DeepSeek-V4-Flash-0731 vs GLM-5.3-Flash
DeepSeek-V4-Flash-0731 and GLM-5.3-Flash are closely matched at 45.0 and 50.6 on the LLM Stats Score. DeepSeek-V4-Flash-0731 is 2.6x cheaper per token.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 and GLM-5.3-Flash are closely matched on the overall LLM Stats Score at 45.0 and 50.6.
In the 5 individual benchmarks reported for both models, GLM-5.3-Flash wins 5; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V4-Flash-0731 is roughly 2.6x 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-V4-Flash-0731
- cost matters — it's about 2.6x cheaper per token
Choose GLM-5.3-Flash
- you value its reported benchmark strengths — it wins 5 of 5 exact shared results
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
9 reported for DeepSeek-V4-Flash-0731 · 15 for GLM-5.3-Flash
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.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Flash-0731 ($0.06/1M tokens) is 2.5x 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
Both models have the same input context window of 1,048,576 tokens. Both models can generate responses up to 1,048,576 tokens.
Input capabilities
Documented input modalities across available providers
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
1 months ago
Aug 26, 2026
2 weeks 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 DeepInfra, FriendliAI, Novita, 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.