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GLM-5 vs Qwen2.5 32B Instruct

GLM-5 leads the LLM Stats Score 37.1 to 7.8.

Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

GLM-5 leads the overall LLM Stats Score 37.1 to 7.8, ranking #65 overall.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose GLM-5

  • overall performance matters — it scores 37.1 and ranks #65 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you want the most recent training data — it shipped Feb 2026

Choose Qwen2.5 32B Instruct

  • you are already invested in the Alibaba Cloud / Qwen Team ecosystem

At a glance

The differences that matter most.

Core performance indexes
37.1
#65
7.8
#273
37.3
#65
7.3
#271
26.1
#62
9.5
#164
Cost, coverage & limits
Benchmark wins
Input price
$1.00 / M
— / M
Output price
$3.20 / M
— / M
Context window
200,000

Individual benchmarks

5 reported for GLM-5 · 18 for Qwen2.5 32B Instruct

No common benchmarks found

GLM-5 and Qwen2.5 32B Instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

711.5B diff

GLM-5 has 711.5B more parameters than Qwen2.5 32B Instruct, making it 2189.2% larger.

Zhipu AI
GLM-5
744.0Bparameters
Alibaba Cloud / Qwen Team
Qwen2.5 32B Instruct
32.5Bparameters
744.0B
GLM-5
32.5B
Qwen2.5 32B Instruct

Context Window

Maximum input and output token capacity

Only GLM-5 specifies input context (200,000 tokens). Only GLM-5 specifies output context (128,000 tokens).

Zhipu AI
GLM-5
Input200,000 tokens
Output128,000 tokens
Alibaba Cloud / Qwen Team
Qwen2.5 32B Instruct
Input- tokens
Output- tokens
Sun Sep 06 2026 • llm-stats.com

License

Usage and distribution terms

GLM-5 is licensed under MIT, while Qwen2.5 32B Instruct uses Apache 2.0.

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

GLM-5

MIT

Open weights

Qwen2.5 32B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while Qwen2.5 32B Instruct was released on 2024-09-19.

GLM-5 is 17 months newer than Qwen2.5 32B Instruct.

GLM-5

Feb 11, 2026

6 months ago

1.4yr newer
Qwen2.5 32B Instruct

Sep 19, 2024

2.0 years 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

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against GLM-5 and Qwen2.5 32B Instruct side-by-side, then vote on the output you prefer.

GLM-5
✓ Preferred
Qwen2.5 32B Instruct
Open in Playground

FAQ

Common questions about GLM-5 vs Qwen2.5 32B Instruct.

Which is better, GLM-5 or Qwen2.5 32B Instruct?

GLM-5 leads the LLM Stats Score 37.1 to 7.8. GLM-5 is made by Zhipu AI and Qwen2.5 32B Instruct is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does GLM-5 compare to Qwen2.5 32B Instruct in benchmarks?

GLM-5 scores t2-bench: 89.7%, SWE-Bench Verified: 77.8%, BrowseComp: 75.9%, MCP Atlas: 67.8%, Terminal-Bench 2.0: 56.2%. Qwen2.5 32B Instruct scores GSM8k: 95.9%, HumanEval: 88.4%, HellaSwag: 85.2%, BBH: 84.5%, MBPP: 84.0%.

What are the context window sizes for GLM-5 and Qwen2.5 32B Instruct?

GLM-5 supports 200K tokens and Qwen2.5 32B Instruct supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GLM-5 and Qwen2.5 32B Instruct?

Key differences include LLM Stats Score (37.1 vs 7.8), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5 and Qwen2.5 32B Instruct?

GLM-5 is developed by Zhipu AI and Qwen2.5 32B Instruct is developed by Alibaba Cloud / Qwen Team.