GLM-4.5 vs Particle-1
Particle-1 leads the LLM Stats Score 31.9 to 27.1. Particle-1 is 1.6x cheaper per token.
Zhipu AI · Particle AI · Updated for 2026
Which is better?
Particle-1 leads the overall LLM Stats Score 31.9 to 27.1, ranking #133 overall.
In the 1 individual benchmarks reported for both models, Particle-1 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Particle-1 is roughly 1.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Particle-1 also accepts a larger context window (1,048,576 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-4.5
- you need open weights you can self-host or fine-tune
Choose Particle-1
- overall performance matters — it scores 31.9 and ranks #133 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 1.6x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Individual benchmarks
14 reported for GLM-4.5 · 3 for Particle-1
GLM-4.5 outperforms in 0 benchmarks, while Particle-1 is better at 1 benchmark (GPQA).
Particle-1 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-4.5 ($0.40/1M tokens) is 1.6x more expensive than Particle-1 ($0.25/1M tokens).
For output processing, GLM-4.5 ($1.60/1M tokens) is 1.6x more expensive than Particle-1 ($1.00/1M tokens).
In conclusion, GLM-4.5 is more expensive than Particle-1.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Particle-1 accepts 1,048,576 input tokens compared to GLM-4.5's 131,072 tokens. Both models can generate responses up to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Particle-1 supports multimodal inputs, whereas GLM-4.5 does not.
Particle-1 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.5
Particle-1
License
Usage and distribution terms
GLM-4.5 is licensed under MIT, while Particle-1 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
GLM-4.5 was released on 2025-07-28, while Particle-1 was released on 2026-10-28.
Particle-1 is 15 months newer than GLM-4.5.
Jul 28, 2025
1.2 years ago
Oct 28, 2026
-1 days ago
1.3yr 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-4.5 is available from DeepInfra, Fireworks, Novita. Particle-1 is available from Particle AI.
GLM-4.5
Particle-1
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.5 and Particle-1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.5 vs Particle-1.