Alibaba (Qwen), open weights

# Qwen3.5 397B-A17B

> Qwen3.5 397B-A17B by Alibaba (Qwen), released February 2026. Ranked #67 of 354 with a Noometry Index of 46.0. API: $0.60 in / $3.60 out per M tokens. 262K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/qwen3-5-397b-a17b
- Last updated: 2026-10-10
- Title: Qwen3.5 397B-A17B Benchmarks, Price & Rank (October 2026)

Qwen3.5 397B-A17B by Alibaba (Qwen) ranks 67th of 354 ranked models on the Noometry Index as of October 2026, with a score of 46.0. Its strongest category is multimodal, where it ranks 44th. API pricing starts at $0.60 per million input tokens and $3.60 per million output tokens, with a 262K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #67 of 354
- **Index score:** 46.0
- **Evidence:** Confirmed 36 results
- **Provider:** [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba)
- **Released:** February 1, 2026
- **Weights:** Open weights
- **Reasoning:** Yes
- **Context window:** 262K
- **Max output:** 66K
- **Input price:** $0.60 / M
- **Output price:** $3.60 / M
- **Blended price:** $1.35 / M
- **Output speed:** 9 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #128 of 219
- **Knowledge cutoff:** January 2025
- **Input:** text, image, video, audio
- **Hugging Face:** [Qwen/Qwen3.5-397B-A17B](https://huggingface.co/Qwen/Qwen3.5-397B-A17B)

## Category scores

Each category score combines every public result we have in that category.

Qwen3.5 397B-A17B category scores

1.  Coding 42.0
2.  Agentic & Tool Use 33.3
3.  Reasoning 34.5
4.  Math 46.1
5.  Knowledge 53.3
6.  Multimodal 40.7
7.  Multilingual 53.7
8.  Instruction Following 75.0
9.  Long Context 44.1
10.  Writing & Preference 62.3
11.  020406080

Qwen3.5 397B-A17B category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 42.0 | #114 | 2 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 33.3 | #53 | 5 |
| [Reasoning](https://noometry.com/best/reasoning) | 34.5 | #70 | 8 |
| [Math](https://noometry.com/best/math) | 46.1 | #73 | 3 |
| [Knowledge](https://noometry.com/best/knowledge) | 53.3 | #58 | 2 |
| [Multimodal](https://noometry.com/best/multimodal) | 40.7 | #44 | 1 |
| [Multilingual](https://noometry.com/best/multilingual) | 53.7 | #59 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 75.0 | #77 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 44.1 | #74 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 62.3 | #79 | 4 |

## Strengths and weaknesses

Categories where Qwen3.5 397B-A17B places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Qwen3.5 397B-A17B: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Knowledge](https://noometry.com/best/knowledge) | 53.3 | +16.0 | #58 of 314, top 19% |
| [Multilingual](https://noometry.com/best/multilingual) | 53.7 | +6.3 | #59 of 297, top 20% |
| [Reasoning](https://noometry.com/best/reasoning) | 34.5 | +10.9 | #70 of 350, top 20% |

### Weakest categories

Qwen3.5 397B-A17B: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 33.3 | +3.0 | #53 of 154, top 35% |
| [Multimodal](https://noometry.com/best/multimodal) | 40.7 | +2.2 | #44 of 128, top 35% |
| [Coding](https://noometry.com/best/coding) | 42.0 | +3.3 | #114 of 340, top 34% |

## Closest competitors

The models ranked just above and below Qwen3.5 397B-A17B. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Qwen3.5 397B-A17B
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Inkling-Small](https://noometry.com/models/inkling-small) | #63 | 46.5 | $0.64 | — | [Compare](https://noometry.com/compare/inkling-small-vs-qwen3-5-397b-a17b) |
| [GPT-5 Pro](https://noometry.com/models/gpt-5-pro) | #64 | 46.4 | $41.25 | 5 | [Compare](https://noometry.com/compare/gpt-5-pro-vs-qwen3-5-397b-a17b) |
| [Grok 4.20 Multi-Agent](https://noometry.com/models/grok-4-20-multi-agent) | #65 | 46.2 | $1.56 | — | [Compare](https://noometry.com/compare/grok-4-20-multi-agent-vs-qwen3-5-397b-a17b) |
| [GLM-5](https://noometry.com/models/glm-5) | #66 | 46.1 | $1.55 | 23 | [Compare](https://noometry.com/compare/glm-5-vs-qwen3-5-397b-a17b) |
| [Qwen3.8 27B](https://noometry.com/models/qwen3-8-27b) | #68 | 46.0 | $1.11 | — | [Compare](https://noometry.com/compare/qwen3-5-397b-a17b-vs-qwen3-8-27b) |
| [GPT-5.3 Codex](https://noometry.com/models/gpt-5-3-codex) | #69 | 45.8 | $4.81 | — | [Compare](https://noometry.com/compare/gpt-5-3-codex-vs-qwen3-5-397b-a17b) |
| [Kimi K2 Thinking Turbo](https://noometry.com/models/kimi-k2-thinking-turbo) | #70 | 45.8 | — | — | [Compare](https://noometry.com/compare/kimi-k2-thinking-turbo-vs-qwen3-5-397b-a17b) |
| [Qwen3.5 Max Preview](https://noometry.com/models/qwen3-5-max-preview) | #71 | 45.3 | — | — | [Compare](https://noometry.com/compare/qwen3-5-397b-a17b-vs-qwen3-5-max-preview) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

Qwen3.5 397B-A17B Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1400 | #72 of 113, top 64% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1465 | #65 of 294, top 23% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Agentic & Tool Use

Qwen3.5 397B-A17B Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 24.9% | #46 of 49, top 94% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [τ²-bench Airline](https://noometry.com/benchmarks/tau2-airline) | 81.5% | #5 of 7, top 72% | enabled | [τ²-bench](https://taubench.com/) | 2026-03-02 |
| [τ²-bench Banking](https://noometry.com/benchmarks/tau2-banking) | 9.8% | #26 of 26, top 100% | enabled | [τ²-bench](https://taubench.com/) | 2026-03-02 |
| [τ²-bench Retail](https://noometry.com/benchmarks/tau2-retail) | 84.4% | Best of 7 | enabled | [τ²-bench](https://taubench.com/) | 2026-03-02 |
| [τ²-bench Telecom](https://noometry.com/benchmarks/tau2-telecom) | 97.8% | Best of 7 | enabled | [τ²-bench](https://taubench.com/) | 2026-03-02 |

### Reasoning

Qwen3.5 397B-A17B Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 73.7% | #15 of 99, top 16% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 58.9% | #58 of 91, top 64% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 12% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 13% | #74 of 129, top 58% | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Thematic Generalization](https://noometry.com/benchmarks/thematic-generalization) | 65.1% | #8 of 23, top 35% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/generalization) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1448 | #65 of 297, top 22% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 18% | #48 of 74, top 65% | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 87.5% | #51 of 151, top 34% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 37.9% | #52 of 125, top 42% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 146.65 | #71 of 213, top 34% |  | [Epoch AI](https://epoch.ai/eci) | 2026-02-13 |

### Math

Qwen3.5 397B-A17B Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 29.5% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 31.2% | #61 of 81, top 76% | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 88.9% | #57 of 173, top 33% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 82.2% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1454 | #60 of 285, top 22% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

Qwen3.5 397B-A17B Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 85.9% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 86.4% | #58 of 186, top 32% | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1462 | #60 of 273, top 22% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

Qwen3.5 397B-A17B Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1263 | #46 of 122, top 38% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |

### Multilingual

Qwen3.5 397B-A17B Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1430 | #59 of 297, top 20% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1500 | #44 of 285, top 16% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1461 | #46 of 223, top 21% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1447 | #47 of 231, top 21% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1426 | #36 of 211, top 18% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1384 | #73 of 213, top 35% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1429 | #69 of 283, top 25% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1441 | #65 of 226, top 29% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Qwen3.5 397B-A17B Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1424 | #69 of 298, top 24% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Qwen3.5 397B-A17B Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1442 | #61 of 291, top 21% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Qwen3.5 397B-A17B Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1438 | #67 of 297, top 23% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1401 | #80 of 295, top 28% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1478 | #60 of 115, top 53% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1446 | #60 of 295, top 21% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Qwen3.5 397B-A17B API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [alibaba](https://www.alibabacloud.com/help/en/model-studio/models) | $0.60 | $3.60 | — | 2026-10-10 |
| [deepinfra](https://deepinfra.com/models) | $0.45 | $3 | $0.22 | 2026-10-10 |
| [openrouter](https://openrouter.ai/qwen/qwen3.5-397b-a17b) | $0.55 | $3.50 | $0.23 | 2026-10-10 |
| [together](https://docs.together.ai/docs/serverless-models) | $0.60 | $3.60 | $0.35 | 2026-10-10 |

[All Alibaba (Qwen) API prices →](https://noometry.com/llm-pricing/alibaba) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare Qwen3.5 397B-A17B

-   [Qwen3.5 397B-A17B vs Qwen3-4B](https://noometry.com/compare/qwen3-4b-vs-qwen3-5-397b-a17b)
-   [Qwen3.5 397B-A17B vs GLM-5](https://noometry.com/compare/glm-5-vs-qwen3-5-397b-a17b)
-   [Qwen3.5 397B-A17B vs Qwen3.8 27B](https://noometry.com/compare/qwen3-5-397b-a17b-vs-qwen3-8-27b)
-   [Qwen3.5 397B-A17B vs Grok 4.20 Multi-Agent](https://noometry.com/compare/grok-4-20-multi-agent-vs-qwen3-5-397b-a17b)
-   [Qwen3.5 397B-A17B vs GPT-5.3 Codex](https://noometry.com/compare/gpt-5-3-codex-vs-qwen3-5-397b-a17b)
-   [Qwen3.5 397B-A17B vs GPT-5 Pro](https://noometry.com/compare/gpt-5-pro-vs-qwen3-5-397b-a17b)
-   [Qwen3.5 397B-A17B vs Kimi K2 Thinking Turbo](https://noometry.com/compare/kimi-k2-thinking-turbo-vs-qwen3-5-397b-a17b)
-   [Qwen3.5 397B-A17B vs GPT-6 Astra](https://noometry.com/compare/gpt-6-astra-vs-qwen3-5-397b-a17b)
-   [Qwen3.5 397B-A17B vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-qwen3-5-397b-a17b)
-   [Qwen3.5 397B-A17B vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-qwen3-5-397b-a17b)
-   [Qwen3.5 397B-A17B vs Kimi K3](https://noometry.com/compare/kimi-k3-vs-qwen3-5-397b-a17b)
-   [Qwen3.5 397B-A17B vs Grok 4.6](https://noometry.com/compare/grok-4-6-vs-qwen3-5-397b-a17b)
-   [Qwen3.5 397B-A17B vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-qwen3-5-397b-a17b)
-   [Qwen3.5 397B-A17B vs Muse Spark 1.3](https://noometry.com/compare/muse-spark-1-3-vs-qwen3-5-397b-a17b)

## Other Alibaba (Qwen) models

-   [Qwen3.8 Max](https://noometry.com/models/qwen3-8-max)56.8
-   [Qwen3.7 Max](https://noometry.com/models/qwen3-7-max)51.5
-   [Qwen3.6 Max Preview](https://noometry.com/models/qwen3-6-max-preview)51.5
-   [Qwen3.6 Plus](https://noometry.com/models/qwen3-6-plus)47.5
-   [Qwen3.8 27B](https://noometry.com/models/qwen3-8-27b)46.0
-   [Qwen3.5 Max Preview](https://noometry.com/models/qwen3-5-max-preview)45.3
-   [Qwen3.7 Plus](https://noometry.com/models/qwen3-7-plus)45.3
-   [Qwen3 Max](https://noometry.com/models/qwen3-max)43.7

## Frequently asked questions

### How good is Qwen3.5 397B-A17B?

Qwen3.5 397B-A17B by Alibaba (Qwen) ranks 67th of 354 ranked models on the Noometry Index as of October 2026, with a score of 46.0. Its strongest category is multimodal, where it ranks 44th. API pricing starts at $0.60 per million input tokens and $3.60 per million output tokens, with a 262K-token context window.

### How much does Qwen3.5 397B-A17B cost?

Qwen3.5 397B-A17B costs $0.60 per million input tokens and $3.60 per million output tokens on Alibaba (Qwen)'s own API.

### What is Qwen3.5 397B-A17B's context window?

Qwen3.5 397B-A17B accepts up to 262K tokens of input and can write up to 66K tokens in one response.

### Is Qwen3.5 397B-A17B open source?

Yes. Qwen3.5 397B-A17B's weights are downloadable from Hugging Face (Qwen/Qwen3.5-397B-A17B); check the license for commercial terms.

### How fast is Qwen3.5 397B-A17B?

Qwen3.5 397B-A17B generated about 9 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are Qwen3.5 397B-A17B's strengths and weaknesses?

Relative to other ranked models, Qwen3.5 397B-A17B places best in knowledge, multilingual, reasoning and lowest in agentic & tool use, multimodal, coding.

### What is Qwen3.5 397B-A17B best at?

Its best category is multimodal, where it ranks 44th on Noometry.

### Cite this page

Noometry. (2026). Qwen3.5 397B-A17B benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/qwen3-5-397b-a17b

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/qwen3-5-397b-a17b.md).