GLM-4.6 vs Qwen3 30B A3B
GLM-4.6 leads the LLM Stats Score 29.4 to 17.6. Qwen3 30B A3B is 6.1x cheaper per token.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GLM-4.6 leads the overall LLM Stats Score 29.4 to 17.6, ranking #121 overall.
In the 2 individual benchmarks reported for both models, GLM-4.6 wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3 30B A3B is roughly 6.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-4.6 also accepts a larger context window (131,072 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GLM-4.6
- overall performance matters — it scores 29.4 and ranks #121 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped Sep 2025
Choose Qwen3 30B A3B
- cost matters — it's about 6.1x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
7 reported for GLM-4.6 · 8 for Qwen3 30B A3B
GLM-4.6 outperforms in 2 benchmarks (AIME 2025, GPQA), while Qwen3 30B A3B is better at 0 benchmarks.
GLM-4.6 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.6 ($0.55/1M tokens) is 5.5x more expensive than Qwen3 30B A3B ($0.10/1M tokens).
For output processing, GLM-4.6 ($2.00/1M tokens) is 6.7x more expensive than Qwen3 30B A3B ($0.30/1M tokens).
In conclusion, GLM-4.6 is more expensive than Qwen3 30B A3B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.6 has 326.5B more parameters than Qwen3 30B A3B, making it 1070.5% larger.
Context Window
Maximum input and output token capacity
GLM-4.6 accepts 131,072 input tokens compared to Qwen3 30B A3B's 128,000 tokens. GLM-4.6 can generate longer responses up to 131,072 tokens, while Qwen3 30B A3B is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
GLM-4.6 supports multimodal inputs, whereas Qwen3 30B A3B does not.
GLM-4.6 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.6
Qwen3 30B A3B
License
Usage and distribution terms
GLM-4.6 is licensed under MIT, while Qwen3 30B A3B 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 30B A3B was released on 2025-04-29.
GLM-4.6 is 5 months newer than Qwen3 30B A3B.
Sep 30, 2025
11 months ago
5mo newerApr 29, 2025
1.4 years 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 30B A3B is available from DeepInfra, Novita, Fireworks.
GLM-4.6
Qwen3 30B A3B
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.6 and Qwen3 30B A3B side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.6 vs Qwen3 30B A3B.