GLM-5.3-Flash vs Qwen3-235B-A22B-Thinking-2507
GLM-5.3-Flash significantly outperforms across most benchmarks. GLM-5.3-Flash is 4.1x cheaper per token.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GLM-5.3-Flash outperforms in 1 benchmarks (Humanity's Last Exam), while Qwen3-235B-A22B-Thinking-2507 is better at 0 benchmarks. GLM-5.3-Flash significantly outperforms across most benchmarks.
On price, GLM-5.3-Flash is roughly 4.1x 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 benchmark, pricing, and model metadata for 2026.
Choose GLM-5.3-Flash
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- cost matters — it's about 4.1x 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
Choose Qwen3-235B-A22B-Thinking-2507
- you want predictable pricing at $0.30/M input and $3.00/M output
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5.3-Flash outperforms in 1 benchmarks (Humanity's Last Exam), while Qwen3-235B-A22B-Thinking-2507 is better at 0 benchmarks.
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, GLM-5.3-Flash ($0.15/1M tokens) is 2.0x cheaper than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 6.0x cheaper than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).
In conclusion, Qwen3-235B-A22B-Thinking-2507 is more expensive than GLM-5.3-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.3-Flash has 85.0B more parameters than Qwen3-235B-A22B-Thinking-2507, making it 36.2% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to Qwen3-235B-A22B-Thinking-2507's 262,144 tokens. Both models can generate responses up to 131,072 tokens.
Input Capabilities
Supported data types and modalities
GLM-5.3-Flash supports multimodal inputs, whereas Qwen3-235B-A22B-Thinking-2507 does not.
GLM-5.3-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.3-Flash
Qwen3-235B-A22B-Thinking-2507
License
Usage and distribution terms
GLM-5.3-Flash is licensed under MIT, while Qwen3-235B-A22B-Thinking-2507 uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
GLM-5.3-Flash was released on 2026-08-26, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.
GLM-5.3-Flash is 13 months newer than Qwen3-235B-A22B-Thinking-2507.
Aug 26, 2026
0 days ago
1.1yr newerJul 25, 2025
1.1 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-5.3-Flash is available from DeepInfra, Novita, ZAI. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.
GLM-5.3-Flash
Qwen3-235B-A22B-Thinking-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Qwen3-235B-A22B-Thinking-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Qwen3-235B-A22B-Thinking-2507.