GLM-4.5 vs Granite 3.3 8B Instruct
GLM-4.5 leads the LLM Stats Score 28.0 to 2.6. Granite 3.3 8B Instruct is 1.4x cheaper per token.
Zhipu AI · IBM · Updated for 2026
Which is better?
GLM-4.5 leads the overall LLM Stats Score 28.0 to 2.6, ranking #128 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, Granite 3.3 8B Instruct is roughly 1.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-4.5 also accepts a larger context window (131,072 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 28.0 and ranks #128 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
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped Jul 2025
Choose Granite 3.3 8B Instruct
- cost matters — it's about 1.4x cheaper per token
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 · 14 for Granite 3.3 8B Instruct
GLM-4.5 outperforms in 2 benchmarks (AIME 2024, MATH-500), while Granite 3.3 8B 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 1.3x cheaper than Granite 3.3 8B Instruct ($0.50/1M tokens).
For output processing, GLM-4.5 ($1.60/1M tokens) is 3.2x more expensive than Granite 3.3 8B Instruct ($0.50/1M tokens).
In conclusion, GLM-4.5 is more expensive than Granite 3.3 8B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.5 has 347.0B more parameters than Granite 3.3 8B Instruct, making it 4337.5% larger.
Context Window
Maximum input and output token capacity
GLM-4.5 accepts 131,072 input tokens compared to Granite 3.3 8B Instruct's 128,000 tokens. GLM-4.5 can generate longer responses up to 131,072 tokens, while Granite 3.3 8B Instruct is limited to 8,192 tokens.
Input capabilities
Documented input modalities across available providers
Granite 3.3 8B Instruct supports multimodal inputs, whereas GLM-4.5 does not.
Granite 3.3 8B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.5
Granite 3.3 8B Instruct
License
Usage and distribution terms
GLM-4.5 is licensed under MIT, while Granite 3.3 8B 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 Granite 3.3 8B Instruct was released on 2025-04-16.
GLM-4.5 is 3 months newer than Granite 3.3 8B Instruct.
Jul 28, 2025
1.1 years ago
3mo newerApr 16, 2025
1.4 years ago
Knowledge Cutoff
When training data ends
Granite 3.3 8B Instruct has a documented knowledge cutoff of 2024-04-01, while GLM-4.5's cutoff date is not specified.
We can confirm Granite 3.3 8B Instruct's training data extends to 2024-04-01, but cannot make a direct comparison without GLM-4.5's cutoff date.
—
Apr 2024
Provider Availability
GLM-4.5 is available from DeepInfra, Fireworks, Novita. Granite 3.3 8B Instruct is available from Replicate.
GLM-4.5
Granite 3.3 8B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.5 and Granite 3.3 8B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.5 vs Granite 3.3 8B Instruct.