- Peringkat
- #194
- Total token output
- 193,676
- Waktu respons (rata-rata)
- 7.62s
- Total Biaya
- $0.044
Mercury 2.5 (high) vs GLM 5.3 (low)
Skor rata-rata hampir imbang di 6.5 vs 6.5. Mercury 2.5 (high) memiliki biaya benchmark lebih rendah di $0.044 vs $0.473. Mercury 2.5 (high) lebih cepat di 7.62s vs 15.28s, dengan tingkat keberhasilan 59.4% vs 63.8%.
Model yang Dibandingkan
- Peringkat
- #195
- Total token output
- 28,602
- Waktu respons (rata-rata)
- 15.28s
- Total Biaya
- $0.473
Model yang direkomendasikan
Mercury 2.5 (high)
It has the best score here (6.5), while costing about 10.9x less than GLM 5.3 (low).
Perbandingan terperinci
| Metrik | Mercury 2.5 Mercury 2.5 high | GLM 5.3 GLM 5.3 low |
|---|---|---|
| Skor | 6.5 | 6.5 |
| Peringkat | #194 | #195 |
| Keandalan | 9.8 | 10.0 |
| Konsistensi | 8.7 | 8.3 |
| Percobaan | 69/69 | 69/69 |
| Tes benar | ||
| Tingkat lulus per percobaan | 59.4% | 63.8% |
| Tes tidak stabil | 4 | 5 |
| Total Run | 69 | 69 |
| Biaya per hasil | 0.362 | 3.936 |
| Total Biaya | $0.044 | $0.473 |
| Harga input | $0.040 / 1M | $1.400 / 1M |
| Harga output | $0.150 / 1M | $4.400 / 1M |
| Total token input | 358,562 | 247,436 |
| Token output | 4,266 | 11,005 |
| Token penalaran | 189,410 | 17,597 |
| Waktu respons (rata-rata) | 7.62s | 15.28s |
| Waktu respons (maks) | 80.30s | 83.60s |
| Waktu respons (total) | 175.36s | 351.49s |
| Parameter | ~100B | 744B total (40B aktif) |
| Ketersediaan | Tertutup | Tertutup |
Showcase generasi model
Hamster playing table tennis
Prompt: Create a detailed SVG illustration of a hamster playing table tennis.
#194 Mercury 2.5
high- Biaya
- $0.001
- Waktu
- 3.5s
- Token
- 2,447 tok
#195 GLM 5.3
low- Biaya
- $0.007
- Waktu
- 28.4s
- Token
- 1,599 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
| Tugas agen | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Mercury 2.5 | 3.9 | 9.6 | 0.0% | 0 | 80.30s | 238,494 | 864 | 18,265 | |
| GLM 5.3 | 5.2 | 3.2 | 33.3% | 1 | 51.27s | 142,447 | 2,473 | 1,321 |
| Trik anti-AI | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Mercury 2.5 | 10.0 | 10.0 | 100.0% | 0 | 1.33s | 782 | 218 | 7,913 | |
| GLM 5.3 | 6.9 | 7.9 | 66.7% | 1 | 4.16s | 639 | 266 | 249 |
| Pemrograman | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Mercury 2.5 | 6.4 | 7.8 | 44.4% | 1 | 6.58s | 7,757 | 415 | 44,012 | |
| GLM 5.3 | 6.2 | 6.9 | 55.6% | 1 | 21.02s | 7,317 | 368 | 6,764 |
| Gabungan | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Mercury 2.5 | 6.4 | 5.8 | 66.7% | 1 | 16.00s | 91,255 | 1,478 | 62,200 | |
| GLM 5.3 | 3.8 | 5.8 | 33.3% | 1 | 53.02s | 80,209 | 6,729 | 5,209 |
| 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 |
|---|---|---|---|---|---|---|---|---|---|
| Mercury 2.5 | 10.0 | 10.0 | 100.0% | 0 | 3.08s | 8,355 | 557 | 9,430 | |
| GLM 5.3 | 10.0 | 10.0 | 100.0% | 0 | 3.42s | 7,149 | 210 | 56 |
| Spesifik domain | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Mercury 2.5 | 2.9 | 7.2 | 11.1% | 1 | 4.86s | 857 | 78 | 22,452 | |
| GLM 5.3 | 5.3 | 10.0 | 33.3% | 0 | 19.23s | 759 | 35 | 3,384 |
| Kecerdasan umum | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Mercury 2.5 | 5.0 | 10.0 | 0.0% | 0 | 2.69s | 528 | 93 | 3,609 | |
| GLM 5.3 | 4.1 | 2.7 | 33.3% | 1 | 4.70s | 498 | 163 | 0 |
| Kepatuhan instruksi | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Mercury 2.5 | 6.5 | 10.0 | 50.0% | 0 | 934ms | 401 | 27 | 1,770 | |
| GLM 5.3 | 10.0 | 10.0 | 100.0% | 0 | 3.40s | 678 | 72 | 81 |
| Pemecahan teka-teki | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Mercury 2.5 | 8.7 | 7.7 | 88.9% | 1 | 1.39s | 771 | 227 | 5,812 | |
| GLM 5.3 | 10.0 | 10.0 | 100.0% | 0 | 4.20s | 672 | 389 | 225 |
| Pemanggilan alat | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Mercury 2.5 | 10.0 | 10.0 | 100.0% | 0 | 3.06s | 9,130 | 283 | 4,484 | |
| GLM 5.3 | 10.0 | 10.0 | 100.0% | 0 | 18.45s | 6,861 | 231 | 0 |
| Pengetahuan umum | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Mercury 2.5 | 3.0 | 10.0 | 0.0% | 0 | 5.48s | 232 | 26 | 9,463 | |
| GLM 5.3 | 3.0 | 10.0 | 0.0% | 0 | 7.42s | 207 | 69 | 308 |
Perbandingan Cepat
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