- Peringkat
- #106
- Total token output
- 241,309
- Waktu respons (rata-rata)
- 72.24s
- Total Biaya
- $0.767
Seed-2.0-Code (low) vs DeepSeek V4 Pro (high)
Seed-2.0-Code (low) unggul dalam skor rata-rata dengan 7.7 vs 7.7. DeepSeek V4 Pro (high) memiliki biaya benchmark lebih rendah di $0.447 vs $0.767. Seed-2.0-Code (low) lebih cepat di 72.24s vs 92.50s, dengan tingkat keberhasilan 77.3% vs 63.6%.
Model yang Dibandingkan
- Peringkat
- #108
- Total token output
- 209,444
- Waktu respons (rata-rata)
- 92.50s
- Total Biaya
- $0.447
Model yang direkomendasikan
DeepSeek V4 Pro (high)
Its score stays close to the best score here (7.7 vs 7.7), while costing about 1.7x less than Seed-2.0-Code (low).
Perbandingan terperinci
| Metrik | Seed-2.0-Code Seed-2.0-Code low | DeepSeek V4 Pro DeepSeek V4 Pro high |
|---|---|---|
| Skor | 7.7 | 7.7 |
| Peringkat | #106 | #108 |
| Keandalan | 8.5 | 10.0 |
| Konsistensi | 7.8 | 7.7 |
| Percobaan | 66/66 | 66/66 |
| Tes benar | ||
| Tingkat lulus per percobaan | 77.3% | 63.6% |
| Tes tidak stabil | 6 | 6 |
| Total Run | 66 | 66 |
| Biaya per hasil | 5.899 | 2.251 |
| Total Biaya | $0.767 | $0.447 |
| Harga input | $0.500 / 1M | $0.940 / 1M |
| Harga output | $3.000 / 1M | $1.880 / 1M |
| Total token input | 85,657 | 90,757 |
| Token output | 8,142 | 22,928 |
| Token penalaran | 233,167 | 186,516 |
| Waktu respons (rata-rata) | 72.24s | 92.50s |
| Waktu respons (maks) | 485.92s | 416.76s |
| Waktu respons (total) | 1589.31s | 2035.01s |
| Parameter | ~200B total (~20B aktif) | 1.6T total (49B aktif) |
| Ketersediaan | Tertutup | Sumber terbuka |
Showcase generasi model
Hamster playing table tennis
Prompt: Create a detailed SVG illustration of a hamster playing table tennis.
#106 Seed-2.0-Code
low
Provider returned error
- Biaya
- $0.000
- Waktu
- 0.3s
- Token
- 0 tok
#108 DeepSeek V4 Pro
high- Biaya
- $0.023
- Waktu
- 257.6s
- Token
- 14,870 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 |
|---|---|---|---|---|---|---|---|---|---|
| Seed-2.0-Code | 8.7 | 7.9 | 91.7% | 1 | 39.56s | 930 | 2,650 | 15,987 | |
| DeepSeek V4 Pro | 5.7 | 5.9 | 58.3% | 2 | 25.70s | 536 | 149 | 3,214 |
| Pemrograman | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Seed-2.0-Code | 7.0 | 7.1 | 55.6% | 1 | 109.70s | 7,948 | 455 | 55,759 | |
| DeepSeek V4 Pro | 6.3 | 8.7 | 33.3% | 0 | 243.00s | 5,090 | 383 | 84,580 |
| Gabungan | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Seed-2.0-Code | 7.3 | 5.8 | 83.3% | 1 | 99.12s | 55,969 | 3,774 | 21,408 | |
| DeepSeek V4 Pro | 10.0 | 10.0 | 100.0% | 0 | 78.99s | 66,082 | 4,582 | 25,404 |
| 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 |
|---|---|---|---|---|---|---|---|---|---|
| Seed-2.0-Code | 10.0 | 10.0 | 100.0% | 0 | 24.88s | 8,046 | 246 | 1,639 | |
| DeepSeek V4 Pro | 10.0 | 10.0 | 100.0% | 0 | 25.03s | 7,690 | 274 | 2,166 |
| Spesifik domain | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Seed-2.0-Code | 4.1 | 4.4 | 44.5% | 2 | 238.45s | 975 | 16 | 132,626 | |
| DeepSeek V4 Pro | 3.6 | 7.2 | 22.2% | 1 | 249.47s | 578 | 16,870 | 58,188 |
| Kecerdasan umum | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Seed-2.0-Code | 6.5 | 3.4 | 66.7% | 1 | 11.14s | 579 | 179 | 1,117 | |
| DeepSeek V4 Pro | 10.0 | 10.0 | 100.0% | 0 | 8.83s | 471 | 115 | 1,013 |
| Kepatuhan instruksi | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Seed-2.0-Code | 9.8 | 10.0 | 100.0% | 0 | 12.20s | 828 | 72 | 1,034 | |
| DeepSeek V4 Pro | 7.8 | 6.6 | 83.3% | 1 | 8.73s | 627 | 66 | 2,726 |
| Pemecahan teka-teki | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Seed-2.0-Code | 9.9 | 10.0 | 100.0% | 0 | 12.50s | 885 | 445 | 2,470 | |
| DeepSeek V4 Pro | 6.9 | 4.9 | 77.8% | 2 | 56.85s | 591 | 178 | 2,563 |
| Pemanggilan alat | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Seed-2.0-Code | 10.0 | 10.0 | 100.0% | 0 | 55.56s | 9,315 | 296 | 637 | |
| DeepSeek V4 Pro | 9.8 | 10.0 | 100.0% | 0 | 15.92s | 8,909 | 295 | 701 |
| Pengetahuan umum | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Seed-2.0-Code | 3.0 | 10.0 | 0.0% | 0 | 10.02s | 182 | 9 | 490 | |
| DeepSeek V4 Pro | 3.0 | 10.0 | 0.0% | 0 | 34.01s | 183 | 16 | 5,961 |
Perbandingan Cepat
Ganti Pasangan Perbandingan
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