Model Comparison

GLM-4.6 vs Qwen3-235B-A22B-Thinking-2507

Both models are evenly matched across the benchmarks. GLM-4.6 is 1.1x cheaper per token.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

GLM-4.6 outperforms in 2 benchmarks (AIME 2025, LiveCodeBench v6), while Qwen3-235B-A22B-Thinking-2507 is better at 2 benchmarks (GPQA, Humanity's Last Exam).

Both models are evenly matched across the benchmarks.

Wed Apr 15 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

GLM-4.6 costs less

For input processing, GLM-4.6 ($0.55/1M tokens) is 1.8x more expensive than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).

For output processing, GLM-4.6 ($2.00/1M tokens) is 1.5x cheaper than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).

In conclusion, Qwen3-235B-A22B-Thinking-2507 is more expensive than GLM-4.6.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Wed Apr 15 2026 • llm-stats.com
Zhipu AI
GLM-4.6
Input tokens$0.55
Output tokens$2.00
Best providerFireworks
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507
Input tokens$0.30
Output tokens$3.00
Best providerFireworks
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

122.0B diff

GLM-4.6 has 122.0B more parameters than Qwen3-235B-A22B-Thinking-2507, making it 51.9% larger.

Zhipu AI
GLM-4.6
357.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507
235.0Bparameters
357.0B
GLM-4.6
235.0B
Qwen3-235B-A22B-Thinking-2507

Context Window

Maximum input and output token capacity

Qwen3-235B-A22B-Thinking-2507 accepts 262,144 input tokens compared to GLM-4.6's 131,072 tokens. Both models can generate responses up to 131,072 tokens.

Zhipu AI
GLM-4.6
Input131,072 tokens
Output131,072 tokens
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507
Input262,144 tokens
Output131,072 tokens
Wed Apr 15 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

GLM-4.6 supports multimodal inputs, whereas Qwen3-235B-A22B-Thinking-2507 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

Text
Images
Audio
Video

Qwen3-235B-A22B-Thinking-2507

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-4.6 is licensed under MIT, while Qwen3-235B-A22B-Thinking-2507 uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

GLM-4.6

MIT

Open weights

Qwen3-235B-A22B-Thinking-2507

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-4.6 was released on 2025-09-30, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.

GLM-4.6 is 2 months newer than Qwen3-235B-A22B-Thinking-2507.

GLM-4.6

Sep 30, 2025

6 months ago

2mo newer
Qwen3-235B-A22B-Thinking-2507

Jul 25, 2025

8 months ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

GLM-4.6 is available from Fireworks, DeepInfra. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.

GLM-4.6

fireworks logo
Fireworks
Input Price:Input: $0.55/1MOutput Price:Output: $2.19/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.60/1MOutput Price:Output: $2.00/1M

Qwen3-235B-A22B-Thinking-2507

fireworks logo
Fireworks
Input Price:Input: $0.30/1MOutput Price:Output: $3.00/1M
novita logo
Novita
Input Price:Input: $0.30/1MOutput Price:Output: $3.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Zhipu AI
Supports multimodal inputs
Less expensive output tokens
Higher AIME 2025 score (93.9% vs 92.3%)
Higher LiveCodeBench v6 score (82.8% vs 74.1%)
Alibaba Cloud / Qwen Team
Larger context window (262,144 tokens)
Less expensive input tokens
Higher GPQA score (81.1% vs 81.0%)
Higher Humanity's Last Exam score (18.2% vs 17.2%)

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-4.6
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507

FAQ

Common questions about GLM-4.6 vs Qwen3-235B-A22B-Thinking-2507

Both models are evenly matched across the benchmarks. GLM-4.6 is made by Zhipu AI and Qwen3-235B-A22B-Thinking-2507 is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
GLM-4.6 scores AIME 2025: 93.9%, LiveCodeBench v6: 82.8%, GPQA: 81.0%, SWE-Bench Verified: 68.0%, BrowseComp: 45.1%. Qwen3-235B-A22B-Thinking-2507 scores MMLU-Redux: 93.8%, AIME 2025: 92.3%, WritingBench: 88.3%, IFEval: 87.8%, Creative Writing v3: 86.1%.
Qwen3-235B-A22B-Thinking-2507 is 1.8x cheaper for input tokens. GLM-4.6 costs $0.55/M input and $2.00/M output via fireworks. Qwen3-235B-A22B-Thinking-2507 costs $0.30/M input and $3.00/M output via fireworks.
GLM-4.6 supports 131K tokens and Qwen3-235B-A22B-Thinking-2507 supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (131K vs 262K), input pricing ($0.55 vs $0.30/M), multimodal support (yes vs no), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
GLM-4.6 is developed by Zhipu AI and Qwen3-235B-A22B-Thinking-2507 is developed by Alibaba Cloud / Qwen Team.