Model Comparison

GLM-5 vs Qwen3 VL 8B Thinking

Comparing GLM-5 and Qwen3 VL 8B Thinking 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 8B Thinking 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 8B Thinking costs less

For input processing, GLM-5 ($1.00/1M tokens) is 5.6x more expensive than Qwen3 VL 8B Thinking ($0.18/1M tokens).

For output processing, GLM-5 ($3.20/1M tokens) is 1.5x more expensive than Qwen3 VL 8B Thinking ($2.09/1M tokens).

In conclusion, GLM-5 is more expensive than Qwen3 VL 8B Thinking.*

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

Lowest available price from all providers

Thu Apr 16 2026 • llm-stats.com

GLM-5

Input tokens$1.00

Output tokens$3.20

Best providerUnknown Organization

Qwen3 VL 8B Thinking

Input tokens$0.18

Output tokens$2.09

Best providerDeepinfra

Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

735.0B diff

GLM-5 has 735.0B more parameters than Qwen3 VL 8B Thinking, making it 8166.7% larger.

GLM-5

744.0Bparameters

Qwen3 VL 8B Thinking

9.0Bparameters

744.0B

GLM-5

9.0B

Qwen3 VL 8B Thinking

Context Window

Maximum input and output token capacity

Qwen3 VL 8B Thinking accepts 262,144 input tokens compared to GLM-5's 200,000 tokens. Qwen3 VL 8B Thinking can generate longer responses up to 262,144 tokens, while GLM-5 is limited to 128,000 tokens.

GLM-5

Input200,000 tokens

Output128,000 tokens

Qwen3 VL 8B Thinking

Input262,144 tokens

Output262,144 tokens

Thu Apr 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3 VL 8B Thinking supports multimodal inputs, whereas GLM-5 does not.

Qwen3 VL 8B Thinking 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 8B Thinking

Text

Images

Audio

Video

License

Usage and distribution terms

GLM-5 is licensed under MIT, while Qwen3 VL 8B Thinking 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 8B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while Qwen3 VL 8B Thinking was released on 2025-09-22.

GLM-5 is 5 months newer than Qwen3 VL 8B Thinking.

GLM-5

Feb 11, 2026

2 months ago

4mo newer

Qwen3 VL 8B Thinking

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 8B Thinking is available from DeepInfra.

GLM-5

Unknown Organization

Input Price:Input: $1.00/1MOutput Price:Output: $3.20/1M

Qwen3 VL 8B Thinking

Deepinfra

Input Price:Input: $0.18/1MOutput Price:Output: $2.09/1M

* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

GLM-5

View details

Zhipu AI

Qwen3 VL 8B Thinking

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	GLM-5	Qwen3 VL 8B Thinking

FAQ

Common questions about GLM-5 vs Qwen3 VL 8B Thinking

GLM-5 (Zhipu AI) and Qwen3 VL 8B Thinking (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 8B Thinking scores DocVQAtest: 95.3%, ScreenSpot: 93.6%, MMLU-Redux: 88.8%, MMBench-V1.1: 87.5%, InfoVQAtest: 86.0%.

Qwen3 VL 8B Thinking is 5.6x cheaper for input tokens. GLM-5 costs $1.00/M input and $3.20/M output via z. Qwen3 VL 8B Thinking costs $0.18/M input and $2.09/M output via deepinfra.

GLM-5 supports 200K tokens and Qwen3 VL 8B Thinking 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 $0.18/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 8B Thinking is developed by Alibaba Cloud / Qwen Team.

GLM-5 vs Qwen3 VL 8B Thinking

Performance Benchmarks

Arena Performance

Pricing Analysis

Model Size

Context Window

Input Capabilities

GLM-5

Qwen3 VL 8B Thinking

License

Release Timeline

Knowledge Cutoff

Provider Availability

GLM-5

Qwen3 VL 8B Thinking

Outputs Comparison

Key Takeaways

GLM-5

Qwen3 VL 8B Thinking

Detailed Comparison

FAQ

Which is better, GLM-5 or Qwen3 VL 8B Thinking?

How does GLM-5 compare to Qwen3 VL 8B Thinking in benchmarks?

Is GLM-5 cheaper than Qwen3 VL 8B Thinking?

What are the context window sizes for GLM-5 and Qwen3 VL 8B Thinking?

What are the main differences between GLM-5 and Qwen3 VL 8B Thinking?

Who makes GLM-5 and Qwen3 VL 8B Thinking?