DeepSeek-R1-0528 vs GLM-4.5
DeepSeek-R1-0528 and GLM-4.5 are closely matched at 24.4 and 28.0 on the LLM Stats Score. GLM-4.5 is 1.3x cheaper per token.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
DeepSeek-R1-0528 and GLM-4.5 are closely matched on the overall LLM Stats Score at 24.4 and 28.0.
In the 8 individual benchmarks reported for both models, DeepSeek-R1-0528 wins 5; this is a narrower head-to-head signal than the composite indexes.
On price, GLM-4.5 is roughly 1.3x 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 DeepSeek-R1-0528
- you value its reported benchmark strengths — it wins 5 of 8 exact shared results
Choose GLM-4.5
- your work emphasizes coding and agents — it leads those capability indexes
- cost matters — it's about 1.3x cheaper per token
- you want the most recent training data — it shipped Jul 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
16 reported for DeepSeek-R1-0528 · 14 for GLM-4.5
DeepSeek-R1-0528 outperforms in 5 benchmarks (AIME 2024, GPQA, Humanity's Last Exam, LiveCodeBench, MMLU-Pro), while GLM-4.5 is better at 3 benchmarks (BrowseComp, SWE-Bench Verified, Terminal-Bench).
DeepSeek-R1-0528 shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-R1-0528 ($0.50/1M tokens) is 1.3x more expensive than GLM-4.5 ($0.40/1M tokens).
For output processing, DeepSeek-R1-0528 ($2.15/1M tokens) is 1.3x more expensive than GLM-4.5 ($1.60/1M tokens).
In conclusion, DeepSeek-R1-0528 is more expensive than GLM-4.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1-0528 has 316.0B more parameters than GLM-4.5, making it 89.0% 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
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-R1-0528 was released on 2025-05-28, while GLM-4.5 was released on 2025-07-28.
GLM-4.5 is 2 months newer than DeepSeek-R1-0528.
May 28, 2025
1.3 years ago
Jul 28, 2025
1.1 years ago
2mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-R1-0528 is available from DeepInfra, DeepSeek, Novita. GLM-4.5 is available from DeepInfra, Fireworks, Novita.
DeepSeek-R1-0528
GLM-4.5
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1-0528 and GLM-4.5 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1-0528 vs GLM-4.5.