GLM-5.3 vs Qwen3-235B-A22B-Thinking-2507
GLM-5.3 significantly outperforms across most benchmarks. Qwen3-235B-A22B-Thinking-2507 is 2.2x cheaper per token.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GLM-5.3 outperforms in 1 benchmarks (Humanity's Last Exam), while Qwen3-235B-A22B-Thinking-2507 is better at 0 benchmarks. GLM-5.3 significantly outperforms across most benchmarks.
On price, Qwen3-235B-A22B-Thinking-2507 is roughly 2.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5.3 also accepts a larger context window (1,000,000 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
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Aug 2026
Choose Qwen3-235B-A22B-Thinking-2507
- cost matters — it's about 2.2x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5.3 outperforms in 1 benchmarks (Humanity's Last Exam), while Qwen3-235B-A22B-Thinking-2507 is better at 0 benchmarks.
GLM-5.3 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 ($1.40/1M tokens) is 4.7x more expensive than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).
For output processing, GLM-5.3 ($4.40/1M tokens) is 1.5x more expensive than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).
In conclusion, GLM-5.3 is more expensive than Qwen3-235B-A22B-Thinking-2507.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.3 has 518.0B more parameters than Qwen3-235B-A22B-Thinking-2507, making it 220.4% larger.
Context Window
Maximum input and output token capacity
GLM-5.3 accepts 1,000,000 input tokens compared to Qwen3-235B-A22B-Thinking-2507's 262,144 tokens. Qwen3-235B-A22B-Thinking-2507 can generate longer responses up to 131,072 tokens, while GLM-5.3 is limited to 128,000 tokens.
Release Timeline
When each model was launched
GLM-5.3 was released on 2026-08-14, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.
GLM-5.3 is 13 months newer than Qwen3-235B-A22B-Thinking-2507.
Aug 14, 2026
1 weeks 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 is available from ZAI. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.
GLM-5.3
Qwen3-235B-A22B-Thinking-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3 and Qwen3-235B-A22B-Thinking-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3 vs Qwen3-235B-A22B-Thinking-2507.