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GLM-4.5-Air vs Parse

Comparing GLM-4.5-Air and Parse across benchmarks, pricing, and capabilities.

Zhipu AI · Cohere · Updated for 2026

Which is better?

GLM-4.5-Air and Parse trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

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

Choose GLM-4.5-Air

  • 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.

GLM-4.5-Air
Zhipu AI
Parse
Cohere
Benchmark wins
—
—
Input price
— / M
— / M
Output price
— / M
— / M
Context window
—
8,192

Individual benchmarks

14 reported for GLM-4.5-Air · 1 for Parse

No common benchmarks found

GLM-4.5-Air 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

103.7B diff

GLM-4.5-Air has 103.7B more parameters than Parse, making it 4508.7% larger.

Zhipu AI
GLM-4.5-Air
106.0Bparameters
Cohere
Parse
2.3Bparameters
106.0B
GLM-4.5-Air
2.3B
Parse

Context Window

Maximum input and output token capacity

Only Parse specifies input context (8,192 tokens).

Zhipu AI
GLM-4.5-Air
Input- tokens
Output- tokens
Cohere
Parse
Input8,192 tokens
Output- tokens
Fri Sep 04 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Parse supports multimodal inputs, whereas GLM-4.5-Air does not.

Parse can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-4.5-Air

Text
Images
Audio
Video

Parse

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-4.5-Air 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.

GLM-4.5-Air

MIT

Open weights

Parse

Proprietary

Closed source

Release Timeline

When each model was launched

GLM-4.5-Air was released on 2025-07-28, while Parse was released on 2026-08-27.

Parse is 13 months newer than GLM-4.5-Air.

GLM-4.5-Air

Jul 28, 2025

1.1 years ago

Parse

Aug 27, 2026

1 weeks ago

1.1yr newer

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

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against GLM-4.5-Air and Parse side-by-side, then vote on the output you prefer.

GLM-4.5-Air
✓ Preferred
Parse
Open in Playground

FAQ

Common questions about GLM-4.5-Air vs Parse.

Which is better, GLM-4.5-Air or Parse?

GLM-4.5-Air (Zhipu AI) and Parse (Cohere) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does GLM-4.5-Air compare to Parse in benchmarks?

GLM-4.5-Air scores MATH-500: 98.1%, AIME 2024: 89.4%, MMLU-Pro: 81.4%, TAU-bench Retail: 77.9%, BFCL-v3: 76.4%. Parse scores ParseBench: 79.2%.

What are the context window sizes for GLM-4.5-Air and Parse?

GLM-4.5-Air supports an unknown number of tokens and Parse supports 8K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GLM-4.5-Air and Parse?

Key differences include multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-4.5-Air and Parse?

GLM-4.5-Air is developed by Zhipu AI and Parse is developed by Cohere.

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