- Peringkat
- #194
- Total token output
- 193,676
- Waktu respons (rata-rata)
- 7.62s
- Total Biaya
- $0.044
Mercury 2.5 (high) vs GLM 5.1
Skor rata-rata hampir imbang di 6.5 vs 6.5. Mercury 2.5 (high) memiliki biaya benchmark lebih rendah di $0.044 vs $0.311. Mercury 2.5 (high) lebih cepat di 7.62s vs 11.10s, dengan tingkat keberhasilan 59.4% vs 47.8%.
Model yang Dibandingkan
- Peringkat
- #196
- Total token output
- 20,076
- Waktu respons (rata-rata)
- 11.10s
- Total Biaya
- $0.311
Model yang direkomendasikan
Mercury 2.5 (high)
It has the best score here (6.5), while costing about 7.2x less than GLM 5.1.
Perbandingan terperinci
| Metrik | Mercury 2.5 Mercury 2.5 high | GLM 5.1 GLM 5.1 none |
|---|---|---|
| Skor | 6.5 | 6.5 |
| Peringkat | #194 | #196 |
| Keandalan | 9.8 | 10.0 |
| Konsistensi | 8.7 | 8.2 |
| Percobaan | 69/69 | 69/69 |
| Tes benar | ||
| Tingkat lulus per percobaan | 59.4% | 47.8% |
| Tes tidak stabil | 4 | 5 |
| Total Run | 69 | 69 |
| Biaya per hasil | 0.362 | 3.478 |
| Total Biaya | $0.044 | $0.311 |
| Harga input | $0.040 / 1M | $0.965 / 1M |
| Harga output | $0.150 / 1M | $3.032 / 1M |
| Total token input | 358,562 | 258,956 |
| Token output | 4,266 | 20,076 |
| Token penalaran | 189,410 | 0 |
| Waktu respons (rata-rata) | 7.62s | 11.10s |
| Waktu respons (maks) | 80.30s | 107.19s |
| Waktu respons (total) | 175.36s | 255.39s |
| Parameter | ~100B | 744B total (40B aktif) |
| Ketersediaan | Tertutup | Sumber terbuka |
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
#196 GLM 5.1
none
Reached the allocated time limit (300 seconds) without receiving showcase output.
- Biaya
- $0.000
- Waktu
- 300.0s
- Token
- 0 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 5.1 | 10.0 | 10.0 | 100.0% | 0 | 107.19s | 134,737 | 5,683 | 0 |
| 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 5.1 | 4.0 | 6.3 | 25.0% | 2 | 2.11s | 555 | 305 | 0 |
| 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 5.1 | 3.9 | 9.7 | 0.0% | 0 | 4.96s | 7,256 | 525 | 0 |
| 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 5.1 | 2.8 | 1.8 | 33.3% | 2 | 46.88s | 99,907 | 12,768 | 0 |
| 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 5.1 | 10.0 | 10.0 | 100.0% | 0 | 1.08s | 7,107 | 204 | 0 |
| 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 5.1 | 5.3 | 7.2 | 44.4% | 1 | 2.27s | 697 | 24 | 0 |
| 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 5.1 | 5.0 | 10.0 | 0.0% | 0 | 790ms | 477 | 39 | 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 5.1 | 9.8 | 10.0 | 100.0% | 0 | 1.98s | 636 | 66 | 0 |
| 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 5.1 | 7.7 | 10.0 | 66.7% | 0 | 1.45s | 609 | 151 | 0 |
| 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 5.1 | 10.0 | 10.0 | 100.0% | 0 | 10.68s | 6,789 | 300 | 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 5.1 | 3.0 | 10.0 | 0.0% | 0 | 2.34s | 186 | 11 | 0 |
Perbandingan Cepat
Ganti Pasangan Perbandingan
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