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Trinity Large Thinking (low) vs Qwen3.5-Flash

Skor rata-rata hampir imbang di 6.0 vs 6.1. Qwen3.5-Flash memiliki biaya benchmark lebih rendah di $0.073 vs $0.656. Qwen3.5-Flash lebih cepat di 25.28s vs 96.78s, dengan tingkat keberhasilan 48.5% vs 39.4%.

Benchmark dihasilkan dari suite pengujian AI BENCHY pada: 2026-07-28

Peringkat
#145
Total token output
815,220
Waktu respons (rata-rata)
96.78s
Total Biaya
$0.656
Peringkat
#140
Total token output
209,201
Waktu respons (rata-rata)
25.28s
Total Biaya
$0.073
Model yang direkomendasikan Qwen3.5-Flash

It has the best score here (6.1), while costing about 9.0x less than Trinity Large Thinking (low).

Perbandingan terperinci

Metrik Trinity Large Thinking Trinity Large Thinking low Rilis: 2026-07-28 Qwen3.5-Flash Qwen3.5-Flash none Rilis: 2026-02-24
Skor 6.0 6.1
Peringkat #145 #140
Keandalan 9.7 10.0
Konsistensi 7.9 9.3
Tes benar
Tingkat lulus per percobaan 48.5% 39.4%
Tes tidak stabil 6 2
Total Run 66 66
Biaya per hasil 8.199 0.933
Total Biaya $0.656 $0.073
Harga input $0.220 / 1M $0.065 / 1M
Harga output $0.850 / 1M $0.260 / 1M
Total token input 122,845 282,347
Token output 117,704 209,201
Token penalaran 697,516 0
Waktu respons (rata-rata) 96.78s 25.28s
Waktu respons (maks) 540.96s 480.96s
Waktu respons (total) 2129.13s 556.24s

Hamster playing table tennis

Prompt: Create a detailed SVG illustration of a hamster playing table tennis.

#145 Trinity Large Thinking

low
Biaya
$0.021
Waktu
173.2s
Token
24,586 tok

#140 Qwen3.5-Flash

none
Biaya
$0.003
Waktu
47.4s
Token
7,799 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

Pemrograman Skor Konsistensi Tingkat lulus per percobaan Tes tidak stabil Tes benar Waktu respons (rata-rata) Token input Token output Token penalaran
Trinity Large Thinking 5.3 10.0 33.3% 0 344.45s 5,441 27,039 217,241
Qwen3.5-Flash 5.5 10.0 33.3% 0 850ms 7,913 519 0

Perbandingan Cepat

Ganti Pasangan Perbandingan