- Peringkat
- #292
- Total token output
- 2,169
- Waktu respons (rata-rata)
- 2.98s
- Total Biaya
- $0.008
Trinity Large Preview vs MiniMax M2.5 (medium)
Trinity Large Preview unggul dalam skor rata-rata dengan 4.8 vs 4.6. Trinity Large Preview memiliki biaya benchmark lebih rendah di $0.008 vs $0.363. Trinity Large Preview lebih cepat di 2.98s vs 69.08s, dengan tingkat keberhasilan 21.2% vs 43.9%.
Model yang Dibandingkan
- Peringkat
- #300
- Total token output
- 272,229
- Waktu respons (rata-rata)
- 69.08s
- Total Biaya
- $0.363
Model yang direkomendasikan
Trinity Large Preview
It has the best score here (4.8), while costing about 46.8x less than MiniMax M2.5 (medium).
Perbandingan terperinci
| Metrik | Trinity Large Preview Trinity Large Preview none | MiniMax M2.5 MiniMax M2.5 medium |
|---|---|---|
| Skor | 4.8 | 4.6 |
| Peringkat | #292 | #300 |
| Keandalan | 10.0 | 10.0 |
| Konsistensi | 8.9 | 6.6 |
| Percobaan | 63/66 | 66/66 |
| Tes benar | ||
| Tingkat lulus per percobaan | 21.2% | 43.9% |
| Tes tidak stabil | 2 | 9 |
| Total Run | 63 | 66 |
| Biaya per hasil | 0.017 | 6.463 |
| Total Biaya | $0.008 | $0.363 |
| Harga input | $0.243 / 1M | $0.300 / 1M |
| Harga output | $0.243 / 1M | $1.200 / 1M |
| Total token input | 29,828 | 142,621 |
| Token output | 2,169 | 20,409 |
| Token penalaran | 0 | 251,820 |
| Waktu respons (rata-rata) | 2.98s | 69.08s |
| Waktu respons (maks) | 14.34s | 251.36s |
| Waktu respons (total) | 56.57s | 1036.24s |
| Parameter | 398B total (13B aktif) | 230B total (10B aktif) |
| Ketersediaan | Sumber terbuka | Bobot tersedia |
Showcase generasi model
Hamster playing table tennis
Prompt: Create a detailed SVG illustration of a hamster playing table tennis.
#292 Trinity Large Preview
none
No endpoints found for arcee-ai/trinity-large-preview:free.
- Biaya
- $0.000
- Waktu
- 0.0s
- Token
- 0 tok
#300 MiniMax M2.5
medium
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
| Trik anti-AI | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Trinity Large Preview | 3.1 | 10.0 | 0.0% | 0 | 2.07s | 651 | 550 | 0 | |
| MiniMax M2.5 | 7.9 | 6.3 | 83.3% | 2 | 20.82s | 612 | 286 | 45,344 |
| Pemrograman | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Trinity Large Preview | 3.7 | 7.7 | 11.1% | 1 | 14.34s | 738 | 397 | 0 | |
| MiniMax M2.5 | 3.4 | 9.1 | 0.0% | 0 | 188.58s | 6,076 | 357 | 106,177 |
| Gabungan | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Trinity Large Preview | 1.5 | 5.0 | 0.0% | 0 | 8.91s | 12,053 | 294 | 0 | |
| MiniMax M2.5 | 3.7 | 1.8 | 50.0% | 2 | 83.19s | 119,959 | 16,687 | 18,834 |
| 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 |
|---|---|---|---|---|---|---|---|---|---|
| Trinity Large Preview | 10.0 | 10.0 | 100.0% | 0 | 3.26s | 6,900 | 186 | 0 | |
| MiniMax M2.5 | 4.6 | 1.7 | 66.7% | 2 | 7.48s | 6,584 | 266 | 3,835 |
| Spesifik domain | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Trinity Large Preview | 5.3 | 10.0 | 33.3% | 0 | 877ms | 738 | 25 | 0 | |
| MiniMax M2.5 | 2.9 | 7.2 | 11.1% | 1 | 158.88s | 368 | 14 | 45,372 |
| Kecerdasan umum | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Trinity Large Preview | 4.5 | 10.0 | 0.0% | 0 | 873ms | 498 | 104 | 0 | |
| MiniMax M2.5 | 3.8 | 2.5 | 33.3% | 1 | 6.63s | 492 | 25 | 1,686 |
| Kepatuhan instruksi | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Trinity Large Preview | 3.5 | 10.0 | 0.0% | 0 | 822ms | 678 | 63 | 0 | |
| MiniMax M2.5 | 7.5 | 10.0 | 50.0% | 0 | 621ms | 699 | 156 | 1,495 |
| Pemecahan teka-teki | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Trinity Large Preview | 3.6 | 7.7 | 11.1% | 1 | 1.97s | 669 | 265 | 0 | |
| MiniMax M2.5 | 5.3 | 7.2 | 44.4% | 1 | 11.21s | 495 | 1,069 | 9,605 |
| Pemanggilan alat | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Trinity Large Preview | 10.0 | 10.0 | 100.0% | 0 | 6.67s | 6,699 | 267 | 0 | |
| MiniMax M2.5 | 10.0 | 10.0 | 100.0% | 0 | 15.35s | 7,123 | 269 | 937 |
| Pengetahuan umum | Skor | Konsistensi | Tingkat lulus per percobaan | Tes tidak stabil | Tes benar | Waktu respons (rata-rata) | Token input | Token output | Token penalaran |
|---|---|---|---|---|---|---|---|---|---|
| Trinity Large Preview | 3.0 | 10.0 | 0.0% | 0 | 777ms | 204 | 18 | 0 | |
| MiniMax M2.5 | 3.0 | 10.0 | 0.0% | 0 | 80.79s | 213 | 1,280 | 18,535 |
Perbandingan Cepat
Ganti Pasangan Perbandingan
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