Model Comparison
GLM-4.5V vs Llama 3.2 11B InstructWhich is better in 2026?
Comparing GLM-4.5V and Llama 3.2 11B Instruct across benchmarks, pricing, and capabilities.
Verdict: GLM-4.5V vs Llama 3.2 11B Instruct — which is better?
GLM-4.5V (by Zhipu AI) and Llama 3.2 11B Instruct (by Meta) 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.
On price, Llama 3.2 11B Instruct is roughly 19.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-4.5V also accepts a larger context window (131,072 input tokens), making it the stronger choice for long documents and large codebases.
Choose GLM-4.5V if…
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped Aug 2025
Choose Llama 3.2 11B Instruct if…
- cost matters — it's about 19.2x cheaper per token
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.5V and Llama 3.2 11B Instructdon'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
For input processing, GLM-4.5V ($0.55/1M tokens) is 11.0x more expensive than Llama 3.2 11B Instruct ($0.05/1M tokens).
For output processing, GLM-4.5V ($2.19/1M tokens) is 43.8x more expensive than Llama 3.2 11B Instruct ($0.05/1M tokens).
In conclusion, GLM-4.5V is more expensive than Llama 3.2 11B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.5V has 97.4B more parameters than Llama 3.2 11B Instruct, making it 918.9% larger.
Context Window
Maximum input and output token capacity
GLM-4.5V accepts 131,072 input tokens compared to Llama 3.2 11B Instruct's 128,000 tokens. GLM-4.5V can generate longer responses up to 131,072 tokens, while Llama 3.2 11B Instruct is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
Both GLM-4.5V and Llama 3.2 11B Instruct support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-4.5V
Llama 3.2 11B Instruct
License
Usage and distribution terms
GLM-4.5V is licensed under MIT, while Llama 3.2 11B Instruct uses Llama 3.2 Community License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Llama 3.2 Community License
Open weights
Release Timeline
When each model was launched
GLM-4.5V was released on 2025-08-11, while Llama 3.2 11B Instruct was released on 2024-09-25.
GLM-4.5V is 11 months newer than Llama 3.2 11B Instruct.
Aug 11, 2025
10 months ago
10mo newerSep 25, 2024
1.7 years ago
Knowledge Cutoff
When training data ends
Llama 3.2 11B Instruct has a documented knowledge cutoff of 2023-12-31, while GLM-4.5V's cutoff date is not specified.
We can confirm Llama 3.2 11B Instruct's training data extends to 2023-12-31, but cannot make a direct comparison without GLM-4.5V's cutoff date.
—
Dec 2023
Provider Availability
GLM-4.5V is available from Fireworks, Novita. Llama 3.2 11B Instruct is available from DeepInfra, Sambanova, Bedrock, Groq, Together, Fireworks.
GLM-4.5V
Llama 3.2 11B Instruct
Outputs Comparison
Key Takeaways
GLM-4.5V
View detailsZhipu AI
Detailed Comparison
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FAQ
Common questions about GLM-4.5V vs Llama 3.2 11B Instruct.