DeepSeek-V4-Flash-0731 vs GLM-5.3-Flash
GLM-5.3-Flash significantly outperforms across most benchmarks. DeepSeek-V4-Flash-0731 is 2.1x cheaper per token.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 outperforms in 0 benchmarks, while GLM-5.3-Flash is better at 5 benchmarks (Agents' Last Exam, AutomationBench, NL2Repo, Terminal-Bench 2.1, Toolathlon). GLM-5.3-Flash significantly outperforms across most benchmarks.
On price, DeepSeek-V4-Flash-0731 is roughly 2.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Flash-0731 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 DeepSeek-V4-Flash-0731
- cost matters — it's about 2.1x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
Choose GLM-5.3-Flash
- you want the strongest raw capability — it leads on 5 of 5 shared benchmarks
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Flash-0731 outperforms in 0 benchmarks, while GLM-5.3-Flash is better at 5 benchmarks (Agents' Last Exam, AutomationBench, NL2Repo, Terminal-Bench 2.1, Toolathlon).
GLM-5.3-Flash significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Flash-0731 ($0.09/1M tokens) is 1.7x cheaper than GLM-5.3-Flash ($0.15/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 2.8x cheaper than GLM-5.3-Flash ($0.50/1M tokens).
In conclusion, GLM-5.3-Flash is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.3-Flash has 16.0B more parameters than DeepSeek-V4-Flash-0731, making it 5.3% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to GLM-5.3-Flash's 1,000,000 tokens. DeepSeek-V4-Flash-0731 can generate longer responses up to 384,000 tokens, while GLM-5.3-Flash is limited to 131,072 tokens.
Input Capabilities
Supported data types and modalities
GLM-5.3-Flash supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 does not.
GLM-5.3-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Flash-0731
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-V4-Flash-0731 was released on 2026-07-31, while GLM-5.3-Flash was released on 2026-08-26.
GLM-5.3-Flash is 1 month newer than DeepSeek-V4-Flash-0731.
Jul 31, 2026
3 weeks ago
Aug 26, 2026
0 days ago
3w 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-V4-Flash-0731 is available from DeepInfra, Novita, Fireworks. GLM-5.3-Flash is available from ZAI.
DeepSeek-V4-Flash-0731
GLM-5.3-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and GLM-5.3-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs GLM-5.3-Flash.