Model Comparison
GLM-4.7 vs Qwen3 VL 4B ThinkingWhich is better in 2026?
GLM-4.7 significantly outperforms across most benchmarks. Qwen3 VL 4B Thinking is 3.1x cheaper per token.
Verdict: GLM-4.7 vs Qwen3 VL 4B Thinking — which is better?
GLM-4.7 (by Zhipu AI) and Qwen3 VL 4B Thinking (by Alibaba Cloud / Qwen Team) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
GLM-4.7 outperforms in 4 benchmarks (AIME 2025, GPQA, LiveCodeBench v6, MMLU-Pro), while Qwen3 VL 4B Thinking is better at 0 benchmarks. GLM-4.7 significantly outperforms across most benchmarks.
On price, Qwen3 VL 4B Thinking is roughly 3.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 4B Thinking also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Choose GLM-4.7 if…
- you want the strongest raw capability — it leads on 4 of 4 shared benchmarks
- you want the most recent training data — it shipped Dec 2025
Choose Qwen3 VL 4B Thinking if…
- cost matters — it's about 3.1x cheaper per token
- you process long inputs — it offers a 262,144 token context window
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.7 outperforms in 4 benchmarks (AIME 2025, GPQA, LiveCodeBench v6, MMLU-Pro), while Qwen3 VL 4B Thinking is better at 0 benchmarks.
GLM-4.7 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.7 ($0.60/1M tokens) is 6.0x more expensive than Qwen3 VL 4B Thinking ($0.10/1M tokens).
For output processing, GLM-4.7 ($2.20/1M tokens) is 2.2x more expensive than Qwen3 VL 4B Thinking ($1.00/1M tokens).
In conclusion, GLM-4.7 is more expensive than Qwen3 VL 4B Thinking.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.7 has 354.0B more parameters than Qwen3 VL 4B Thinking, making it 8850.0% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 4B Thinking accepts 262,144 input tokens compared to GLM-4.7's 202,800 tokens. Qwen3 VL 4B Thinking can generate longer responses up to 262,144 tokens, while GLM-4.7 is limited to 131,072 tokens.
Input Capabilities
Supported data types and modalities
Both GLM-4.7 and Qwen3 VL 4B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-4.7
Qwen3 VL 4B Thinking
License
Usage and distribution terms
GLM-4.7 is licensed under MIT, while Qwen3 VL 4B Thinking uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
GLM-4.7 was released on 2025-12-22, while Qwen3 VL 4B Thinking was released on 2025-09-22.
GLM-4.7 is 3 months newer than Qwen3 VL 4B Thinking.
Dec 22, 2025
6 months ago
3mo newerSep 22, 2025
9 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-4.7 is available from Fireworks, Novita. Qwen3 VL 4B Thinking is available from DeepInfra.
GLM-4.7
Qwen3 VL 4B Thinking
Outputs Comparison
Key Takeaways
GLM-4.7
View detailsZhipu AI
Qwen3 VL 4B Thinking
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against GLM-4.7 and Qwen3 VL 4B Thinking side-by-side, then vote on the output you prefer.
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FAQ
Common questions about GLM-4.7 vs Qwen3 VL 4B Thinking.