DeepSeek-R1-0528 vs GLM-5.3-Flash
GLM-5.3-Flash leads the LLM Stats Score 51.6 to 24.6. GLM-5.3-Flash is 3.8x cheaper per token.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
GLM-5.3-Flash leads the overall LLM Stats Score 51.6 to 24.6, ranking #11 overall.
In the 1 individual benchmarks reported for both models, GLM-5.3-Flash wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, GLM-5.3-Flash is roughly 3.8x 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 LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-R1-0528
- you want predictable pricing at $0.50/M input and $2.15/M output
Choose GLM-5.3-Flash
- overall performance matters — it scores 51.6 and ranks #11 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 3.8x 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
At a glance
The differences that matter most.
Individual benchmarks
16 reported for DeepSeek-R1-0528 · 15 for GLM-5.3-Flash
DeepSeek-R1-0528 outperforms in 0 benchmarks, while GLM-5.3-Flash is better at 1 benchmark (Humanity's Last Exam).
GLM-5.3-Flash 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, DeepSeek-R1-0528 ($0.50/1M tokens) is 3.3x more expensive than GLM-5.3-Flash ($0.15/1M tokens).
For output processing, DeepSeek-R1-0528 ($2.15/1M tokens) is 4.3x more expensive than GLM-5.3-Flash ($0.50/1M tokens).
In conclusion, DeepSeek-R1-0528 is more expensive than GLM-5.3-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1-0528 has 351.0B more parameters than GLM-5.3-Flash, making it 109.7% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to DeepSeek-R1-0528's 131,072 tokens. Both models can generate responses up to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
GLM-5.3-Flash supports multimodal inputs, whereas DeepSeek-R1-0528 does not.
GLM-5.3-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1-0528
GLM-5.3-Flash
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-5.3-Flash was released on 2026-08-26.
GLM-5.3-Flash is 15 months newer than DeepSeek-R1-0528.
May 28, 2025
1.3 years ago
Aug 26, 2026
3 days ago
1.2yr 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-5.3-Flash is available from DeepInfra, Novita, ZAI.
DeepSeek-R1-0528
GLM-5.3-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1-0528 and GLM-5.3-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1-0528 vs GLM-5.3-Flash.