GLM-5.3-Flash vs Qwen3 VL 235B A22B Instruct
GLM-5.3-Flash significantly outperforms across most benchmarks. GLM-5.3-Flash is 2.5x cheaper per token.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GLM-5.3-Flash outperforms in 1 benchmarks (CharXiv-R), while Qwen3 VL 235B A22B Instruct is better at 0 benchmarks. GLM-5.3-Flash significantly outperforms across most benchmarks.
On price, GLM-5.3-Flash is roughly 2.5x 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 2.5x 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 VL 235B A22B Instruct
- you want predictable pricing at $0.30/M input and $1.49/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 (CharXiv-R), while Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct ($0.30/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 3.0x cheaper than Qwen3 VL 235B A22B Instruct ($1.49/1M tokens).
In conclusion, Qwen3 VL 235B A22B Instruct 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 84.0B more parameters than Qwen3 VL 235B A22B Instruct, making it 35.6% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to Qwen3 VL 235B A22B Instruct's 262,144 tokens. Qwen3 VL 235B A22B Instruct can generate longer responses up to 262,144 tokens, while GLM-5.3-Flash is limited to 131,072 tokens.
Input Capabilities
Supported data types and modalities
Both GLM-5.3-Flash and Qwen3 VL 235B A22B Instruct support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-5.3-Flash
Qwen3 VL 235B A22B Instruct
License
Usage and distribution terms
GLM-5.3-Flash is licensed under MIT, while Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct was released on 2025-09-22.
GLM-5.3-Flash is 11 months newer than Qwen3 VL 235B A22B Instruct.
Aug 26, 2026
0 days ago
11mo 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-5.3-Flash is available from DeepInfra, Novita, ZAI. Qwen3 VL 235B A22B Instruct is available from DeepInfra, Novita.
GLM-5.3-Flash
Qwen3 VL 235B A22B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Qwen3 VL 235B A22B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Qwen3 VL 235B A22B Instruct.