GLM-5 vs DeepSeek-V3.2 (Non-thinking) Comparison

Comparing GLM-5 and DeepSeek-V3.2 (Non-thinking) across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-5 and DeepSeek-V3.2 (Non-thinking) 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

DeepSeek-V3.2 (Non-thinking) costs less

For input processing, GLM-5 ($1.00/1M tokens) is 3.6x more expensive than DeepSeek-V3.2 (Non-thinking) ($0.28/1M tokens).

For output processing, GLM-5 ($3.20/1M tokens) is 7.6x more expensive than DeepSeek-V3.2 (Non-thinking) ($0.42/1M tokens).

In conclusion, GLM-5 is more expensive than DeepSeek-V3.2 (Non-thinking).*

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

Lowest available price from all providers

Tue Mar 17 2026 • llm-stats.com

GLM-5

Input tokens$1.00

Output tokens$3.20

Best providerUnknown Organization

DeepSeek-V3.2 (Non-thinking)

Input tokens$0.28

Output tokens$0.42

Best providerDeepSeek

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Model Size

Parameter count comparison

59.0B diff

GLM-5 has 59.0B more parameters than DeepSeek-V3.2 (Non-thinking), making it 8.6% larger.

GLM-5

744.0Bparameters

DeepSeek-V3.2 (Non-thinking)

685.0Bparameters

744.0B

GLM-5

685.0B

DeepSeek-V3.2 (Non-thinking)

Context Window

Maximum input and output token capacity

GLM-5 accepts 200,000 input tokens compared to DeepSeek-V3.2 (Non-thinking)'s 131,072 tokens. GLM-5 can generate longer responses up to 128,000 tokens, while DeepSeek-V3.2 (Non-thinking) is limited to 8,192 tokens.

GLM-5

Input200,000 tokens

Output128,000 tokens

DeepSeek-V3.2 (Non-thinking)

Input131,072 tokens

Output8,192 tokens

Tue Mar 17 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

GLM-5

MIT

Open weights

DeepSeek-V3.2 (Non-thinking)

MIT

Open weights

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while DeepSeek-V3.2 (Non-thinking) was released on 2025-12-01.

GLM-5 is 2 months newer than DeepSeek-V3.2 (Non-thinking).

GLM-5

Feb 11, 2026

1 months ago

2mo newer

DeepSeek-V3.2 (Non-thinking)

Dec 1, 2025

3 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-5 is available from ZAI. DeepSeek-V3.2 (Non-thinking) is available from DeepSeek. The availability of providers can affect quality of the model and reliability.