Provider Comparison

GLM-5 vs DeepSeek-V3.2: Chinese LLM Showdown

GLM-5 vs DeepSeek-V3.2 compared in 2026: Both open-weight Chinese MoE models. Benchmarks, pricing, coding, community—which open-source LLM wins?

GLM-5

DeepSeek

Quick Verdict

For developers choosing between GLM-5 and DeepSeek-V3.2 in 2026, DeepSeek-V3.2 is the stronger default choice for most use cases: it offers the best API pricing in the frontier tier, a larger community, superior coding benchmark scores, and fully open-source code that makes self-hosting and fine-tuning more accessible. GLM-5 earns preference in three specific scenarios: enterprise deployments that require Zhipu AI's commercial support and SLA guarantees, research projects with deep Tsinghua University integration, and workflows specifically optimized for Zhipu's ecosystem of tools and APIs. Both models are genuinely frontier-tier alternatives to proprietary Western models for Chinese-first and multilingual workloads. If cost is your primary concern, DeepSeek-V3.2's API pricing is hard to beat. If you need enterprise commercial support, GLM-5 offers better backing.

Detailed Comparison

A side-by-side analysis of key factors to help you make the right choice.

Factor	GLM-5Recommended	DeepSeek	Winner
Benchmark Performance	Top-5 LMArena; strong MMLU, GSM8K, CMMLU	Top-10 LMArena; strong math, code, multilingual
Parameter Count	600B+ total (MoE), ~50B active per token	671B total (MoE), ~37B active per token
MoE Architecture	Mature MoE with 16 experts per layer	DeepSeek-MoE with optimized load balancing
API Pricing	Zhipu AI API: competitive per-token pricing	DeepSeek API: among the cheapest frontier models
Open Source	Open weights released on Hugging Face	Fully open weights + model code on GitHub
Multilingual Quality	Excellent Chinese + English; multilingual-first	Excellent Chinese + English; strong multilingual
Coding (HumanEval)	~87% HumanEval pass@1	~89% HumanEval pass@1
Community & Ecosystem	Growing Zhipu ecosystem; academic backing	Very strong: massive GitHub community, 80K+ stars
Total Score	2/ 8	4/ 8	2 ties

Benchmark Performance

GLM-5

Top-5 LMArena; strong MMLU, GSM8K, CMMLU

DeepSeek

Top-10 LMArena; strong math, code, multilingual

Parameter Count

GLM-5

600B+ total (MoE), ~50B active per token

DeepSeek

671B total (MoE), ~37B active per token

MoE Architecture

GLM-5

Mature MoE with 16 experts per layer

DeepSeek

DeepSeek-MoE with optimized load balancing

API Pricing

GLM-5

Zhipu AI API: competitive per-token pricing

DeepSeek

DeepSeek API: among the cheapest frontier models

Open Source

GLM-5

Open weights released on Hugging Face

DeepSeek

Fully open weights + model code on GitHub

Multilingual Quality

GLM-5

Excellent Chinese + English; multilingual-first

DeepSeek

Excellent Chinese + English; strong multilingual

Coding (HumanEval)

GLM-5

~87% HumanEval pass@1

DeepSeek

~89% HumanEval pass@1

Community & Ecosystem

GLM-5

Growing Zhipu ecosystem; academic backing

DeepSeek

Very strong: massive GitHub community, 80K+ stars

Key Statistics

Real data from verified industry sources to support your decision.

GLM-5 has 600B+ parameters (MoE) with ~50B active per token

Zhipu AI Technical Report

Zhipu AI Technical Report (2026)

DeepSeek-V3.2 has 671B total parameters with ~37B active, trained on 15T tokens

DeepSeek Technical Report

DeepSeek Technical Report (2026)

DeepSeek API pricing: $0.28/M input tokens — among the most cost-effective frontier models

DeepSeek Pricing

DeepSeek Pricing (2026)

DeepSeek GitHub repository has 80,000+ stars, one of the most-starred AI repos

GitHub

GitHub (2026)

Both GLM-5 and DeepSeek-V3.2 score within 2% of each other on standard MMLU benchmarks

MMLU Leaderboard

MMLU Leaderboard (2026)

All statistics are from reputable third-party sources. Links to original sources available upon request.

When to Choose Each Option

Clear guidance based on your specific situation and needs.

Choose GLM-5 when...

You need enterprise commercial support with SLA guarantees from Zhipu AI
Your project has deep integration with Tsinghua University research ecosystem
You prefer Zhipu's hosted API with commercial backing for production workloads
Your use case benefits from GLM-5's specific Chinese enterprise alignment

Choose DeepSeek when...

You want the best API price-performance ratio in the frontier tier
You need the largest open-source community with 80K+ GitHub stars and ecosystem support
Your workload is coding-heavy and you need best-in-class HumanEval performance
You want fully open-source code (not just weights) for maximum deployment flexibility

Our Recommendation

Frequently Asked Questions

Common questions about this comparison answered.

Both are Chinese open-weight MoE LLMs, but DeepSeek-V3.2 has a larger community, cheaper API pricing, and better coding benchmarks. GLM-5 has stronger enterprise commercial support through Zhipu AI and deeper academic integration with Tsinghua University.

DeepSeek-V3.2 is cheaper. DeepSeek's API pricing starts at $0.28/M input tokens, making it one of the most affordable frontier model APIs available. Zhipu AI's GLM-5 API is competitive but generally higher than DeepSeek's.

Yes. Both GLM-5 and DeepSeek-V3.2 release open weights that can be run with vLLM, Ollama, or similar inference frameworks. DeepSeek also releases full model code; GLM-5 releases model weights. Both require significant hardware (A100/H100 clusters).

Both are excellent at Chinese. GLM-5 has a slight edge in Chinese cultural context and Chinese enterprise workflows from its Tsinghua/Beijing research environment. DeepSeek-V3.2 is also trained extensively on Chinese data and performs comparably on CMMLU.

DeepSeek-V3.2 leads on coding benchmarks—approximately 89% vs 87% HumanEval pass@1. For code-intensive workloads, DeepSeek-V3.2 or its Coder variant is preferable.

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