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GLM-5.3-Flash vs Ling 3.1 Flash

GLM-5.3-Flash and Ling 3.1 Flash are closely matched at 49.0 and 51.1 on the LLM Stats Score.

Zhipu AI · InclusionAI · Updated for 2026

Which is better?

GLM-5.3-Flash and Ling 3.1 Flash are closely matched on the overall LLM Stats Score at 49.0 and 51.1.

In the 1 individual benchmarks reported for both models, Ling 3.1 Flash wins 1; this is a narrower head-to-head signal than the composite indexes.

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

Choose GLM-5.3-Flash

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

Choose Ling 3.1 Flash

  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you want the most recent training data — it shipped Sep 2026

At a glance

The differences that matter most.

GLM-5.3-Flash
Zhipu AI
Ling 3.1 Flash
InclusionAI
Core performance indexes
LLM Stats Score
49.0
#23
51.1
#14
Reasoning
48.0
#22
47.7
#25
Coding
33.0
#43
39.2
#17
Agents
35.5
#21
38.3
#11
Cost, coverage & limits
Benchmark wins
0 of 1
1 of 1
Input price
$0.15 / M
— / M
Output price
$0.50 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
GLM-5.3-Flash
Ling 3.1 Flash
Tool use
28.5#17
29.0#13
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

15 reported for GLM-5.3-Flash · 11 for Ling 3.1 Flash

1 shared

GLM-5.3-Flash outperforms in 0 benchmarks, while Ling 3.1 Flash is better at 1 benchmark (AutomationBench).

Ling 3.1 Flash significantly outperforms across most benchmarks.

Wed Oct 07 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

240.0B diff

Ling 3.1 Flash has 240.0B more parameters than GLM-5.3-Flash, making it 75.0% larger.

Zhipu AI
GLM-5.3-Flash
320.0Bparameters
InclusionAI
Ling 3.1 Flash
560.0Bparameters
320.0B
GLM-5.3-Flash
560.0B
Ling 3.1 Flash

Context Window

Maximum input and output token capacity

Only GLM-5.3-Flash specifies input context (1,048,576 tokens). Only GLM-5.3-Flash specifies output context (1,048,576 tokens).

Zhipu AI
GLM-5.3-Flash
Input1,048,576 tokens
Output1,048,576 tokens
InclusionAI
Ling 3.1 Flash
Input- tokens
Output- tokens
Wed Oct 07 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

GLM-5.3-Flash supports multimodal inputs, whereas Ling 3.1 Flash does not.

GLM-5.3-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-5.3-Flash

Text
Images
Audio
Video

Ling 3.1 Flash

Text
Images
Audio
Video

Release Timeline

When each model was launched

GLM-5.3-Flash was released on 2026-08-26, while Ling 3.1 Flash was released on 2026-09-30.

Ling 3.1 Flash is 1 month newer than GLM-5.3-Flash.

GLM-5.3-Flash

Aug 26, 2026

1 months ago

Ling 3.1 Flash

Sep 30, 2026

1 weeks ago

1mo newer

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 GLM-5.3-Flash and Ling 3.1 Flash side-by-side, then vote on the output you prefer.

GLM-5.3-Flash
✓ Preferred
Ling 3.1 Flash
Open in Playground

FAQ

Common questions about GLM-5.3-Flash vs Ling 3.1 Flash.

Which is better, GLM-5.3-Flash or Ling 3.1 Flash?

GLM-5.3-Flash and Ling 3.1 Flash are closely matched on the LLM Stats Score at 49.0 and 51.1. GLM-5.3-Flash is made by Zhipu AI and Ling 3.1 Flash is made by InclusionAI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does GLM-5.3-Flash compare to Ling 3.1 Flash in benchmarks?

GLM-5.3-Flash scores CharXiv-R: 89.4%, Terminal-Bench 2.1: 84.3%, MMVU: 80.5%, Toolathlon: 78.4%, Chartography: 78.0%. Ling 3.1 Flash scores CyberGym: 87.9%, DRACO: 85.5%, FrontierSWE: 75.2%, Multi-Challenge: 69.8%, SkillsBench: 68.7%.

What are the context window sizes for GLM-5.3-Flash and Ling 3.1 Flash?

GLM-5.3-Flash supports 1.0M tokens and Ling 3.1 Flash 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 GLM-5.3-Flash and Ling 3.1 Flash?

Key differences include LLM Stats Score (49.0 vs 51.1), multimodal support (yes vs no), licensing (MIT vs Unknown). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5.3-Flash and Ling 3.1 Flash?

GLM-5.3-Flash is developed by Zhipu AI and Ling 3.1 Flash is developed by InclusionAI.

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