GLM-4.6 vs Parse
Comparing GLM-4.6 and Parse across benchmarks, pricing, and capabilities.
Zhipu AI · Cohere · Updated for 2026
Which is better?
GLM-4.6 and Parse trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
GLM-4.6 also accepts a larger context window (131,072 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-4.6
- you process long inputs — it offers a 131,072 token context window
- you need open weights you can self-host or fine-tune
Choose Parse
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Individual benchmarks
7 reported for GLM-4.6 · 1 for Parse
GLM-4.6 and Parsedon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
GLM-4.6 has 354.7B more parameters than Parse, making it 15421.7% larger.
Context Window
Maximum input and output token capacity
GLM-4.6 accepts 131,072 input tokens compared to Parse's 8,192 tokens. Only GLM-4.6 specifies output context (131,072 tokens).
Input capabilities
Documented input modalities across available providers
Both GLM-4.6 and Parse support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-4.6
Parse
License
Usage and distribution terms
GLM-4.6 is licensed under MIT, while Parse 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-4.6 was released on 2025-09-30, while Parse was released on 2026-08-27.
Parse is 11 months newer than GLM-4.6.
Sep 30, 2025
11 months ago
Aug 27, 2026
1 weeks ago
11mo 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-4.6 is available from Fireworks, DeepInfra. Parse is available from Azure, Cohere.
GLM-4.6
Parse
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.6 and Parse side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.6 vs Parse.