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GLM-5.3-Flash vs SWE-2 Flash

GLM-5.3-Flash and SWE-2 Flash are closely matched at 47.9 and 43.8 on the LLM Stats Score.

Zhipu AI · Cognition · Updated for 2026

Which is better?

GLM-5.3-Flash and SWE-2 Flash are closely matched on the overall LLM Stats Score at 47.9 and 43.8.

The models split the 2 individual benchmarks reported for both models evenly.

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 SWE-2 Flash

  • you want the most recent training data — it shipped Nov 2026

At a glance

The differences that matter most.

GLM-5.3-Flash
Zhipu AI
SWE-2 Flash
Cognition
Core performance indexes
LLM Stats Score
47.9
#34
43.8
#59
Reasoning
47.0
#31
42.6
#61
Coding
30.0
#68
33.0
#44
Agents
33.0
#37
30.1
#50
Cost, coverage & limits
Benchmark wins
1 of 2
1 of 2
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
SWE-2 Flash
Tool use
28.8#28
23.0#58
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

15 reported for GLM-5.3-Flash · 7 for SWE-2 Flash

2 shared

GLM-5.3-Flash outperforms in 1 benchmarks (DeepSWE 1.1), while SWE-2 Flash is better at 1 benchmark (Terminal-Bench 2.1).

Both models are evenly matched across the benchmarks.

Thu Nov 12 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

44.0B diff

GLM-5.3-Flash has 44.0B more parameters than SWE-2 Flash, making it 15.9% larger.

Zhipu AI
GLM-5.3-Flash
320.0Bparameters
Cognition
SWE-2 Flash
276.0Bparameters
320.0B
GLM-5.3-Flash
276.0B
SWE-2 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
Cognition
SWE-2 Flash
Input- tokens
Output- tokens
Thu Nov 12 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

GLM-5.3-Flash supports multimodal inputs, whereas SWE-2 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

SWE-2 Flash

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5.3-Flash is licensed under MIT, while SWE-2 Flash uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

GLM-5.3-Flash

MIT

Open weights

SWE-2 Flash

Proprietary

Closed source

Release Timeline

When each model was launched

GLM-5.3-Flash was released on 2026-08-26, while SWE-2 Flash was released on 2026-11-04.

SWE-2 Flash is 2 months newer than GLM-5.3-Flash.

GLM-5.3-Flash

Aug 26, 2026

2 months ago

SWE-2 Flash

Nov 4, 2026

1 weeks ago

2mo 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 SWE-2 Flash side-by-side, then vote on the output you prefer.

GLM-5.3-Flash
✓ Preferred
SWE-2 Flash
Open in Playground

FAQ

Common questions about GLM-5.3-Flash vs SWE-2 Flash.

Which is better, GLM-5.3-Flash or SWE-2 Flash?

GLM-5.3-Flash and SWE-2 Flash are closely matched on the LLM Stats Score at 47.9 and 43.8. GLM-5.3-Flash is made by Zhipu AI and SWE-2 Flash is made by Cognition. 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 SWE-2 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%. SWE-2 Flash scores Terminal-Bench 2.1: 89.2%, DeepSWE 1.1: 60.1%, SWE Atlas - Codebase QnA: 50.0%, FrontierCode 1.1: 45.5%, ExploitBench: 34.8%.

What are the context window sizes for GLM-5.3-Flash and SWE-2 Flash?

GLM-5.3-Flash supports 1.0M tokens and SWE-2 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 SWE-2 Flash?

Key differences include LLM Stats Score (47.9 vs 43.8), multimodal support (yes vs no), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5.3-Flash and SWE-2 Flash?

GLM-5.3-Flash is developed by Zhipu AI and SWE-2 Flash is developed by Cognition.

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