GLM-5.3-Flash vs Llama 3.2 90B Instruct
Comparing GLM-5.3-Flash and Llama 3.2 90B Instruct across benchmarks, pricing, and capabilities.
Zhipu AI · Meta · Updated for 2026
Which is better?
GLM-5.3-Flash and Llama 3.2 90B Instruct trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, GLM-5.3-Flash is roughly 1.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5.3-Flash also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose GLM-5.3-Flash
- cost matters — it's about 1.5x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Aug 2026
Choose Llama 3.2 90B Instruct
- you want predictable pricing at $0.35/M input and $0.40/M output
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5.3-Flash and Llama 3.2 90B Instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5.3-Flash ($0.15/1M tokens) is 2.3x cheaper than Llama 3.2 90B Instruct ($0.35/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 1.3x more expensive than Llama 3.2 90B Instruct ($0.40/1M tokens).
In conclusion, Llama 3.2 90B Instruct is more expensive than GLM-5.3-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.3-Flash has 230.0B more parameters than Llama 3.2 90B Instruct, making it 255.6% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to Llama 3.2 90B Instruct's 128,000 tokens. GLM-5.3-Flash 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-5.3-Flash and Llama 3.2 90B Instruct support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-5.3-Flash
Llama 3.2 90B Instruct
License
Usage and distribution terms
GLM-5.3-Flash 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-5.3-Flash was released on 2026-08-26, while Llama 3.2 90B Instruct was released on 2024-09-25.
GLM-5.3-Flash is 23 months newer than Llama 3.2 90B Instruct.
Aug 26, 2026
1 days ago
1.9yr newerSep 25, 2024
1.9 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-5.3-Flash is available from DeepInfra, Novita, ZAI. Llama 3.2 90B Instruct is available from DeepInfra, Bedrock, Fireworks, Together, Hyperbolic.
GLM-5.3-Flash
Llama 3.2 90B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Llama 3.2 90B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Llama 3.2 90B Instruct.