Model Comparison
GLM-5 vs Qwen3-235B-A22B-Thinking-2507Which is better in 2026?
Comparing GLM-5 and Qwen3-235B-A22B-Thinking-2507 across benchmarks, pricing, and capabilities.
Verdict: GLM-5 vs Qwen3-235B-A22B-Thinking-2507 — which is better?
GLM-5 (by Zhipu AI) and Qwen3-235B-A22B-Thinking-2507 (by Alibaba Cloud / Qwen Team) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
On price, Qwen3-235B-A22B-Thinking-2507 is roughly 1.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3-235B-A22B-Thinking-2507 also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Choose GLM-5 if…
- you want the most recent training data — it shipped Feb 2026
Choose Qwen3-235B-A22B-Thinking-2507 if…
- cost matters — it's about 1.6x cheaper per token
- you process long inputs — it offers a 262,144 token context window
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5 and Qwen3-235B-A22B-Thinking-2507 don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5 ($1.00/1M tokens) is 3.3x more expensive than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).
For output processing, GLM-5 ($3.20/1M tokens) is 1.1x more expensive than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).
In conclusion, GLM-5 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 has 509.0B more parameters than Qwen3-235B-A22B-Thinking-2507, making it 216.6% larger.
Context Window
Maximum input and output token capacity
Qwen3-235B-A22B-Thinking-2507 accepts 262,144 input tokens compared to GLM-5's 200,000 tokens. Qwen3-235B-A22B-Thinking-2507 can generate longer responses up to 131,072 tokens, while GLM-5 is limited to 128,000 tokens.
License
Usage and distribution terms
GLM-5 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 was released on 2026-02-11, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.
GLM-5 is 7 months newer than Qwen3-235B-A22B-Thinking-2507.
Feb 11, 2026
3 months ago
6mo newerJul 25, 2025
10 months 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 is available from FriendliAI, ZAI. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.
GLM-5
Qwen3-235B-A22B-Thinking-2507
Outputs Comparison
Key Takeaways
GLM-5
View detailsZhipu AI
No standout differentiators in the data we have for this pair.
Qwen3-235B-A22B-Thinking-2507
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about GLM-5 vs Qwen3-235B-A22B-Thinking-2507.