DeepSeek vs GLM-4.5
Detailed side-by-side comparison to help you choose the right tool
DeepSeek
🟢No CodeAI Models
Chinese AI company offering powerful models at remarkably low prices with strong coding abilities and reasoning capabilities that rival OpenAI and Anthropic.
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CustomGLM-4.5
AI Models
Zhipu AI's flagship open-source large language model designed specifically for agentic AI applications, featuring 355B total parameters with 32B active per inference and MIT licensing.
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DeepSeek - Pros & Cons
Pros
- ✓API pricing at $0.14-$0.27 per million input tokens, roughly 95% cheaper than GPT-4
- ✓DeepSeek-R1 and V3 model weights released under MIT license for free commercial self-hosting
- ✓DeepSeek-R1 matches OpenAI o1 performance on MATH-500 (97.3%) and Codeforces reasoning benchmarks
- ✓Free unlimited web chat at chat.deepseek.com with no login paywall
- ✓OpenAI SDK-compatible API enables drop-in migration from existing ChatGPT integrations
- ✓Native bilingual Chinese-English training with published technical research papers on arXiv
Cons
- ✗Data is processed on servers in mainland China, creating compliance issues for EU, US government, and regulated industries
- ✗API has experienced multi-hour outages and registration freezes during viral traffic spikes since January 2025
- ✗Weaker performance on creative writing, nuanced English prose, and multilingual tasks outside Chinese/English
- ✗Smaller third-party ecosystem and fewer integrations compared to OpenAI or Anthropic
- ✗Content moderation reflects Chinese regulatory requirements, restricting certain political and historical topics
GLM-4.5 - Pros & Cons
Pros
- ✓MIT licensing allows commercial deployment, modification, self-hosting, and derivative work without the contractual limits common in closed frontier models.
- ✓The 355B total / 32B active MoE design gives teams a frontier-scale model while activating a much smaller subset of parameters per inference.
- ✓A 128K context window and 96K maximum output make it practical for long documents, large codebases, lengthy transcripts, and multi-step agent traces.
- ✓Hybrid reasoning lets developers choose deeper Thinking Mode for complex tool use or Non-Thinking Mode for faster direct responses.
- ✓Official documentation highlights function calling, structured output, streaming, context caching, and integration with code-agent environments such as Claude Code and Roo Code.
- ✓The GLM-4.5-Air variant provides a smaller 106B total / 12B active option for teams that need a lower-cost deployment path.
Cons
- ✗It is not a turnkey voice-agent product; teams still need speech-to-text, text-to-speech, telephony, orchestration, monitoring, and safety layers for production voice workflows.
- ✗Full self-hosting is hardware intensive: official full-context GLM-4.5 configurations list up to H100 x 32 or H200 x 16 for 128K-context BF16 inference.
- ✗Hosted API pricing is token-based rather than a simple monthly SaaS plan, with Z.AI listing GLM-4.5 at $0.60 per 1M input tokens and $2.20 per 1M output tokens and GLM-4.5-Air at $0.20 per 1M input tokens and $1.10 per 1M output tokens.
- ✗Although Z.AI reports strong open-model benchmark results, closed models such as Claude and GPT may still be easier to operate and may perform better in some enterprise support workflows.
- ✗Some website setup examples reference older or adjacent GLM model names, so developers should rely on the current Z.AI docs or Hugging Face model card when deploying.
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