Model Comparison

GLM-4.5 vs Qwen3-235B-A22B-Thinking-2507

GLM-4.5 shows notably better performance in the majority of benchmarks. GLM-4.5 is 1.4x cheaper per token.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

6 benchmarks

GLM-4.5 outperforms in 4 benchmarks (BFCL-v3, MMLU-Pro, TAU-bench Airline, TAU-bench Retail), while Qwen3-235B-A22B-Thinking-2507 is better at 2 benchmarks (GPQA, Humanity's Last Exam).

GLM-4.5 shows notably better performance in the majority of benchmarks.

Fri May 15 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

GLM-4.5 costs less

For input processing, GLM-4.5 ($0.40/1M tokens) is 1.3x more expensive than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).

For output processing, GLM-4.5 ($1.60/1M tokens) is 1.9x cheaper than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).

In conclusion, Qwen3-235B-A22B-Thinking-2507 is more expensive than GLM-4.5.*

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

Lowest available price from all providers

Fri May 15 2026 • llm-stats.com

GLM-4.5

Input tokens$0.40

Output tokens$1.60

Best providerDeepinfra

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

120.0B diff

GLM-4.5 has 120.0B more parameters than Qwen3-235B-A22B-Thinking-2507, making it 51.1% larger.

GLM-4.5

355.0Bparameters

Qwen3-235B-A22B-Thinking-2507

235.0Bparameters

355.0B

GLM-4.5

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-4.5's 131,072 tokens. Both models can generate responses up to 131,072 tokens.

GLM-4.5

Input131,072 tokens

Output131,072 tokens

Qwen3-235B-A22B-Thinking-2507

Input262,144 tokens

Output131,072 tokens

Fri May 15 2026 • llm-stats.com

License

Usage and distribution terms

GLM-4.5 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-4.5

MIT

Open weights

Qwen3-235B-A22B-Thinking-2507

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-4.5 was released on 2025-07-28, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.

GLM-4.5 is 0 month newer than Qwen3-235B-A22B-Thinking-2507.

GLM-4.5

Jul 28, 2025

9 months ago

3d 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-4.5 is available from DeepInfra, Fireworks, Novita. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.

GLM-4.5

Deepinfra

Input Price:Input: $0.40/1MOutput Price:Output: $1.60/1M

Fireworks

Input Price:Input: $0.55/1MOutput Price:Output: $2.19/1M

Novita

Input Price:Input: $0.60/1MOutput Price:Output: $2.20/1M

Qwen3-235B-A22B-Thinking-2507

Fireworks

Input Price:Input: $0.30/1MOutput Price:Output: $3.00/1M

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

GLM-4.5

View details

Zhipu AI

Less expensive output tokens

Higher BFCL-v3 score (77.8% vs 71.9%)

Higher MMLU-Pro score (84.6% vs 84.4%)

Higher TAU-bench Airline score (60.4% vs 46.0%)

Higher TAU-bench Retail score (79.7% vs 67.8%)

Qwen3-235B-A22B-Thinking-2507

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Less expensive input tokens

Higher GPQA score (81.1% vs 79.1%)

Higher Humanity's Last Exam score (18.2% vs 14.4%)

Detailed Comparison

AI Model Comparison Table
Feature	GLM-4.5	Qwen3-235B-A22B-Thinking-2507

FAQ

Common questions about GLM-4.5 vs Qwen3-235B-A22B-Thinking-2507.

Which is better, GLM-4.5 or Qwen3-235B-A22B-Thinking-2507?

GLM-4.5 shows notably better performance in the majority of benchmarks. GLM-4.5 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-4.5 compare to Qwen3-235B-A22B-Thinking-2507 in benchmarks?

GLM-4.5 scores MATH-500: 98.2%, AIME 2024: 91.0%, MMLU-Pro: 84.6%, TAU-bench Retail: 79.7%, GPQA: 79.1%. 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%.

Is GLM-4.5 cheaper than Qwen3-235B-A22B-Thinking-2507?

Qwen3-235B-A22B-Thinking-2507 is 1.3x cheaper for input tokens. GLM-4.5 costs $0.40/M input and $1.60/M output via deepinfra. 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-4.5 and Qwen3-235B-A22B-Thinking-2507?

GLM-4.5 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.

What are the main differences between GLM-4.5 and Qwen3-235B-A22B-Thinking-2507?

Key differences include context window (131K vs 262K), input pricing ($0.40 vs $0.30/M), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-4.5 and Qwen3-235B-A22B-Thinking-2507?

GLM-4.5 is developed by Zhipu AI and Qwen3-235B-A22B-Thinking-2507 is developed by Alibaba Cloud / Qwen Team.