GLM-5.3 vs DeepSeek V4 Pro: AI Model Comparison
Compare GLM-5.3 and DeepSeek V4 Pro on agentic tool calling, 199 tok/s coding speed, token pricing ($1.73/M vs $0.48/M), and open weights.
Quick Verdict
Choose GLM-5.3 for autonomous agent workflows, complex multi-step browser/OS tool automation, and bilingual enterprise applications. Choose DeepSeek V4 Pro for high-speed code generation, automated test writing, and maximum token budget efficiency.
In the open-weights and enterprise self-hosted AI arena, Zhipu AI's GLM-5.3 and DeepSeek's V4 Pro represent two of the most capable models ever released. GLM-5.3 leads in autonomous agent execution loops (41.9 score) and bilingual Chinese-English structured tool calling, while DeepSeek V4 Pro delivers 199 tokens/sec output speed and unbeatable token economics ($0.48/M blended tokens).
Models at a Glance
GLM-5.3
by Zhipu AI
Pay-as-you-go
Zhipu AI Platform
DeepSeek-V4 Pro
by DeepSeek
Pay-as-you-go
DeepSeek Platform
Capabilities Comparison
| Capability | GLM-5.3 | DeepSeek-V4 Pro |
|---|---|---|
| Text Generation | ||
| Code Generation | ||
| Image Generation | ||
| Vision / Image Understanding | ||
| Video Generation | ||
| Audio / Voice Generation | ||
| Web Browsing / Search | ||
| Code Execution | ||
| Function Calling | ||
| Structured Output (JSON) | ||
| Advanced Reasoning (CoT) | ||
| File Upload & Analysis | ||
| Fine-Tuning | ||
| Plugins / Extensions | ||
| Memory / History | ||
| Agentic Capabilities | ||
| Custom Bots |
Use Case Ratings
GLM-5.3
DeepSeek-V4 Pro
Benchmark Scores
| Benchmark | GLM-5.3 | DeepSeek-V4 Pro |
|---|---|---|
| MMLU (Knowledge) | 89.8% | 89.4% |
| MMLU-Pro | 81.2% | 81.0% |
| HumanEval (Coding) | 91.8% | 91.2% |
| GPQA (Graduate Q&A) | 71.2% | 68.5% |
| MATH (Competition) | 90.1% | 89.8% |
| GSM8K (Grade Math) | 96.2% | 96.5% |
| ARC (Reasoning) | 96.5% | 96.8% |
| HellaSwag | 95.5% | 95.2% |
| MT-Bench | 9.28 | 9.30 |
| LMSYS Arena ELO | 1840 | 1980 |
| SWE-Bench | 45.0% | 44.3% |
| AIME (Advanced Math) | 77.8% | 78.2% |
Feature-by-Feature Comparison
| Feature | GLM-5.3 | DeepSeek-V4 Pro |
|---|---|---|
| Autonomous Agent Loop Score | 41.9 (Top Open Model) | 38.4 |
| Inference Output Speed | 120 tok/s | 199 tok/s (1.65x faster) |
| Blended Price / 1M Tokens | $1.73 | $0.48 (3.6x cheaper) |
| LMSYS Arena ELO Rating | 1,840 | 1,980 (Higher) |
Pricing Comparison
| Plan | GLM-5.3 | DeepSeek-V4 Pro |
|---|---|---|
| Free Version | ||
| Subscription | Pay-as-you-go | Pay-as-you-go |
| API Input (1M tokens) | $0.60 | $0.14 |
| API Output (1M tokens) | $2.40 | $0.55 |
Pros & Cons
GLM-5.3
✅ Pros
- Industry-leading autonomous agent loop execution (41.9 score)
- Higher SWE-Bench software engineering accuracy (45.0% vs 44.3%)
- Exceptional bilingual Chinese-English tool calling and parsing
- Open weights available for on-premise deployment
❌ Cons
- 3.6x higher API token pricing than DeepSeek ($1.73/M vs $0.48/M)
- Slower output generation throughput (120 tok/s vs 199 tok/s)
DeepSeek-V4 Pro
✅ Pros
- 1.65x faster token generation throughput (199 tok/s vs 120 tok/s)
- 3.6x cheaper blended API token pricing ($0.48/M vs $1.73/M)
- Higher LMSYS Arena community rating (1,980 vs 1,840)
- Higher AIME competition math score (78.2% vs 77.8%)
❌ Cons
- Slightly lower agent loop orchestration score (38.4 vs 41.9)
- Slightly lower SWE-Bench accuracy (44.3% vs 45.0%)
🏆 Who Wins in Each Category?
Cost & Throughput
DeepSeek is 3.6x cheaper and streams at 199 tokens/sec.
Agentic Loop Orchestration
GLM-5.3 achieves a 41.9 score on autonomous agent workflows.
Software Engineering
GLM-5.3 scores 45.0% on SWE-Bench vs 44.3% for DeepSeek.
Our Pick: DeepSeek-V4 Pro
DeepSeek V4 Pro wins for generation velocity (199 tok/s), $0.48/M token economy, and community preference; GLM-5.3 wins for complex autonomous agent workflows and tool calling.
Try DeepSeek-V4 ProFrequently Asked Questions
Which model is better for building autonomous web browsing agents?▼
GLM-5.3 is optimized for computer use and multi-step browser DOM interaction, making it highly effective for autonomous web scraping and RPA automation.
Can I serve both models using vLLM on the same GPU cluster?▼
Yes. Both GLM-5.3 and DeepSeek V4 Pro publish standard Hugging Face weights compatible with vLLM, SGLang, and TensorRT-LLM.
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