LLM Comparison
DeepSeek V3.2 vs GLM-5.3-Flash
Side-by-side specs, pricing & capabilities · Updated October 2026
Price vs Intelligence
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2/6 modelsSame tier:
| Organization | ||
| OpenTools Score | 52 152 | 72 444 |
| Family | DeepSeek | GLM |
| Status | Current | Current |
| Release Date | Dec 2025 | Aug 2026 |
| Context Window | 164K tokens | 1.3M tokens |
| Input Price | $0.26/M tokens | $0.07/M tokens |
| Output Price | $0.42/M tokens | $0.25/M tokens |
| Pricing Notes | Cache read: $0.1350/M tokens | OpenRouter lists a limited-time discounted price of $0.075/M input and $0.25/M output. Standard first-party style pricing is commonly $0.15/M input and $0.50/M output; cache-read pricing may vary by provider. |
| Capabilities | textcode | textvisionvideo-inputcodetool-usereasoninglong-contextopen-weights |
| Training Cutoff | — | Not disclosed |
| Max Output | 164K tokens | 131K tokens |
| API Identifier | deepseek/deepseek-v3.2 | z-ai/glm-5.3-flash |
| Benchmarks | ||
| MMLU-Pro | 85deepseek | — |
| GPQA Diamond | 82.4deepseek | 91.2121artificial-analysis |
| AIME 2025 | 93.1deepseek | — |
| LiveCodeBench | 83.3deepseek | — |
| SWE-bench Verified | 73.1deepseek | — |
| HLE | 25.1deepseek | — |
| BrowseComp | 51.4deepseek | — |
| Artificial Analysis Intelligence Index v4.1.1 | — | 57artificial-analysis |
| MMLU | — | 88.1official |
| Artificial Analysis Intelligence Index v4.3 | — | 41.9074artificial-analysis |
| SciCode | — | 51.6204artificial-analysis |
| Terminal-Bench 2.1 | — | 84.2697artificial-analysis |
| View DeepSeek V3.2 | View GLM-5.3-Flash | |
Cost Calculator
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| Model | Input | Output | Total / mo | vs Best |
|---|---|---|---|---|
| GLM-5.3-FlashCheapest | $0.08 | $0.13 | $0.20 | — |
| DeepSeek V3.2 | $0.26 | $0.21 | $0.47 | +134% |
DeepSeek
DeepSeek V3.2
DeepSeek V3.2 is a large language model from DeepSeek. Supports up to 163,840 token context window. Achieves 87.1% on MMLU. Available from $0.26/M input tokens.
Z.ai
GLM-5.3-Flash
GLM-5.3-Flash is Z.ai’s open-weight GLM-5 model for efficient coding, long-horizon agent tasks, and long-context multimodal reasoning. It uses a hybrid sparse and linear attention design with 320B total parameters and 18B active parameters.
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