Model Comparison
GLM-5 vs Qwen3.5-122B-A10BWhich is better in 2026?
GLM-5 significantly outperforms across most benchmarks. Qwen3.5-122B-A10B is 1.4x cheaper per token.
Verdict: GLM-5 vs Qwen3.5-122B-A10B — which is better?
GLM-5 (by Zhipu AI) and Qwen3.5-122B-A10B (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.
GLM-5 outperforms in 4 benchmarks (BrowseComp, SWE-Bench Verified, t2-bench, Terminal-Bench 2.0), while Qwen3.5-122B-A10B is better at 0 benchmarks. GLM-5 significantly outperforms across most benchmarks.
On price, Qwen3.5-122B-A10B is roughly 1.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3.5-122B-A10B 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 strongest raw capability — it leads on 4 of 4 shared benchmarks
Choose Qwen3.5-122B-A10B if…
- cost matters — it's about 1.4x cheaper per token
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Feb 2026
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5 outperforms in 4 benchmarks (BrowseComp, SWE-Bench Verified, t2-bench, Terminal-Bench 2.0), while Qwen3.5-122B-A10B is better at 0 benchmarks.
GLM-5 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5 ($1.00/1M tokens) is 2.5x more expensive than Qwen3.5-122B-A10B ($0.40/1M tokens).
For output processing, GLM-5 ($3.20/1M tokens) costs the same as Qwen3.5-122B-A10B ($3.20/1M tokens).
In conclusion, GLM-5 is more expensive than Qwen3.5-122B-A10B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5 has 622.0B more parameters than Qwen3.5-122B-A10B, making it 509.8% larger.
Context Window
Maximum input and output token capacity
Qwen3.5-122B-A10B accepts 262,144 input tokens compared to GLM-5's 200,000 tokens. GLM-5 can generate longer responses up to 128,000 tokens, while Qwen3.5-122B-A10B is limited to 64,000 tokens.
Input Capabilities
Supported data types and modalities
Qwen3.5-122B-A10B supports multimodal inputs, whereas GLM-5 does not.
Qwen3.5-122B-A10B can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5
Qwen3.5-122B-A10B
License
Usage and distribution terms
GLM-5 is licensed under MIT, while Qwen3.5-122B-A10B 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 Qwen3.5-122B-A10B was released on 2026-02-24.
Qwen3.5-122B-A10B is 0 month newer than GLM-5.
Feb 11, 2026
4 months ago
Feb 24, 2026
3 months ago
1w newerKnowledge 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. Qwen3.5-122B-A10B is available from Novita.
GLM-5
Qwen3.5-122B-A10B
Outputs Comparison
Key Takeaways
GLM-5
View detailsZhipu AI
Qwen3.5-122B-A10B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about GLM-5 vs Qwen3.5-122B-A10B.