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Ling 3.0 Flash Fin vs Qwen3 VL 32B Thinking

Ling 3.0 Flash Fin leads the LLM Stats Score 43.2 to 23.6.

InclusionAI · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Ling 3.0 Flash Fin leads the overall LLM Stats Score 43.2 to 23.6, ranking #41 overall.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Ling 3.0 Flash Fin

  • overall performance matters — it scores 43.2 and ranks #41 on LLM Stats
  • your work emphasizes reasoning and agents — it leads those capability indexes
  • you want the most recent training data — it shipped Sep 2026

Choose Qwen3 VL 32B Thinking

  • you need open weights you can self-host or fine-tune

At a glance

The differences that matter most.

Ling 3.0 Flash Fin
InclusionAI
Qwen3 VL 32B Thinking
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
43.2
#41
23.6
#173
Reasoning
44.6
#33
24.7
#153
Agents
29.5
#36
13.0
#96
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.06 / M
— / M
Output price
$0.18 / M
— / M
Context window
262,144
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Ling 3.0 Flash Fin
Qwen3 VL 32B Thinking
Finance
36.7#5
27.8#37
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

6 reported for Ling 3.0 Flash Fin · 47 for Qwen3 VL 32B Thinking

No common benchmarks found

Ling 3.0 Flash Fin and Qwen3 VL 32B Thinkingdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

91.0B diff

Ling 3.0 Flash Fin has 91.0B more parameters than Qwen3 VL 32B Thinking, making it 275.8% larger.

InclusionAI
Ling 3.0 Flash Fin
124.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
33.0Bparameters
124.0B
Ling 3.0 Flash Fin
33.0B
Qwen3 VL 32B Thinking

Context Window

Maximum input and output token capacity

Only Ling 3.0 Flash Fin specifies input context (262,144 tokens). Only Ling 3.0 Flash Fin specifies output context (262,144 tokens).

InclusionAI
Ling 3.0 Flash Fin
Input262,144 tokens
Output262,144 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Input- tokens
Output- tokens
Fri Sep 11 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Qwen3 VL 32B Thinking supports multimodal inputs, whereas Ling 3.0 Flash Fin does not.

Qwen3 VL 32B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.

Ling 3.0 Flash Fin

Text
Images
Audio
Video

Qwen3 VL 32B Thinking

Text
Images
Audio
Video

Release Timeline

When each model was launched

Ling 3.0 Flash Fin was released on 2026-09-03, while Qwen3 VL 32B Thinking was released on 2025-09-22.

Ling 3.0 Flash Fin is 12 months newer than Qwen3 VL 32B Thinking.

Ling 3.0 Flash Fin

Sep 3, 2026

1 weeks ago

11mo newer
Qwen3 VL 32B Thinking

Sep 22, 2025

11 months ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against Ling 3.0 Flash Fin and Qwen3 VL 32B Thinking side-by-side, then vote on the output you prefer.

Ling 3.0 Flash Fin
✓ Preferred
Qwen3 VL 32B Thinking
Open in Playground

FAQ

Common questions about Ling 3.0 Flash Fin vs Qwen3 VL 32B Thinking.

Which is better, Ling 3.0 Flash Fin or Qwen3 VL 32B Thinking?

Ling 3.0 Flash Fin leads the LLM Stats Score 43.2 to 23.6. Ling 3.0 Flash Fin is made by InclusionAI and Qwen3 VL 32B Thinking is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Ling 3.0 Flash Fin compare to Qwen3 VL 32B Thinking in benchmarks?

Ling 3.0 Flash Fin scores SpreadSheetBench-v1: 86.5%, Finance Agent v1.1: 69.2%, Finance Agent v2: 59.8%, Tau3 Banking: 41.0%, APEX-Agents: 29.2%. Qwen3 VL 32B Thinking scores DocVQAtest: 96.1%, ScreenSpot: 95.7%, MMLU-Redux: 91.9%, MMBench-V1.1: 90.8%, CharXiv-D: 90.2%.

What are the context window sizes for Ling 3.0 Flash Fin and Qwen3 VL 32B Thinking?

Ling 3.0 Flash Fin supports 262K tokens and Qwen3 VL 32B Thinking supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Ling 3.0 Flash Fin and Qwen3 VL 32B Thinking?

Key differences include LLM Stats Score (43.2 vs 23.6), multimodal support (no vs yes), licensing (Unknown vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Ling 3.0 Flash Fin and Qwen3 VL 32B Thinking?

Ling 3.0 Flash Fin is developed by InclusionAI and Qwen3 VL 32B Thinking is developed by Alibaba Cloud / Qwen Team.

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