DeepSeek-V3.2-Exp vs GLM-4.7 Comparison

Comparing DeepSeek-V3.2-Exp and GLM-4.7 across benchmarks, pricing, and capabilities.

Performance Benchmarks

Comparative analysis across standard metrics

9 benchmarks

DeepSeek-V3.2-Exp outperforms in 2 benchmarks (MMLU-Pro, Terminal-Bench), while GLM-4.7 is better at 7 benchmarks (AIME 2025, BrowseComp, BrowseComp-zh, GPQA, Humanity's Last Exam, SWE-bench Multilingual, SWE-Bench Verified).

GLM-4.7 significantly outperforms across most benchmarks.

Sat Mar 14 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

DeepSeek-V3.2-Exp costs less

For input processing, DeepSeek-V3.2-Exp ($0.27/1M tokens) is 2.2x cheaper than GLM-4.7 ($0.60/1M tokens).

For output processing, DeepSeek-V3.2-Exp ($0.41/1M tokens) is 5.4x cheaper than GLM-4.7 ($2.20/1M tokens).

In conclusion, GLM-4.7 is more expensive than DeepSeek-V3.2-Exp.*

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

Lowest available price from all providers
Sat Mar 14 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2-Exp
Input tokens$0.27
Output tokens$0.41
Best providerNovita
Zhipu AI
GLM-4.7
Input tokens$0.60
Output tokens$2.20
Best providerFireworks
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Model Size

Parameter count comparison

327.0B diff

DeepSeek-V3.2-Exp has 327.0B more parameters than GLM-4.7, making it 91.3% larger.

DeepSeek
DeepSeek-V3.2-Exp
685.0Bparameters
Zhipu AI
GLM-4.7
358.0Bparameters
685.0B
DeepSeek-V3.2-Exp
358.0B
GLM-4.7

Context Window

Maximum input and output token capacity

GLM-4.7 accepts 202,800 input tokens compared to DeepSeek-V3.2-Exp's 163,840 tokens. GLM-4.7 can generate longer responses up to 131,072 tokens, while DeepSeek-V3.2-Exp is limited to 65,536 tokens.

DeepSeek
DeepSeek-V3.2-Exp
Input163,840 tokens
Output65,536 tokens
Zhipu AI
GLM-4.7
Input202,800 tokens
Output131,072 tokens
Sat Mar 14 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

GLM-4.7 supports multimodal inputs, whereas DeepSeek-V3.2-Exp does not.

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

DeepSeek-V3.2-Exp

Text
Images
Audio
Video

GLM-4.7

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under MIT.

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

DeepSeek-V3.2-Exp

MIT

Open weights

GLM-4.7

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2-Exp was released on 2025-09-29, while GLM-4.7 was released on 2025-12-22.

GLM-4.7 is 3 months newer than DeepSeek-V3.2-Exp.

DeepSeek-V3.2-Exp

Sep 29, 2025

5 months ago

GLM-4.7

Dec 22, 2025

2 months ago

2mo newer

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

DeepSeek-V3.2-Exp is available from Novita. GLM-4.7 is available from Fireworks, Novita. The availability of providers can affect quality of the model and reliability.

DeepSeek-V3.2-Exp

novita logo
Novita
Input Price:Input: $0.27/1MOutput Price:Output: $0.41/1M

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
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Less expensive input tokens
Less expensive output tokens
Higher MMLU-Pro score (85.0% vs 84.3%)
Higher Terminal-Bench score (37.7% vs 33.3%)
Larger context window (202,800 tokens)
Supports multimodal inputs
Higher AIME 2025 score (95.7% vs 89.3%)
Higher BrowseComp score (52.0% vs 40.1%)
Higher BrowseComp-zh score (66.6% vs 47.9%)
Higher GPQA score (85.7% vs 79.9%)
Higher Humanity's Last Exam score (42.8% vs 19.8%)
Higher SWE-bench Multilingual score (66.7% vs 57.9%)
Higher SWE-Bench Verified score (73.8% vs 67.8%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2-Exp
Zhipu AI
GLM-4.7