Model Comparison

GLM-4.7 vs Qwen3-235B-A22B-Thinking-2507

GLM-4.7 significantly outperforms across most benchmarks. Qwen3-235B-A22B-Thinking-2507 is 1.0x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

GLM-4.7 outperforms in 4 benchmarks (AIME 2025, GPQA, Humanity's Last Exam, LiveCodeBench v6), while Qwen3-235B-A22B-Thinking-2507 is better at 1 benchmark (MMLU-Pro).

GLM-4.7 significantly outperforms across most benchmarks.

Sun May 03 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Qwen3-235B-A22B-Thinking-2507 costs less

For input processing, GLM-4.7 ($0.60/1M tokens) is 2.0x more expensive than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).

For output processing, GLM-4.7 ($2.20/1M tokens) is 1.4x cheaper than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).

In conclusion, GLM-4.7 is more expensive than Qwen3-235B-A22B-Thinking-2507.*

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

Lowest available price from all providers
Sun May 03 2026 • llm-stats.com
Zhipu AI
GLM-4.7
Input tokens$0.60
Output tokens$2.20
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

123.0B diff

GLM-4.7 has 123.0B more parameters than Qwen3-235B-A22B-Thinking-2507, making it 52.3% larger.

Zhipu AI
GLM-4.7
358.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507
235.0Bparameters
358.0B
GLM-4.7
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-4.7's 202,800 tokens. Both models can generate responses up to 131,072 tokens.

Zhipu AI
GLM-4.7
Input202,800 tokens
Output131,072 tokens
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507
Input262,144 tokens
Output131,072 tokens
Sun May 03 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

GLM-4.7 supports multimodal inputs, whereas Qwen3-235B-A22B-Thinking-2507 does not.

GLM-4.7 can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-4.7

Text
Images
Audio
Video

Qwen3-235B-A22B-Thinking-2507

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-4.7 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-4.7

MIT

Open weights

Qwen3-235B-A22B-Thinking-2507

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-4.7 was released on 2025-12-22, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.

GLM-4.7 is 5 months newer than Qwen3-235B-A22B-Thinking-2507.

GLM-4.7

Dec 22, 2025

4 months ago

5mo newer
Qwen3-235B-A22B-Thinking-2507

Jul 25, 2025

9 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-4.7 is available from Fireworks, Novita. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.

GLM-4.7

fireworks logo
Fireworks
Input Price:Input: $0.60/1MOutput Price:Output: $2.20/1M
novita logo
Novita
Input Price:Input: $0.60/1MOutput Price:Output: $2.20/1M

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 (95.7% vs 92.3%)
Higher GPQA score (85.7% vs 81.1%)
Higher Humanity's Last Exam score (42.8% vs 18.2%)
Higher LiveCodeBench v6 score (84.9% vs 74.1%)
Alibaba Cloud / Qwen Team
Larger context window (262,144 tokens)
Less expensive input tokens
Higher MMLU-Pro score (84.4% vs 84.3%)

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-4.7
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507

FAQ

Common questions about GLM-4.7 vs Qwen3-235B-A22B-Thinking-2507.

Which is better, GLM-4.7 or Qwen3-235B-A22B-Thinking-2507?

GLM-4.7 significantly outperforms across most benchmarks. GLM-4.7 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.

How does GLM-4.7 compare to Qwen3-235B-A22B-Thinking-2507 in benchmarks?

GLM-4.7 scores AIME 2025: 95.7%, Tau-bench: 87.4%, GPQA: 85.7%, LiveCodeBench v6: 84.9%, MMLU-Pro: 84.3%. 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%.

Is GLM-4.7 cheaper than Qwen3-235B-A22B-Thinking-2507?

Qwen3-235B-A22B-Thinking-2507 is 2.0x cheaper for input tokens. GLM-4.7 costs $0.60/M input and $2.20/M output via fireworks. Qwen3-235B-A22B-Thinking-2507 costs $0.30/M input and $3.00/M output via fireworks.

What are the context window sizes for GLM-4.7 and Qwen3-235B-A22B-Thinking-2507?

GLM-4.7 supports 203K 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.

What are the main differences between GLM-4.7 and Qwen3-235B-A22B-Thinking-2507?

Key differences include context window (203K vs 262K), input pricing ($0.60 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.

Who makes GLM-4.7 and Qwen3-235B-A22B-Thinking-2507?

GLM-4.7 is developed by Zhipu AI and Qwen3-235B-A22B-Thinking-2507 is developed by Alibaba Cloud / Qwen Team.