DeepSeek-V3.2-Exp vs GLM-4.7
GLM-4.7 leads the LLM Stats Score 34.6 to 28.5. DeepSeek-V3.2-Exp is 3.3x cheaper per token.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
GLM-4.7 leads the overall LLM Stats Score 34.6 to 28.5, ranking #85 overall.
In the 9 individual benchmarks reported for both models, GLM-4.7 wins 7; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V3.2-Exp is roughly 3.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-4.7 also accepts a larger context window (202,800 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V3.2-Exp
- cost matters — it's about 3.3x cheaper per token
Choose GLM-4.7
- overall performance matters — it scores 34.6 and ranks #85 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 7 of 9 exact shared results
- you process long inputs — it offers a 202,800 token context window
- you want the most recent training data — it shipped Dec 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
14 reported for DeepSeek-V3.2-Exp · 13 for GLM-4.7
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.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
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
Model Size
Parameter count comparison
DeepSeek-V3.2-Exp has 327.0B more parameters than GLM-4.7, making it 91.3% larger.
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.
Input capabilities
Documented input modalities across available providers
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
GLM-4.7
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
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.
Sep 29, 2025
11 months ago
Dec 22, 2025
8 months ago
2mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V3.2-Exp is available from Novita. GLM-4.7 is available from Fireworks, Novita.
DeepSeek-V3.2-Exp
GLM-4.7
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.2-Exp and GLM-4.7 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2-Exp vs GLM-4.7.