GLM-4.5 vs Llama 3.1 8B Instruct
GLM-4.5 leads the LLM Stats Score 27.7 to -2.6. Llama 3.1 8B Instruct is 31.1x cheaper per token.
Zhipu AI · Meta · Updated for 2026
Which is better?
GLM-4.5 leads the overall LLM Stats Score 27.7 to -2.6, ranking #143 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, Llama 3.1 8B Instruct is roughly 31.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GLM-4.5
- overall performance matters — it scores 27.7 and ranks #143 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- you want the most recent training data — it shipped Jul 2025
Choose Llama 3.1 8B Instruct
- cost matters — it's about 31.1x 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 · 18 for Llama 3.1 8B Instruct
GLM-4.5 outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Llama 3.1 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 20.0x more expensive than Llama 3.1 8B Instruct ($0.02/1M tokens).
For output processing, GLM-4.5 ($1.60/1M tokens) is 53.3x more expensive than Llama 3.1 8B Instruct ($0.03/1M tokens).
In conclusion, GLM-4.5 is more expensive than Llama 3.1 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 Llama 3.1 8B Instruct, making it 4337.5% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 131,072 tokens. Both models can generate responses up to 131,072 tokens.
License
Usage and distribution terms
GLM-4.5 is licensed under MIT, while Llama 3.1 8B Instruct uses Llama 3.1 Community License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Llama 3.1 Community License
Open weights
Release Timeline
When each model was launched
GLM-4.5 was released on 2025-07-28, while Llama 3.1 8B Instruct was released on 2024-07-23.
GLM-4.5 is 12 months newer than Llama 3.1 8B Instruct.
Jul 28, 2025
1.2 years ago
1.0yr newerJul 23, 2024
2.2 years ago
Knowledge Cutoff
When training data ends
Llama 3.1 8B Instruct has a documented knowledge cutoff of 2023-12-31, while GLM-4.5's cutoff date is not specified.
We can confirm Llama 3.1 8B Instruct's training data extends to 2023-12-31, but cannot make a direct comparison without GLM-4.5's cutoff date.
—
Dec 2023
Provider Availability
GLM-4.5 is available from DeepInfra, Fireworks, Novita. Llama 3.1 8B Instruct is available from DeepInfra, Lambda, Groq, Sambanova, Cerebras, Hyperbolic, Together, Fireworks, Bedrock.
GLM-4.5
Llama 3.1 8B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.5 and Llama 3.1 8B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.5 vs Llama 3.1 8B Instruct.