Model Comparison

GLM-5 vs Qwen3 VL 4B Instruct

Comparing GLM-5 and Qwen3 VL 4B Instruct across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-5 and Qwen3 VL 4B Instruct don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Qwen3 VL 4B Instruct costs less

For input processing, GLM-5 ($1.00/1M tokens) is 10.0x more expensive than Qwen3 VL 4B Instruct ($0.10/1M tokens).

For output processing, GLM-5 ($3.20/1M tokens) is 5.3x more expensive than Qwen3 VL 4B Instruct ($0.60/1M tokens).

In conclusion, GLM-5 is more expensive than Qwen3 VL 4B Instruct.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Thu Apr 16 2026 • llm-stats.com
Zhipu AI
GLM-5
Input tokens$1.00
Output tokens$3.20
Best providerUnknown Organization
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Instruct
Input tokens$0.10
Output tokens$0.60
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

740.0B diff

GLM-5 has 740.0B more parameters than Qwen3 VL 4B Instruct, making it 18500.0% larger.

Zhipu AI
GLM-5
744.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Instruct
4.0Bparameters
744.0B
GLM-5
4.0B
Qwen3 VL 4B Instruct

Context Window

Maximum input and output token capacity

Qwen3 VL 4B Instruct accepts 262,144 input tokens compared to GLM-5's 200,000 tokens. Qwen3 VL 4B Instruct can generate longer responses up to 262,144 tokens, while GLM-5 is limited to 128,000 tokens.

Zhipu AI
GLM-5
Input200,000 tokens
Output128,000 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Instruct
Input262,144 tokens
Output262,144 tokens
Thu Apr 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3 VL 4B Instruct supports multimodal inputs, whereas GLM-5 does not.

Qwen3 VL 4B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-5

Text
Images
Audio
Video

Qwen3 VL 4B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5 is licensed under MIT, while Qwen3 VL 4B Instruct uses Apache 2.0.

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

GLM-5

MIT

Open weights

Qwen3 VL 4B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while Qwen3 VL 4B Instruct was released on 2025-09-22.

GLM-5 is 5 months newer than Qwen3 VL 4B Instruct.

GLM-5

Feb 11, 2026

2 months ago

4mo newer
Qwen3 VL 4B Instruct

Sep 22, 2025

6 months ago

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

Provider Availability

GLM-5 is available from ZAI. Qwen3 VL 4B Instruct is available from DeepInfra.

GLM-5

z logo
Unknown Organization
Input Price:Input: $1.00/1MOutput Price:Output: $3.20/1M

Qwen3 VL 4B Instruct

deepinfra logo
Deepinfra
Input Price:Input: $0.10/1MOutput Price:Output: $0.60/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Zhipu AI
Alibaba Cloud / Qwen Team

Qwen3 VL 4B Instruct

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)
Supports multimodal inputs
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-5
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Instruct

FAQ

Common questions about GLM-5 vs Qwen3 VL 4B Instruct

GLM-5 (Zhipu AI) and Qwen3 VL 4B Instruct (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.
GLM-5 scores t2-bench: 89.7%, SWE-Bench Verified: 77.8%, BrowseComp: 75.9%, MCP Atlas: 67.8%, Terminal-Bench 2.0: 56.2%. Qwen3 VL 4B Instruct scores DocVQAtest: 95.3%, ScreenSpot: 94.0%, OCRBench: 88.1%, MMBench-V1.1: 85.1%, AI2D: 84.1%.
Qwen3 VL 4B Instruct is 10.0x cheaper for input tokens. GLM-5 costs $1.00/M input and $3.20/M output via z. Qwen3 VL 4B Instruct costs $0.10/M input and $0.60/M output via deepinfra.
GLM-5 supports 200K tokens and Qwen3 VL 4B Instruct supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (200K vs 262K), input pricing ($1.00 vs $0.10/M), multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
GLM-5 is developed by Zhipu AI and Qwen3 VL 4B Instruct is developed by Alibaba Cloud / Qwen Team.