GLM-4.6 vs Qwen3 VL 235B A22B Thinking
GLM-4.6 significantly outperforms across most benchmarks. GLM-4.6 is 1.3x cheaper per token.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GLM-4.6 outperforms in 3 benchmarks (AIME 2025, Humanity's Last Exam, LiveCodeBench v6), while Qwen3 VL 235B A22B Thinking is better at 0 benchmarks. GLM-4.6 significantly outperforms across most benchmarks.
On price, GLM-4.6 is roughly 1.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 235B A22B Thinking also accepts a larger context window (262,144 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-4.6
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- cost matters — it's about 1.3x cheaper per token
- you want the most recent training data — it shipped Sep 2025
Choose Qwen3 VL 235B A22B Thinking
- you process long inputs — it offers a 262,144 token context window
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.6 outperforms in 3 benchmarks (AIME 2025, Humanity's Last Exam, LiveCodeBench v6), while Qwen3 VL 235B A22B Thinking is better at 0 benchmarks.
GLM-4.6 significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.6 ($0.55/1M tokens) is 1.2x more expensive than Qwen3 VL 235B A22B Thinking ($0.45/1M tokens).
For output processing, GLM-4.6 ($2.00/1M tokens) is 1.7x cheaper than Qwen3 VL 235B A22B Thinking ($3.49/1M tokens).
In conclusion, Qwen3 VL 235B A22B Thinking is more expensive than GLM-4.6.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.6 has 121.0B more parameters than Qwen3 VL 235B A22B Thinking, making it 51.3% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 235B A22B Thinking accepts 262,144 input tokens compared to GLM-4.6's 131,072 tokens. Qwen3 VL 235B A22B Thinking can generate longer responses up to 262,144 tokens, while GLM-4.6 is limited to 131,072 tokens.
Input Capabilities
Supported data types and modalities
Both GLM-4.6 and Qwen3 VL 235B A22B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-4.6
Qwen3 VL 235B A22B Thinking
License
Usage and distribution terms
GLM-4.6 is licensed under MIT, while Qwen3 VL 235B A22B Thinking 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-4.6 was released on 2025-09-30, while Qwen3 VL 235B A22B Thinking was released on 2025-09-22.
GLM-4.6 is 0 month newer than Qwen3 VL 235B A22B Thinking.
Sep 30, 2025
11 months ago
1w newerSep 22, 2025
11 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-4.6 is available from Fireworks, DeepInfra. Qwen3 VL 235B A22B Thinking is available from DeepInfra, Novita.
GLM-4.6
Qwen3 VL 235B A22B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.6 and Qwen3 VL 235B A22B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.6 vs Qwen3 VL 235B A22B Thinking.