Qwen: Qwen3.5 397B A17B
Server-rendered model summary page for indexing/share previews. Use the interactive explorer for full filtering and comparison.
Identifiers & provenance
- Primary ID
- qwen/qwen3.5-397b-a17b
- OpenRouter ID
- qwen/qwen3.5-397b-a17b
- Arena ID
- qwen3.5-397b-a17b
- Canonical slug
- qwen/qwen3.5-397b-a17b-20260216
- Match method
- openrouter_name
- Match key
- qwen3.5-397b-a17b
Source semantics
- Arena rank is a human-preference leaderboard signal, not a universal truth metric.
- OpenRouter usage/popularity reflects adoption/traffic, not benchmark quality.
- Pricing fields may differ by provider and can include extra modes beyond prompt/completion.
Read more on Methodology & data sources.
Description
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.
Raw fields snapshot
{
"id": "qwen/qwen3.5-397b-a17b",
"name": "Qwen: Qwen3.5 397B A17B",
"description": "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.",
"created": 1771223018,
"canonical_slug": "qwen/qwen3.5-397b-a17b-20260216",
"hugging_face_id": "Qwen/Qwen3.5-397B-A17B",
"source_type": "both",
"context_length": 262144,
"max_completion_tokens": 65536,
"is_moderated": false,
"architecture": {
"modality": "text+image+video->text",
"input_modalities": [
"text",
"image",
"video"
],
"output_modalities": [
"text"
],
"tokenizer": "Qwen3",
"instruct_type": null
},
"input_modalities": [
"text",
"image",
"video"
],
"output_modalities": [
"text"
],
"modality": "text+image+video->text",
"tokenizer": "Qwen3",
"instruct_type": null,
"supported_parameters": [
"frequency_penalty",
"include_reasoning",
"logit_bias",
"max_tokens",
"min_p",
"presence_penalty",
"reasoning",
"repetition_penalty",
"response_format",
"seed",
"stop",
"structured_outputs",
"temperature",
"tool_choice",
"tools",
"top_k",
"top_p"
],
"default_parameters": {
"temperature": 0.6,
"top_p": 0.95,
"frequency_penalty": null
},
"per_request_limits": null,
"top_provider": {
"context_length": 262144,
"max_completion_tokens": 65536,
"is_moderated": false
},
"pricing": {
"prompt": "0.00000055",
"completion": "0.0000035",
"input_cache_read": "0.00000055"
},
"PPM": {
"prompt": 0.55,
"completion": 3.5,
"input_cache_read": 0.55
},
"openrouter_raw": {
"id": "qwen/qwen3.5-397b-a17b",
"canonical_slug": "qwen/qwen3.5-397b-a17b-20260216",
"hugging_face_id": "Qwen/Qwen3.5-397B-A17B",
"name": "Qwen: Qwen3.5 397B A17B",
"created": 1771223018,
"description": "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.",
"context_length": 262144,
"architecture": {
"modality": "text+image+video->text",
"input_modalities": [
"text",
"image",
"video"
],
"output_modalities": [
"text"
],
"tokenizer": "Qwen3",
"instruct_type": null
},
"pricing": {
"prompt": "0.00000055",
"completion": "0.0000035",
"input_cache_read": "0.00000055"
},
"top_provider": {
"context_length": 262144,
"max_completion_tokens": 65536,
"is_moderated": false
},
"per_request_limits": null,
"supported_parameters": [
"frequency_penalty",
"include_reasoning",
"logit_bias",
"max_tokens",
"min_p",
"presence_penalty",
"reasoning",
"repetition_penalty",
"response_format",
"seed",
"stop",
"structured_outputs",
"temperature",
"tool_choice",
"tools",
"top_k",
"top_p"
],
"default_parameters": {
"temperature": 0.6,
"top_p": 0.95,
"frequency_penalty": null
},
"expiration_date": null
},
"categories": {
"text-expert": {
"score": 1458,
"rank_ub": 30,
"votes": 375,
"ci_95": "±29"
},
"text-industry-software-and-it-services": {
"score": 1497,
"rank_ub": 14,
"votes": 1756,
"ci_95": "±13"
},
"text-industry-writing-and-literature-and-language": {
"score": 1427,
"rank_ub": 22,
"votes": 1034,
"ci_95": "±18"
},
"text-overall": {
"score": 1454,
"rank_ub": 17,
"votes": 4958,
"ci_95": "±8"
},
"text-industry-life-and-physical-and-social-science": {
"score": 1478,
"rank_ub": 16,
"votes": 732,
"ci_95": "±21"
},
"text-industry-mathematical": {
"score": 1427,
"rank_ub": 42,
"votes": 237,
"ci_95": "±35"
},
"text-industry-entertainment-and-sports-and-media": {
"score": 1415,
"rank_ub": 26,
"votes": 803,
"ci_95": "±20"
},
"text-industry-business-and-management-and-financial-operations": {
"score": 1447,
"rank_ub": 19,
"votes": 861,
"ci_95": "±19"
},
"text-industry-medicine-and-healthcare": {
"score": 1450,
"rank_ub": 47,
"votes": 306,
"ci_95": "±33"
},
"text-industry-legal-and-government": {
"score": 1422,
"rank_ub": 56,
"votes": 314,
"ci_95": "±31"
},
"text-instruction-following": {
"score": 1442,
"rank_ub": 20,
"votes": 1359,
"ci_95": "±15"
},
"text-math": {
"score": 1444,
"rank_ub": 23,
"votes": 342,
"ci_95": "±30"
},
"text-creative-writing": {
"score": 1419,
"rank_ub": 26,
"votes": 752,
"ci_95": "±21"
},
"text-multi-turn": {
"score": 1443,
"rank_ub": 32,
"votes": 830,
"ci_95": "±20"
},
"text-hard-prompts": {
"score": 1477,
"rank_ub": 19,
"votes": 2775,
"ci_95": "±11"
},
"text-coding": {
"score": 1502,
"rank_ub": 19,
"votes": 1146,
"ci_95": "±16"
},
"text-hard-prompts-english": {
"score": 1480,
"rank_ub": 19,
"votes": 1379,
"ci_95": "±15"
},
"text-longer-query": {
"score": 1458,
"rank_ub": 19,
"votes": 1364,
"ci_95": "±15"
},
"text-english": {
"score": 1460,
"rank_ub": 22,
"votes": 2303,
"ci_95": "±12"
},
"text-spanish": {
"score": 1463,
"rank_ub": 13,
"votes": 198,
"ci_95": "±42"
},
"text-russian": {
"score": 1456,
"rank_ub": 13,
"votes": 558,
"ci_95": "±24"
},
"text-exclude-ties": {
"score": 1458,
"rank_ub": 17,
"votes": 3294,
"ci_95": "±13"
},
"code-overall": {
"score": 1396,
"rank_ub": 17,
"votes": 2473,
"ci_95": "+12/-12"
},
"code-html": {
"score": 1386,
"rank_ub": 23,
"votes": 321,
"ci_95": "+33/-33"
},
"code-react": {
"score": 1384,
"rank_ub": 15,
"votes": 2142,
"ci_95": "+13/-13"
},
"vision-overall": {
"score": 1244,
"rank_ub": 11,
"votes": 3437,
"ci_95": "±12"
},
"vision-english": {
"score": 1233,
"rank_ub": 12,
"votes": 1375,
"ci_95": "±17"
},
"vision-creative-writing": {
"score": 1232,
"rank_ub": 13,
"votes": 183,
"ci_95": "±44"
},
"vision-diagram": {
"score": 1256,
"rank_ub": 13,
"votes": 796,
"ci_95": "±20"
},
"vision-homework": {
"score": 1278,
"rank_ub": 9,
"votes": 501,
"ci_95": "±25"
},
"vision-ocr": {
"score": 1264,
"rank_ub": 9,
"votes": 2282,
"ci_95": "±13"
}
},
"arena_model_id": "qwen3.5-397b-a17b",
"leaderboard_name": "qwen3.5-397b-a17b",
"match_method": "openrouter_name",
"match_key": "qwen3.5-397b-a17b",
"match_input": "Qwen: Qwen3.5 397B A17B",
"arena_aliases": []
}