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Ling 3.1 Flash vs Qwen2.5 72B Instruct

Ling 3.1 Flash leads the LLM Stats Score 51.1 to 12.1.

InclusionAI · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Ling 3.1 Flash leads the overall LLM Stats Score 51.1 to 12.1, ranking #14 overall.

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

Choose Ling 3.1 Flash

  • overall performance matters — it scores 51.1 and ranks #14 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Sep 2026

Choose Qwen2.5 72B Instruct

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

At a glance

The differences that matter most.

Core performance indexes
51.1
#14
12.1
#268
47.7
#25
12.2
#260
39.2
#17
9.1
#188
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
$0.35 / M
Output price
— / M
$0.40 / M
Context window
—
131,072

Individual benchmarks

11 reported for Ling 3.1 Flash · 14 for Qwen2.5 72B Instruct

No common benchmarks found

Ling 3.1 Flash and Qwen2.5 72B Instructdon'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

487.3B diff

Ling 3.1 Flash has 487.3B more parameters than Qwen2.5 72B Instruct, making it 670.3% larger.

InclusionAI
Ling 3.1 Flash
560.0Bparameters
Alibaba Cloud / Qwen Team
Qwen2.5 72B Instruct
72.7Bparameters
560.0B
Ling 3.1 Flash
72.7B
Qwen2.5 72B Instruct

Context Window

Maximum input and output token capacity

Only Qwen2.5 72B Instruct specifies input context (131,072 tokens). Only Qwen2.5 72B Instruct specifies output context (8,192 tokens).

InclusionAI
Ling 3.1 Flash
Input- tokens
Output- tokens
Alibaba Cloud / Qwen Team
Qwen2.5 72B Instruct
Input131,072 tokens
Output8,192 tokens
Wed Oct 07 2026 • llm-stats.com

Release Timeline

When each model was launched

Ling 3.1 Flash was released on 2026-09-30, while Qwen2.5 72B Instruct was released on 2024-09-19.

Ling 3.1 Flash is 25 months newer than Qwen2.5 72B Instruct.

Ling 3.1 Flash

Sep 30, 2026

1 weeks ago

2.0yr newer
Qwen2.5 72B Instruct

Sep 19, 2024

2.0 years 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?

Judge for yourself.

Run your own prompts against Ling 3.1 Flash and Qwen2.5 72B Instruct side-by-side, then vote on the output you prefer.

Ling 3.1 Flash
✓ Preferred
Qwen2.5 72B Instruct
Open in Playground

FAQ

Common questions about Ling 3.1 Flash vs Qwen2.5 72B Instruct.

Which is better, Ling 3.1 Flash or Qwen2.5 72B Instruct?

Ling 3.1 Flash leads the LLM Stats Score 51.1 to 12.1. Ling 3.1 Flash is made by InclusionAI and Qwen2.5 72B Instruct 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 3.1 Flash compare to Qwen2.5 72B Instruct in benchmarks?

Ling 3.1 Flash scores CyberGym: 87.9%, DRACO: 85.5%, FrontierSWE: 75.2%, Multi-Challenge: 69.8%, SkillsBench: 68.7%. Qwen2.5 72B Instruct scores GSM8k: 95.8%, MT-Bench: 93.5%, MBPP: 88.2%, MMLU-Redux: 86.8%, HumanEval: 86.6%.

What are the context window sizes for Ling 3.1 Flash and Qwen2.5 72B Instruct?

Ling 3.1 Flash supports an unknown number of tokens and Qwen2.5 72B Instruct supports 131K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Ling 3.1 Flash and Qwen2.5 72B Instruct?

Key differences include LLM Stats Score (51.1 vs 12.1), licensing (Unknown vs Qwen). See the full comparison above for benchmark-by-benchmark results.

Who makes Ling 3.1 Flash and Qwen2.5 72B Instruct?

Ling 3.1 Flash is developed by InclusionAI and Qwen2.5 72B Instruct is developed by Alibaba Cloud / Qwen Team.