- Peringkat
- #166
- Total token output
- 35,547
- Waktu respons (rata-rata)
- 11.71s
- Total Biaya
- $0.196
DeepSeek V4 Pro vs GLM 5.3 FlashX (high)
Skor rata-rata hampir imbang di 6.8 vs 6.8. GLM 5.3 FlashX (high) memiliki biaya benchmark lebih rendah di $0.135 vs $0.196. DeepSeek V4 Pro lebih cepat di 11.71s vs 12.07s, dengan tingkat keberhasilan 47.0% vs 68.2%.
Model yang Dibandingkan
- Peringkat
- #163
- Total token output
- 83,899
- Waktu respons (rata-rata)
- 12.07s
- Total Biaya
- $0.135
Model yang direkomendasikan
GLM 5.3 FlashX (high)
It has the strongest score in this comparison (6.8) and the best overall balance of cost and response time across all 2 models.
Perbandingan terperinci
| Metrik | DeepSeek V4 Pro DeepSeek V4 Pro none | GLM 5.3 FlashX GLM 5.3 FlashX high |
|---|---|---|
| Skor | 6.8 | 6.8 |
| Peringkat | #166 | #163 |
| Keandalan | 10.0 | 10.0 |
| Konsistensi | 8.6 | 8.2 |
| Percobaan | 66/66 | 66/66 |
| Tes benar | ||
| Tingkat lulus per percobaan | 47.0% | 68.2% |
| Tes tidak stabil | 4 | 5 |
| Total Run | 66 | 66 |
| Biaya per hasil | 1.061 | 1.125 |
| Total Biaya | $0.196 | $0.135 |
| Harga input | $0.893 / 1M | $0.370 / 1M |
| Harga output | $1.785 / 1M | $1.250 / 1M |
| Total token input | 148,078 | 81,365 |
| Token output | 35,547 | 6,821 |
| Token penalaran | 0 | 77,078 |
| Waktu respons (rata-rata) | 11.71s | 12.07s |
| Waktu respons (maks) | 119.44s | 91.16s |
| Waktu respons (total) | 257.67s | 265.62s |
| Parameter | 1.6T total (49B aktif) | 320B total (18B aktif) |
| Ketersediaan | Sumber terbuka | Tertutup |
Showcase generasi model
Hamster playing table tennis
Prompt: Create a detailed SVG illustration of a hamster playing table tennis.
#166 DeepSeek V4 Pro
none
Reached the allocated time limit (300 seconds) without receiving showcase output.
- Biaya
- $0.000
- Waktu
- 300.0s
- Token
- 0 tok
#163 GLM 5.3 FlashX
high- Biaya
- $0.030
- Waktu
- 169.4s
- Token
- 23,950 tok
Model teratas berdasarkan skor
Skor vs Total Biaya
Waktu respons (rata-rata)
Skor vs Waktu respons (rata-rata)
Total token output
Skor vs Total token output
Rincian Kategori
| Trik anti-AI | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| DeepSeek V4 Pro | 3.2 | 6.1 | 16.7% | 2 | 4.02s | 540 | 1,168 | 0 | |
| GLM 5.3 FlashX | 8.2 | 7.9 | 83.3% | 1 | 1.98s | 639 | 425 | 606 |
| Pemrograman | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| DeepSeek V4 Pro | 5.6 | 10.0 | 33.3% | 0 | 13.38s | 7,275 | 5,500 | 0 | |
| GLM 5.3 FlashX | 8.2 | 7.2 | 88.9% | 1 | 8.06s | 7,317 | 374 | 9,267 |
| Gabungan | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| DeepSeek V4 Pro | 7.9 | 6.9 | 66.7% | 1 | 71.59s | 122,040 | 26,362 | 0 | |
| GLM 5.3 FlashX | 3.0 | 10.0 | 0.0% | 0 | 27.73s | 56,585 | 4,123 | 15,563 |
| Parsing dan ekstraksi data | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| DeepSeek V4 Pro | 10.0 | 10.0 | 100.0% | 0 | 4.61s | 7,568 | 200 | 0 | |
| GLM 5.3 FlashX | 7.3 | 5.8 | 83.3% | 1 | 1.82s | 7,149 | 298 | 285 |
| Spesifik domain | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| DeepSeek V4 Pro | 3.0 | 10.0 | 0.0% | 0 | 4.91s | 675 | 20 | 0 | |
| GLM 5.3 FlashX | 3.6 | 7.2 | 22.2% | 1 | 50.04s | 759 | 506 | 48,992 |
| Kecerdasan umum | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| DeepSeek V4 Pro | 5.0 | 10.0 | 0.0% | 0 | 2.05s | 471 | 126 | 0 | |
| GLM 5.3 FlashX | 6.1 | 3.1 | 66.7% | 1 | 2.28s | 498 | 169 | 229 |
| Kepatuhan instruksi | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| DeepSeek V4 Pro | 6.3 | 5.8 | 66.7% | 1 | 4.12s | 627 | 713 | 0 | |
| GLM 5.3 FlashX | 10.0 | 10.0 | 100.0% | 0 | 1.72s | 678 | 72 | 177 |
| Pemecahan teka-teki | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| DeepSeek V4 Pro | 10.0 | 10.0 | 100.0% | 0 | 3.61s | 594 | 442 | 0 | |
| GLM 5.3 FlashX | 10.0 | 10.0 | 100.0% | 0 | 2.06s | 672 | 462 | 434 |
| Pemanggilan alat | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| DeepSeek V4 Pro | 10.0 | 10.0 | 100.0% | 0 | 7.40s | 8,105 | 328 | 0 | |
| GLM 5.3 FlashX | 10.0 | 10.0 | 100.0% | 0 | 7.09s | 6,861 | 231 | 84 |
| Pengetahuan umum | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| DeepSeek V4 Pro | 3.0 | 10.0 | 0.0% | 0 | 5.76s | 183 | 688 | 0 | |
| GLM 5.3 FlashX | 3.0 | 10.0 | 0.0% | 0 | 5.34s | 207 | 161 | 1,441 |
Perbandingan Cepat
Ganti Pasangan Perbandingan
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