GLM-4.5 vs Qwen3 VL 4B Instruct
GLM-4.5 leads the LLM Stats Score 27.7 to 10.7. Qwen3 VL 4B Instruct is 3.1x cheaper per token.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GLM-4.5 leads the overall LLM Stats Score 27.7 to 10.7, ranking #140 overall.
In the 2 individual benchmarks reported for both models, GLM-4.5 wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3 VL 4B Instruct 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 Instruct also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GLM-4.5
- overall performance matters — it scores 27.7 and ranks #140 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
Choose Qwen3 VL 4B Instruct
- cost matters — it's about 3.1x cheaper per token
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Sep 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
14 reported for GLM-4.5 · 45 for Qwen3 VL 4B Instruct
GLM-4.5 outperforms in 2 benchmarks (BFCL-v3, MMLU-Pro), while Qwen3 VL 4B Instruct is better at 0 benchmarks.
GLM-4.5 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.5 ($0.40/1M tokens) is 4.0x more expensive than Qwen3 VL 4B Instruct ($0.10/1M tokens).
For output processing, GLM-4.5 ($1.60/1M tokens) is 2.7x more expensive than Qwen3 VL 4B Instruct ($0.60/1M tokens).
In conclusion, GLM-4.5 is more expensive than Qwen3 VL 4B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.5 has 351.0B more parameters than Qwen3 VL 4B Instruct, making it 8775.0% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 4B Instruct accepts 262,144 input tokens compared to GLM-4.5's 131,072 tokens. Qwen3 VL 4B Instruct can generate longer responses up to 262,144 tokens, while GLM-4.5 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3 VL 4B Instruct supports multimodal inputs, whereas GLM-4.5 does not.
Qwen3 VL 4B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.5
Qwen3 VL 4B Instruct
License
Usage and distribution terms
GLM-4.5 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.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
GLM-4.5 was released on 2025-07-28, while Qwen3 VL 4B Instruct was released on 2025-09-22.
Qwen3 VL 4B Instruct is 2 months newer than GLM-4.5.
Jul 28, 2025
1.2 years ago
Sep 22, 2025
12 months ago
1mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-4.5 is available from DeepInfra, Fireworks, Novita. Qwen3 VL 4B Instruct is available from DeepInfra.
GLM-4.5
Qwen3 VL 4B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.5 and Qwen3 VL 4B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.5 vs Qwen3 VL 4B Instruct.