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GLM-4.5V vs Qwen3.8-27B

Comparing GLM-4.5V and Qwen3.8-27B across benchmarks, pricing, and capabilities.

Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

GLM-4.5V and Qwen3.8-27B trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

Based on current benchmark, pricing, and model metadata for 2026.

Choose GLM-4.5V

  • you want predictable pricing at $0.55/M input and $2.19/M output

Choose Qwen3.8-27B

  • you want the most recent training data — it shipped Aug 2026

At a glance

The differences that matter most.

GLM-4.5V
Zhipu AI
Qwen3.8-27B
Alibaba Cloud / Qwen Team
Benchmark wins
—
—
Input price
$0.55 / M
— / M
Output price
$2.19 / M
— / M
Context window
131,072
—
Released
Aug 2025
Aug 2026
License
MIT
Apache 2.0

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-4.5V and Qwen3.8-27Bdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

80.2B diff

GLM-4.5V has 80.2B more parameters than Qwen3.8-27B, making it 288.7% larger.

Zhipu AI
GLM-4.5V
108.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3.8-27B
27.8Bparameters
108.0B
GLM-4.5V
27.8B
Qwen3.8-27B

Context Window

Maximum input and output token capacity

Only GLM-4.5V specifies input context (131,072 tokens). Only GLM-4.5V specifies output context (131,072 tokens).

Zhipu AI
GLM-4.5V
Input131,072 tokens
Output131,072 tokens
Alibaba Cloud / Qwen Team
Qwen3.8-27B
Input- tokens
Output- tokens
Tue Aug 25 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both GLM-4.5V and Qwen3.8-27B support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

GLM-4.5V

Text
Images
Audio
Video

Qwen3.8-27B

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-4.5V is licensed under MIT, while Qwen3.8-27B uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

GLM-4.5V

MIT

Open weights

Qwen3.8-27B

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-4.5V was released on 2025-08-11, while Qwen3.8-27B was released on 2026-08-14.

Qwen3.8-27B is 12 months newer than GLM-4.5V.

GLM-4.5V

Aug 11, 2025

1.0 years ago

Qwen3.8-27B

Aug 14, 2026

1 weeks ago

1.0yr 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-4.5V and Qwen3.8-27B side-by-side, then vote on the output you prefer.

GLM-4.5V
✓ Preferred
Qwen3.8-27B
Open in Playground

FAQ

Common questions about GLM-4.5V vs Qwen3.8-27B.

Which is better, GLM-4.5V or Qwen3.8-27B?

GLM-4.5V (Zhipu AI) and Qwen3.8-27B (Alibaba Cloud / Qwen Team) 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-4.5V compare to Qwen3.8-27B in benchmarks?

Qwen3.8-27B scores MathVision: 94.6%, OmniDocBench 1.5: 91.1%, LiveCodeBench v6: 90.3%, CharXiv-R: 90.2%, GPQA: 89.2%.

What are the context window sizes for GLM-4.5V and Qwen3.8-27B?

GLM-4.5V supports 131K tokens and Qwen3.8-27B supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GLM-4.5V and Qwen3.8-27B?

Key differences include licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-4.5V and Qwen3.8-27B?

GLM-4.5V is developed by Zhipu AI and Qwen3.8-27B is developed by Alibaba Cloud / Qwen Team.

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