GLM-4.7 vs Laguna XS 2.1
GLM-4.7 leads the LLM Stats Score 34.3 to 24.4. Laguna XS 2.1 is 5.9x cheaper per token.
Zhipu AI · Poolside · Updated for 2026
Which is better?
GLM-4.7 leads the overall LLM Stats Score 34.3 to 24.4, ranking #95 overall.
In the 3 individual benchmarks reported for both models, GLM-4.7 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, Laguna XS 2.1 is roughly 5.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Laguna XS 2.1 also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GLM-4.7
- overall performance matters — it scores 34.3 and ranks #95 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
Choose Laguna XS 2.1
- cost matters — it's about 5.9x cheaper per token
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Jul 2026
At a glance
The differences that matter most.
Individual benchmarks
13 reported for GLM-4.7 · 4 for Laguna XS 2.1
GLM-4.7 outperforms in 3 benchmarks (SWE-bench Multilingual, SWE-Bench Verified, Terminal-Bench 2.0), while Laguna XS 2.1 is better at 0 benchmarks.
GLM-4.7 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.7 ($0.40/1M tokens) is 4.0x more expensive than Laguna XS 2.1 ($0.10/1M tokens).
For output processing, GLM-4.7 ($1.75/1M tokens) is 8.8x more expensive than Laguna XS 2.1 ($0.20/1M tokens).
In conclusion, GLM-4.7 is more expensive than Laguna XS 2.1.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.7 has 325.0B more parameters than Laguna XS 2.1, making it 984.8% larger.
Context Window
Maximum input and output token capacity
Laguna XS 2.1 accepts 262,144 input tokens compared to GLM-4.7's 202,752 tokens. Only GLM-4.7 specifies output context (202,752 tokens).
Input capabilities
Documented input modalities across available providers
GLM-4.7 supports multimodal inputs, whereas Laguna XS 2.1 does not.
GLM-4.7 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.7
Laguna XS 2.1
License
Usage and distribution terms
GLM-4.7 is licensed under MIT, while Laguna XS 2.1 uses OpenMDW License v1.1.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
OpenMDW License v1.1
Open weights
Release Timeline
When each model was launched
GLM-4.7 was released on 2025-12-22, while Laguna XS 2.1 was released on 2026-07-02.
Laguna XS 2.1 is 6 months newer than GLM-4.7.
Dec 22, 2025
9 months ago
Jul 2, 2026
2 months ago
6mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-4.7 is available from DeepInfra, Fireworks, Novita. Laguna XS 2.1 is available from Poolside.
GLM-4.7
Laguna XS 2.1
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.7 and Laguna XS 2.1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.7 vs Laguna XS 2.1.