GLM-4.5V vs Qwen3-Coder 480B A35B Instruct
Comparing GLM-4.5V and Qwen3-Coder 480B A35B Instruct across benchmarks, pricing, and capabilities.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GLM-4.5V and Qwen3-Coder 480B A35B Instruct trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Qwen3-Coder 480B A35B Instruct is roughly 2.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3-Coder 480B A35B 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-4.5V
- you want the most recent training data — it shipped Aug 2025
Choose Qwen3-Coder 480B A35B Instruct
- cost matters — it's about 2.0x cheaper per token
- you process long inputs — it offers a 262,144 token context window
At a glance
The differences that matter most.
Individual benchmarks
0 reported for GLM-4.5V · 10 for Qwen3-Coder 480B A35B Instruct
GLM-4.5V and Qwen3-Coder 480B A35B Instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.5V ($0.55/1M tokens) is 1.8x more expensive than Qwen3-Coder 480B A35B Instruct ($0.30/1M tokens).
For output processing, GLM-4.5V ($2.19/1M tokens) is 2.2x more expensive than Qwen3-Coder 480B A35B Instruct ($1.00/1M tokens).
In conclusion, GLM-4.5V is more expensive than Qwen3-Coder 480B A35B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3-Coder 480B A35B Instruct has 372.0B more parameters than GLM-4.5V, making it 344.4% larger.
Context Window
Maximum input and output token capacity
Qwen3-Coder 480B A35B Instruct accepts 262,144 input tokens compared to GLM-4.5V's 131,072 tokens. Qwen3-Coder 480B A35B Instruct can generate longer responses up to 262,144 tokens, while GLM-4.5V is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
GLM-4.5V supports multimodal inputs, whereas Qwen3-Coder 480B A35B Instruct does not.
GLM-4.5V can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.5V
Qwen3-Coder 480B A35B Instruct
License
Usage and distribution terms
GLM-4.5V is licensed under MIT, while Qwen3-Coder 480B A35B 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-4.5V was released on 2025-08-11, while Qwen3-Coder 480B A35B Instruct was released on 2025-01-31.
GLM-4.5V is 6 months newer than Qwen3-Coder 480B A35B Instruct.
Aug 11, 2025
1.2 years ago
6mo newerJan 31, 2025
1.7 years 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-4.5V is available from Fireworks, Novita. Qwen3-Coder 480B A35B Instruct is available from DeepInfra.
GLM-4.5V
Qwen3-Coder 480B A35B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.5V and Qwen3-Coder 480B A35B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.5V vs Qwen3-Coder 480B A35B Instruct.
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