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GLM-5.3 vs Laguna S 2.1

GLM-5.3 leads the LLM Stats Score 54.2 to 41.4. Laguna S 2.1 is 17.2x cheaper per token.

Zhipu AI · Poolside · Updated for 2026

Which is better?

GLM-5.3 leads the overall LLM Stats Score 54.2 to 41.4, ranking #6 overall.

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

On price, Laguna S 2.1 is roughly 17.2x 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

  • overall performance matters — it scores 54.2 and ranks #6 on LLM Stats
  • your work emphasizes reasoning and coding — 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 17.2x cheaper per token
  • you need open weights you can self-host or fine-tune

At a glance

The differences that matter most.

GLM-5.3
Zhipu AI
Laguna S 2.1
Poolside
Core performance indexes
LLM Stats Score
54.2
#6
41.4
#43
Reasoning
53.4
#5
41.5
#40
Coding
45.4
#6
33.1
#37
Agents
41.2
#5
25.7
#43
Cost, coverage & limits
Benchmark wins
3 of 3
0 of 3
Input price
$1.40 / M
$0.10 / M
Output price
$4.40 / 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
Laguna S 2.1
Tool use
35.4#2
18.9#56
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

16 reported for GLM-5.3 · 6 for Laguna S 2.1

3 shared

GLM-5.3 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 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 ($1.40/1M tokens) is 14.0x more expensive than Laguna S 2.1 ($0.10/1M tokens).

For output processing, GLM-5.3 ($4.40/1M tokens) is 22.0x more expensive than Laguna S 2.1 ($0.20/1M tokens).

In conclusion, GLM-5.3 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
Input tokens$1.40
Output tokens$4.40
Best providerNovita
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

635.0B diff

GLM-5.3 has 635.0B more parameters than Laguna S 2.1, making it 538.1% larger.

Zhipu AI
GLM-5.3
753.0Bparameters
Poolside
Laguna S 2.1
118.0Bparameters
753.0B
GLM-5.3
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 specifies output context (131,072 tokens).

Zhipu AI
GLM-5.3
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

Release Timeline

When each model was launched

GLM-5.3 was released on 2026-08-14, while Laguna S 2.1 was released on 2026-07-21.

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

GLM-5.3

Aug 14, 2026

2 weeks ago

3w 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 is available from Novita, ZAI. Laguna S 2.1 is available from Poolside.

GLM-5.3

novita logo
Novita
Input Price:Input: $1.40/1MOutput Price:Output: $4.40/1M
z logo
Unknown Organization
Input Price:Input: $1.40/1MOutput Price:Output: $4.40/1M

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 and Laguna S 2.1 side-by-side, then vote on the output you prefer.

GLM-5.3
✓ Preferred
Laguna S 2.1
Open in Playground

FAQ

Common questions about GLM-5.3 vs Laguna S 2.1.

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

GLM-5.3 leads the LLM Stats Score 54.2 to 41.4. GLM-5.3 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 compare to Laguna S 2.1 in benchmarks?

GLM-5.3 scores Terminal-Bench 2.1: 88.2%, CyberGym: 84.5%, FrontierSWE: 78.1%, Toolathlon: 73.0%, DeepSWE 1.1: 66.9%. 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 cheaper than Laguna S 2.1?

Laguna S 2.1 is 14.0x cheaper for input tokens. GLM-5.3 costs $1.40/M input and $4.40/M output via novita. 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 and Laguna S 2.1?

GLM-5.3 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 and Laguna S 2.1?

Key differences include LLM Stats Score (54.2 vs 41.4), input pricing ($1.40 vs $0.10/M), licensing (Unknown vs OpenMDW License v1.1). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5.3 and Laguna S 2.1?

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

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