Model Comparison

GLM-5 vs Qwen2.5-Coder 32B Instruct

Comparing GLM-5 and Qwen2.5-Coder 32B Instruct across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

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

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Qwen2.5-Coder 32B Instruct costs less

For input processing, GLM-5 ($1.00/1M tokens) is 11.1x more expensive than Qwen2.5-Coder 32B Instruct ($0.09/1M tokens).

For output processing, GLM-5 ($3.20/1M tokens) is 35.6x more expensive than Qwen2.5-Coder 32B Instruct ($0.09/1M tokens).

In conclusion, GLM-5 is more expensive than Qwen2.5-Coder 32B Instruct.*

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

Lowest available price from all providers

Wed Apr 15 2026 • llm-stats.com

GLM-5

Input tokens$1.00

Output tokens$3.20

Best providerUnknown Organization

Qwen2.5-Coder 32B Instruct

Input tokens$0.09

Output tokens$0.09

Best providerLambda

Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

712.0B diff

GLM-5 has 712.0B more parameters than Qwen2.5-Coder 32B Instruct, making it 2225.0% larger.

GLM-5

744.0Bparameters

Qwen2.5-Coder 32B Instruct

32.0Bparameters

744.0B

GLM-5

32.0B

Qwen2.5-Coder 32B Instruct

Context Window

Maximum input and output token capacity

GLM-5 accepts 200,000 input tokens compared to Qwen2.5-Coder 32B Instruct's 128,000 tokens. Both models can generate responses up to 128,000 tokens.

GLM-5

Input200,000 tokens

Output128,000 tokens

Qwen2.5-Coder 32B Instruct

Input128,000 tokens

Output128,000 tokens

Wed Apr 15 2026 • llm-stats.com

License

Usage and distribution terms

GLM-5 is licensed under MIT, while Qwen2.5-Coder 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-Coder 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-Coder 32B Instruct was released on 2024-09-19.

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

GLM-5

Feb 11, 2026

2 months ago

1.4yr newer

Qwen2.5-Coder 32B Instruct

Sep 19, 2024

1.6 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

Provider Availability

GLM-5 is available from ZAI. Qwen2.5-Coder 32B Instruct is available from Lambda, DeepInfra, Hyperbolic, Fireworks.

GLM-5

Unknown Organization

Input Price:Input: $1.00/1MOutput Price:Output: $3.20/1M

Qwen2.5-Coder 32B Instruct

Lambda

Input Price:Input: $0.09/1MOutput Price:Output: $0.09/1M

Deepinfra

Input Price:Input: $0.18/1MOutput Price:Output: $0.18/1M

Hyperbolic

Input Price:Input: $0.20/1MOutput Price:Output: $0.20/1M

Fireworks

Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M

* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

GLM-5

View details

Zhipu AI

Larger context window (200,000 tokens)

Qwen2.5-Coder 32B Instruct

View details

Alibaba Cloud / Qwen Team

Less expensive input tokens

Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature	GLM-5	Qwen2.5-Coder 32B Instruct

FAQ

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

GLM-5 (Zhipu AI) and Qwen2.5-Coder 32B Instruct (Alibaba Cloud / Qwen Team) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

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-Coder 32B Instruct scores HumanEval: 92.7%, GSM8k: 91.1%, MBPP: 90.2%, HellaSwag: 83.0%, Winogrande: 80.8%.

Qwen2.5-Coder 32B Instruct is 11.1x cheaper for input tokens. GLM-5 costs $1.00/M input and $3.20/M output via z. Qwen2.5-Coder 32B Instruct costs $0.09/M input and $0.09/M output via lambda.

GLM-5 supports 200K tokens and Qwen2.5-Coder 32B Instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

Key differences include context window (200K vs 128K), input pricing ($1.00 vs $0.09/M), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

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

GLM-5 vs Qwen2.5-Coder 32B Instruct

Performance Benchmarks

Arena Performance

Pricing Analysis

Model Size

Context Window

License

Release Timeline

Knowledge Cutoff

Provider Availability

GLM-5

Qwen2.5-Coder 32B Instruct

Outputs Comparison

Key Takeaways

GLM-5

Qwen2.5-Coder 32B Instruct

Detailed Comparison

FAQ

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

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

Is GLM-5 cheaper than Qwen2.5-Coder 32B Instruct?

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

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

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