparable

CodingQwen3.5 397B wins

6 benchmarks

Benchmark	Ling 2.6 Flash	Qwen3.5 397B	Result
SciCodeSource	27%	—	Not comparable
AA Coding IndexSource	25.3%	48.2%	Qwen3.5 397B leads
AA-SciCodeSource	27.1%	42.0%	Qwen3.5 397B leads
SWE-bench VerifiedSource	—	76.2%	Not comparable
LiveCodeBench v6Source	—	83.6%	Not comparable
SWE-bench ProSource	—	50.9%	Not comparable

Reasoning

4 benchmarks

Benchmark	Ling 2.6 Flash	Qwen3.5 397B	Result
AA-LCRSource	25.0%	65.7%	Qwen3.5 397B leads
CritPtSource	0.0%	1.7%	Qwen3.5 397B leads
LongBench v2Source	—	63.2%	Not comparable
AI-NeedleSource	—	68.7%	Not comparable

KnowledgeLing 2.6 Flash wins

12 benchmarks

Benchmark	Ling 2.6 Flash	Qwen3.5 397B	Result
Artificial Analysis Intelligence IndexSource	14.1%	33.7%	Qwen3.5 397B leads
GPQASource	59%	88.4%	Qwen3.5 397B leads
AA-GPQA DiamondSource	59.3%	89.3%	Qwen3.5 397B leads
AA-HLESource	6.2%	27.3%	Qwen3.5 397B leads
AA-Omniscience IndexSource	-65.7%	-29.8%	Qwen3.5 397B leads
AA-Omniscience AccuracySource	15.4%	31.4%	Qwen3.5 397B leads
AA-Omniscience Hallucination RateSource	95.8%	89.1%	Qwen3.5 397B leads
SuperGPQASource	—	70.4%	Not comparable
MMLU-ProSource	—	87.8%	Not comparable
MMLU-ReduxSource	—	94.9%	Not comparable
C-EvalSource	—	93%	Not comparable
HLESource	—	28.7%	Not comparable

Math

5 benchmarks

Benchmark	Ling 2.6 Flash	Qwen3.5 397B	Result
AIME26Source	—	93.3%	Not comparable
HMMT Feb 2025Source	—	94.8%	Not comparable
HMMT Nov 2025Source	—	92.7%	Not comparable
HMMT Feb 2026Source	—	87.9%	Not comparable
MMAnswerBenchSource	—	80.9%	Not comparable

Multilingual

2 benchmarks

Benchmark	Ling 2.6 Flash	Qwen3.5 397B	Result
MMLU-ProXSource	—	84.7%	Not comparable
NOVA-63Source	—	59.1%	Not comparable

Multimodal

7 benchmarks

Benchmark	Ling 2.6 Flash	Qwen3.5 397B	Result
MMMU-ProSource	—	79%	Not comparable
MathVisionSource	—	88.6%	Not comparable
CharXivSource	—	80.8%	Not comparable
VideoMMMUSource	—	84.7%	Not comparable
ScreenSpot ProSource	—	65.6%	Not comparable
V*Source	—	95.8%	Not comparable
AA-MMMU-ProSource	—	77.3%	Not comparable

Inst. FollowingQwen3.5 397B wins

3 benchmarks

Benchmark	Ling 2.6 Flash	Qwen3.5 397B	Result
IFBenchSource	57%	—	Not comparable
AA-IFBenchSource	57.4%	78.8%	Qwen3.5 397B leads
IFEvalSource	—	92.6%	Not comparable

Frequently Asked Questions (4)

Which is better, Ling 2.6 Flash or Qwen3.5 397B?

Qwen3.5 397B is ahead on BenchLM's BenchAlign leaderboard, 57.01 to 43.87. The biggest single separator in this matchup is GPQA, where the scores are 59% and 88.4%.

Which is better for knowledge tasks, Ling 2.6 Flash or Qwen3.5 397B?

Ling 2.6 Flash has the edge for knowledge tasks in this comparison, averaging 59 versus 56.6. 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 Qwen3.5 397B?

Qwen3.5 397B has the edge for coding in this comparison, averaging 66.5 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 Qwen3.5 397B?

Qwen3.5 397B has the edge for instruction following in this comparison, averaging 92.6 versus 57. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.

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