Model Comparison
GLM-4.6 vs Llama 3.2 90B InstructWhich is better in 2026?
GLM-4.6 significantly outperforms across most benchmarks. Llama 3.2 90B Instruct is 2.5x cheaper per token.
Verdict: GLM-4.6 vs Llama 3.2 90B Instruct — which is better?
GLM-4.6 (by Zhipu AI) and Llama 3.2 90B 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.
GLM-4.6 outperforms in 1 benchmarks (GPQA), while Llama 3.2 90B Instruct is better at 0 benchmarks. GLM-4.6 significantly outperforms across most benchmarks.
On price, Llama 3.2 90B Instruct is roughly 2.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-4.6 also accepts a larger context window (131,072 input tokens), making it the stronger choice for long documents and large codebases.
Choose GLM-4.6 if…
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped Sep 2025
Choose Llama 3.2 90B Instruct if…
- cost matters — it's about 2.5x cheaper per token
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.6 outperforms in 1 benchmarks (GPQA), while Llama 3.2 90B Instruct is better at 0 benchmarks.
GLM-4.6 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.6 ($0.55/1M tokens) is 1.6x more expensive than Llama 3.2 90B Instruct ($0.35/1M tokens).
For output processing, GLM-4.6 ($2.00/1M tokens) is 5.0x more expensive than Llama 3.2 90B Instruct ($0.40/1M tokens).
In conclusion, GLM-4.6 is more expensive than Llama 3.2 90B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.6 has 267.0B more parameters than Llama 3.2 90B Instruct, making it 296.7% larger.
Context Window
Maximum input and output token capacity
GLM-4.6 accepts 131,072 input tokens compared to Llama 3.2 90B Instruct's 128,000 tokens. GLM-4.6 can generate longer responses up to 131,072 tokens, while Llama 3.2 90B Instruct is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
Both GLM-4.6 and Llama 3.2 90B Instruct support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-4.6
Llama 3.2 90B Instruct
License
Usage and distribution terms
GLM-4.6 is licensed under MIT, while Llama 3.2 90B Instruct uses Llama 3.2.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Llama 3.2
Open weights
Release Timeline
When each model was launched
GLM-4.6 was released on 2025-09-30, while Llama 3.2 90B Instruct was released on 2024-09-25.
GLM-4.6 is 12 months newer than Llama 3.2 90B Instruct.
Sep 30, 2025
9 months ago
1.0yr newerSep 25, 2024
1.8 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-4.6 is available from Fireworks, DeepInfra. Llama 3.2 90B Instruct is available from DeepInfra, Bedrock, Fireworks, Together, Hyperbolic.
GLM-4.6
Llama 3.2 90B Instruct
Outputs Comparison
Key Takeaways
GLM-4.6
View detailsZhipu AI
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against GLM-4.6 and Llama 3.2 90B Instruct side-by-side, then vote on the output you prefer.
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FAQ
Common questions about GLM-4.6 vs Llama 3.2 90B Instruct.