GLM-4.7 vs Qwen3 VL 8B Instruct
GLM-4.7 significantly outperforms across most benchmarks. Qwen3 VL 8B Instruct is 5.4x cheaper per token.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GLM-4.7 outperforms in 3 benchmarks (AIME 2025, LiveCodeBench v6, MMLU-Pro), while Qwen3 VL 8B Instruct is better at 0 benchmarks. GLM-4.7 significantly outperforms across most benchmarks.
On price, Qwen3 VL 8B Instruct is roughly 5.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-4.7 also accepts a larger context window (202,800 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-4.7
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- you process long inputs — it offers a 202,800 token context window
- you want the most recent training data — it shipped Dec 2025
Choose Qwen3 VL 8B Instruct
- cost matters — it's about 5.4x cheaper per token
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.7 outperforms in 3 benchmarks (AIME 2025, LiveCodeBench v6, MMLU-Pro), while Qwen3 VL 8B Instruct is better at 0 benchmarks.
GLM-4.7 significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.7 ($0.60/1M tokens) is 7.5x more expensive than Qwen3 VL 8B Instruct ($0.08/1M tokens).
For output processing, GLM-4.7 ($2.20/1M tokens) is 4.4x more expensive than Qwen3 VL 8B Instruct ($0.50/1M tokens).
In conclusion, GLM-4.7 is more expensive than Qwen3 VL 8B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.7 has 349.0B more parameters than Qwen3 VL 8B Instruct, making it 3877.8% larger.
Context Window
Maximum input and output token capacity
GLM-4.7 accepts 202,800 input tokens compared to Qwen3 VL 8B Instruct's 131,072 tokens. GLM-4.7 can generate longer responses up to 131,072 tokens, while Qwen3 VL 8B Instruct is limited to 32,768 tokens.
Input Capabilities
Supported data types and modalities
Both GLM-4.7 and Qwen3 VL 8B Instruct support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-4.7
Qwen3 VL 8B Instruct
License
Usage and distribution terms
GLM-4.7 is licensed under MIT, while Qwen3 VL 8B 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-4.7 was released on 2025-12-22, while Qwen3 VL 8B Instruct was released on 2025-09-22.
GLM-4.7 is 3 months newer than Qwen3 VL 8B Instruct.
Dec 22, 2025
8 months ago
3mo 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-4.7 is available from Fireworks, Novita. Qwen3 VL 8B Instruct is available from Novita, DeepInfra.
GLM-4.7
Qwen3 VL 8B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.7 and Qwen3 VL 8B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.7 vs Qwen3 VL 8B Instruct.