The AI arena is free today

Open Superagent

GLM-5.3-Flash vs Laguna S 2.1

GLM-5.3-Flash leads the LLM Stats Score 51.6 to 41.4. Laguna S 2.1 is 1.9x cheaper per token.

Zhipu AI · Poolside · Updated for 2026

Which is better?

GLM-5.3-Flash leads the overall LLM Stats Score 51.6 to 41.4, ranking #11 overall.

In the 3 individual benchmarks reported for both models, GLM-5.3-Flash wins 3; this is a narrower head-to-head signal than the composite indexes.

On price, Laguna S 2.1 is roughly 1.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

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

Choose GLM-5.3-Flash

  • overall performance matters — it scores 51.6 and ranks #11 on LLM Stats
  • your work emphasizes agents — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 3 of 3 exact shared results
  • you want the most recent training data — it shipped Aug 2026

Choose Laguna S 2.1

  • cost matters — it's about 1.9x cheaper per token

At a glance

The differences that matter most.

GLM-5.3-Flash
Zhipu AI
Laguna S 2.1
Poolside
Core performance indexes
LLM Stats Score
51.6
#11
41.4
#43
Reasoning
50.3
#13
41.5
#40
Coding
37.8
#22
33.1
#37
Agents
39.1
#9
25.7
#43
Cost, coverage & limits
Benchmark wins
3 of 3
0 of 3
Input price
$0.15 / M
$0.10 / M
Output price
$0.50 / M
$0.20 / M
Context window
1,048,576
1,048,576

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
GLM-5.3-Flash
Laguna S 2.1
Tool use
34.2#4
18.9#56
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

15 reported for GLM-5.3-Flash · 6 for Laguna S 2.1

3 shared

GLM-5.3-Flash outperforms in 3 benchmarks (DeepSWE 1.1, Terminal-Bench 2.1, Toolathlon), while Laguna S 2.1 is better at 0 benchmarks.

GLM-5.3-Flash significantly outperforms across most benchmarks.

Fri Aug 28 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Laguna S 2.1 costs less

For input processing, GLM-5.3-Flash ($0.15/1M tokens) is 1.5x more expensive than Laguna S 2.1 ($0.10/1M tokens).

For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 2.5x more expensive than Laguna S 2.1 ($0.20/1M tokens).

In conclusion, GLM-5.3-Flash is more expensive than Laguna S 2.1.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Fri Aug 28 2026 • llm-stats.com
Zhipu AI
GLM-5.3-Flash
Input tokens$0.15
Output tokens$0.50
Best providerDeepinfra
Poolside
Laguna S 2.1
Input tokens$0.10
Output tokens$0.20
Best providerPoolside
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

202.0B diff

GLM-5.3-Flash has 202.0B more parameters than Laguna S 2.1, making it 171.2% larger.

Zhipu AI
GLM-5.3-Flash
320.0Bparameters
Poolside
Laguna S 2.1
118.0Bparameters
320.0B
GLM-5.3-Flash
118.0B
Laguna S 2.1

Context Window

Maximum input and output token capacity

Both models have the same input context window of 1,048,576 tokens. Only GLM-5.3-Flash specifies output context (131,072 tokens).

Zhipu AI
GLM-5.3-Flash
Input1,048,576 tokens
Output131,072 tokens
Poolside
Laguna S 2.1
Input1,048,576 tokens
Output- tokens
Fri Aug 28 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

GLM-5.3-Flash supports multimodal inputs, whereas Laguna S 2.1 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

Laguna S 2.1

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5.3-Flash is licensed under MIT, while Laguna S 2.1 uses OpenMDW License v1.1.

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

GLM-5.3-Flash

MIT

Open weights

Laguna S 2.1

OpenMDW License v1.1

Open weights

Release Timeline

When each model was launched

GLM-5.3-Flash was released on 2026-08-26, while Laguna S 2.1 was released on 2026-07-21.

GLM-5.3-Flash is 1 month newer than Laguna S 2.1.

GLM-5.3-Flash

Aug 26, 2026

2 days ago

1mo newer
Laguna S 2.1

Jul 21, 2026

1 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

Provider Availability

GLM-5.3-Flash is available from DeepInfra, Novita, ZAI. Laguna S 2.1 is available from Poolside.

GLM-5.3-Flash

deepinfra logo
Deepinfra
Input Price:Input: $0.15/1MOutput Price:Output: $0.50/1M
novita logo
Novita
Input Price:Input: $0.15/1MOutput Price:Output: $0.50/1M
z logo
Unknown Organization
Input Price:Input: $0.15/1MOutput Price:Output: $0.50/1M

Laguna S 2.1

poolside logo
Poolside
Input Price:Input: $0.10/1MOutput Price:Output: $0.20/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against GLM-5.3-Flash and Laguna S 2.1 side-by-side, then vote on the output you prefer.

GLM-5.3-Flash
✓ Preferred
Laguna S 2.1
Open in Playground

FAQ

Common questions about GLM-5.3-Flash vs Laguna S 2.1.

Which is better, GLM-5.3-Flash or Laguna S 2.1?

GLM-5.3-Flash leads the LLM Stats Score 51.6 to 41.4. GLM-5.3-Flash is made by Zhipu AI and Laguna S 2.1 is made by Poolside. 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 Laguna S 2.1 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%. Laguna S 2.1 scores SWE-bench Multilingual: 78.5%, Terminal-Bench 2.1: 70.2%, SWE-Bench Pro: 59.4%, Toolathlon: 49.7%, SWE Atlas - Codebase QnA: 46.2%.

Is GLM-5.3-Flash cheaper than Laguna S 2.1?

Laguna S 2.1 is 1.5x cheaper for input tokens. GLM-5.3-Flash costs $0.15/M input and $0.50/M output via deepinfra. Laguna S 2.1 costs $0.10/M input and $0.20/M output via poolside.

What are the context window sizes for GLM-5.3-Flash and Laguna S 2.1?

GLM-5.3-Flash supports 1.0M tokens and Laguna S 2.1 supports 1.0M 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 Laguna S 2.1?

Key differences include LLM Stats Score (51.6 vs 41.4), input pricing ($0.15 vs $0.10/M), multimodal support (yes vs no), licensing (MIT vs OpenMDW License v1.1). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5.3-Flash and Laguna S 2.1?

GLM-5.3-Flash is developed by Zhipu AI and Laguna S 2.1 is developed by Poolside.

Related comparisons