GLM-4 Plus vs DeepSeek V3: Benchmark Comparison
Detailed comparison of GLM-4 Plus and DeepSeek V3 covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | GLM-4 Plus | DeepSeek V3 |
|---|---|---|
| Vendor | other | deepseek |
| Version | glm-4-plus | v3 |
| Release Date | 2024-08-12 | 2024-12-26 |
| Context Window | 131072 tokens | 64000 tokens |
| Input Modalities | text, image | text |
| Output Modalities | text | text |
| License | Proprietary | DeepSeek License |
| SOC2 | ✗ | ✗ |
| HIPAA | ✗ | ✗ |
| GDPR | ✗ | ✗ |
| ISO 27001 | ✗ | ✗ |
Benchmark Results
| Benchmark | GLM-4 Plus | DeepSeek V3 | Winner |
|---|---|---|---|
| ARC | 94.5 | — | GLM-4 Plus |
| BBH | 83.6 | 84.9 | DeepSeek V3 |
| GPQA | 35.4 | — | GLM-4 Plus |
| GSM8K | 81.8 | 89.3 | DeepSeek V3 |
| HUMANEVAL | 72.7 | 82.6 | DeepSeek V3 |
| IFEVAL | 79.2 | — | GLM-4 Plus |
| MATH | 53.3 | 61.6 | DeepSeek V3 |
| MMLU | 84.3 | 88.5 | DeepSeek V3 |
| MUSR | 51.3 | — | GLM-4 Plus |
| WINOGRANDE | 85.8 | — | GLM-4 Plus |
Pricing Comparison
| Tier (per Mtok) | GLM-4 Plus | DeepSeek V3 |
|---|---|---|
| Input | $0.5 | $0.27 |
| Output | $0.5 | $1.1 |
| Cache Read | $0 | $0.07 |
| Cache Write | $0 | $0.27 |
GLM-4 Plus vs DeepSeek V3
Ikhtisar Model
GLM-4 Plus and DeepSeek V3 are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
Spesifikasi Utama
| Vendor | Tanggal Rilis | Jendela Konteks | Lisensi |
|---|---|---|---|
| Other / Deepseek | 2024-08-12 / 2024-12-26 | 131K / 64K | Proprietary / DeepSeek License |
Kinerja Benchmark
| Benchmark | GLM-4 Plus | DeepSeek V3 | Pemenang |
|---|---|---|---|
| ARC | 94.5 | — | A |
| BBH (BIG-Bench Hard) | 83.6 | 84.9 | B |
| GPQA | 35.4 | — | A |
| GSM8K (Grade School Math 8K) | 81.8 | 89.3 | B |
| HumanEval | 72.7 | 82.6 | B |
| IFEval | 79.2 | — | A |
| MATH | 53.3 | 61.6 | B |
| MMLU (Massive Multitask Language Understanding) | 84.3 | 88.5 | B |
| MUSR | 51.3 | — | A |
| WinoGrande | 85.8 | — | A |
Perbandingan Harga
| Input | Output | Baca Cache | Tulis Cache |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
per juta token — A / B
Kelebihan & Kekurangan
GLM-4 Plus
- ✅ MMLU score 84.3, strong knowledge reasoning.
- ✅ 支持文本、图像、音频多模态输入。
- ⚠️ 闭源专有模型，不支持自托管。
DeepSeek V3
- ✅ MMLU score 88.5, strong knowledge reasoning.
- ✅ HumanEval 82.6, excellent code generation.
- ✅ GSM8K 89.3, robust math reasoning.
- ✅ 采用 MoE 混合专家架构。
- ⚠️ 闭源专有模型，不支持自托管。
Pendapat Editor
GLM-4 Plus and DeepSeek V3 each have their strengths. Choose based on workload (code, long context, vision), referencing the tables above.
FAQ
Which model is better for coding tasks?
Refer to the HumanEval benchmark table; the model with a higher score is better suited for coding tasks.
Which model is cheaper?
Refer to the pricing comparison table above; the model with lower input/output prices is more cost-effective.
Which has a longer context window?
Refer to the key specifications table; the model with a larger context window is better for long documents.
Referensi
Editor's Take
See Editor's Take section.