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The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers state-of-the-art performance comparable to leading-edge models across a wide range of tasks, including language understanding, logical reasoning, code generation, agent-based tasks, image understanding, video understanding, and graphical user interface (GUI) interactions. With its robust code-generation and agent capabilities, the model exhibits strong generalization across diverse agent.
Quality Index
45.0
20th of 442
Top 5%
Coding Index
41.3
24th of 352
Top 7%
Price/1M
$1.35
515th cheapest
335% above median
Top 76%
Speed
53 tok/s
Top 47%
TTFT
1.32s
Context Window
262K
61st largest
Top 25%
Input
$0.60
per 1M tokens
Output
$3.60
per 1M tokens
Blended
$1.35
per 1M tokens
Cheaper than 24% of models. Median price is $0.31/1M tokens.
Daily
$1.35
Monthly
$40.50
53
tokens/sec
Faster than 53% of models
1.32
seconds
Faster than 20% of models
39.35
seconds
Faster than 5% of models
Market Median
46 tok/s
15% faster
Median TTFT
0.42s
217% slower
Throughput/Dollar
39
tok/s per $/1M
Speed Comparison
Context Window
262K
tokens
Larger than 75% of models
Max Output
66K
tokens
25% of context
1.8M
1.4K
Multi-GPU
8x A100 / H100