Alibaba (Qwen), open weights

# Qwen3.5 27B

> Qwen3.5 27B by Alibaba (Qwen), released February 2026. Ranked #127 of 354 with a Noometry Index of 41.9. API: $0.30 in / $2.40 out per M tokens. 262K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/qwen3-5-27b
- Last updated: 2026-10-10
- Title: Qwen3.5 27B Benchmarks, Price & Rank (October 2026)

Qwen3.5 27B by Alibaba (Qwen) ranks 127th of 354 ranked models on the Noometry Index as of October 2026, with a score of 41.9. Its strongest category is multimodal, where it ranks 59th. API pricing starts at $0.30 per million input tokens and $2.40 per million output tokens, with a 262K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #127 of 354
- **Index score:** 41.9
- **Evidence:** Confirmed 28 results
- **Provider:** [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba)
- **Released:** February 23, 2026
- **Weights:** Open weights
- **Reasoning:** Yes
- **Context window:** 262K
- **Max output:** 66K
- **Input price:** $0.30 / M
- **Output price:** $2.40 / M
- **Blended price:** $0.82 / M
- **Output speed:** Not measured
- **Value:** #94 of 219
- **Knowledge cutoff:** Unknown
- **Input:** text, image, video, audio
- **Hugging Face:** [Qwen/Qwen3.5-27B](https://huggingface.co/Qwen/Qwen3.5-27B)

## Category scores

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

Qwen3.5 27B category scores

1.  Coding 38.9
2.  Reasoning 27.5
3.  Math 38.8
4.  Knowledge 38.0
5.  Multimodal 39.4
6.  Multilingual 50.8
7.  Instruction Following 73.5
8.  Long Context 43.1
9.  Writing & Preference 59.3
10.  020406080

Qwen3.5 27B category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 38.9 | #168 | 3 |
| [Reasoning](https://noometry.com/best/reasoning) | 27.5 | #117 | 5 |
| [Math](https://noometry.com/best/math) | 38.8 | #127 | 2 |
| [Knowledge](https://noometry.com/best/knowledge) | 38.0 | #150 | 2 |
| [Multimodal](https://noometry.com/best/multimodal) | 39.4 | #59 | 1 |
| [Multilingual](https://noometry.com/best/multilingual) | 50.8 | #115 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 73.5 | #119 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 43.1 | #106 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 59.3 | #111 | 3 |

## Strengths and weaknesses

Categories where Qwen3.5 27B places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Qwen3.5 27B: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Reasoning](https://noometry.com/best/reasoning) | 27.5 | +3.9 | #117 of 350, top 34% |
| [Writing & Preference](https://noometry.com/best/writing) | 59.3 | +5.6 | #111 of 312, top 36% |
| [Long Context](https://noometry.com/best/long-context) | 43.1 | +2.2 | #106 of 296, top 36% |

### Weakest categories

Qwen3.5 27B: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 38.9 | +0.1 | #168 of 340, top 50% |
| [Knowledge](https://noometry.com/best/knowledge) | 38.0 | +0.7 | #150 of 314, top 48% |
| [Multimodal](https://noometry.com/best/multimodal) | 39.4 | +0.9 | #59 of 128, top 47% |

## Closest competitors

The models ranked just above and below Qwen3.5 27B. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Qwen3.5 27B
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Qwen3.5 35B-A3B](https://noometry.com/models/qwen3-5-35b-a3b) | #123 | 42.0 | $0.69 | — | [Compare](https://noometry.com/compare/qwen3-5-27b-vs-qwen3-5-35b-a3b) |
| [GLM-4.7](https://noometry.com/models/glm-4-7) | #124 | 42.0 | $1 | — | [Compare](https://noometry.com/compare/glm-4-7-vs-qwen3-5-27b) |
| [GPT-5.4 nano](https://noometry.com/models/gpt-5-4-nano) | #125 | 41.9 | $0.46 | 19 | [Compare](https://noometry.com/compare/gpt-5-4-nano-vs-qwen3-5-27b) |
| [Amazon Nova Experimental Chat 10 09](https://noometry.com/models/amazon-nova-experimental-chat-10-09) | #126 | 41.9 | — | — | [Compare](https://noometry.com/compare/amazon-nova-experimental-chat-10-09-vs-qwen3-5-27b) |
| [GPT-5 Mini](https://noometry.com/models/gpt-5-mini) | #128 | 41.8 | $0.69 | 3 | [Compare](https://noometry.com/compare/gpt-5-mini-vs-qwen3-5-27b) |
| [ERNIE 5.0 0110](https://noometry.com/models/ernie-5-0) | #129 | 41.8 | — | — | [Compare](https://noometry.com/compare/ernie-5-0-vs-qwen3-5-27b) |
| [Granite 4.2 30b](https://noometry.com/models/granite-4-2-30b) | #130 | 41.8 | — | — | [Compare](https://noometry.com/compare/granite-4-2-30b-vs-qwen3-5-27b) |
| [Muse Glimmer](https://noometry.com/models/muse-glimmer) | #131 | 41.7 | — | — | [Compare](https://noometry.com/compare/muse-glimmer-vs-qwen3-5-27b) |

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 27B Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1358 | #86 of 113, top 77% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 39.5% | #78 of 119, top 66% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1427 | #114 of 294, top 39% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 349.45 | #93 of 105, top 89% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

Qwen3.5 27B Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 201.98 | #52 of 60, top 87% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Qwen3.5 27B Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 47.9% | #63 of 91, top 70% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [Thematic Generalization](https://noometry.com/benchmarks/thematic-generalization) | 45.5% | #18 of 23, top 79% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/generalization) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1414 | #113 of 297, top 39% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 82.4% | #65 of 151, top 44% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 34% | #66 of 125, top 53% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

Qwen3.5 27B Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [MathArena Final-Answer Competitions](https://noometry.com/benchmarks/matharena) | 56.7% | #25 of 29, top 87% |  | [MathArena](https://matharena.ai/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1429 | #88 of 285, top 31% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

Qwen3.5 27B Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 12.1% | #75 of 96, top 79% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1428 | #98 of 273, top 36% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

Qwen3.5 27B Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1241 | #63 of 122, top 52% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |

### Multilingual

Qwen3.5 27B Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1390 | #115 of 297, top 39% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1478 | #66 of 285, top 24% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1410 | #113 of 223, top 51% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1393 | #99 of 231, top 43% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1345 | #101 of 211, top 48% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1358 | #98 of 213, top 47% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1390 | #119 of 283, top 43% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1407 | #102 of 226, top 46% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Qwen3.5 27B Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1393 | #111 of 298, top 38% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Qwen3.5 27B Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1413 | #102 of 291, top 36% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Qwen3.5 27B Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1409 | #111 of 297, top 38% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1362 | #119 of 295, top 41% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1410 | #110 of 295, top 38% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Qwen3.5 27B API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [alibaba](https://www.alibabacloud.com/help/en/model-studio/models) | $0.30 | $2.40 | — | 2026-10-10 |
| [deepinfra](https://deepinfra.com/models) | $0.26 | $2.60 | — | 2026-10-10 |
| [openrouter](https://openrouter.ai/qwen/qwen3.5-27b) | $0.26 | $2.60 | — | 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 27B

-   [Qwen3.5 27B vs Qwen3.5 397B-A17B](https://noometry.com/compare/qwen3-5-27b-vs-qwen3-5-397b-a17b)
-   [Qwen3.5 27B vs Amazon Nova Experimental Chat 10 09](https://noometry.com/compare/amazon-nova-experimental-chat-10-09-vs-qwen3-5-27b)
-   [Qwen3.5 27B vs GPT-5 Mini](https://noometry.com/compare/gpt-5-mini-vs-qwen3-5-27b)
-   [Qwen3.5 27B vs GPT-5.4 nano](https://noometry.com/compare/gpt-5-4-nano-vs-qwen3-5-27b)
-   [Qwen3.5 27B vs ERNIE 5.0 0110](https://noometry.com/compare/ernie-5-0-vs-qwen3-5-27b)
-   [Qwen3.5 27B vs GLM-4.7](https://noometry.com/compare/glm-4-7-vs-qwen3-5-27b)
-   [Qwen3.5 27B vs Granite 4.2 30b](https://noometry.com/compare/granite-4-2-30b-vs-qwen3-5-27b)
-   [Qwen3.5 27B vs GPT-6 Astra](https://noometry.com/compare/gpt-6-astra-vs-qwen3-5-27b)
-   [Qwen3.5 27B vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-qwen3-5-27b)
-   [Qwen3.5 27B vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-qwen3-5-27b)
-   [Qwen3.5 27B vs Kimi K3](https://noometry.com/compare/kimi-k3-vs-qwen3-5-27b)
-   [Qwen3.5 27B vs Grok 4.6](https://noometry.com/compare/grok-4-6-vs-qwen3-5-27b)
-   [Qwen3.5 27B vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-qwen3-5-27b)
-   [Qwen3.5 27B vs Muse Spark 1.3](https://noometry.com/compare/muse-spark-1-3-vs-qwen3-5-27b)

## 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
-   [Qwen3.5 397B-A17B](https://noometry.com/models/qwen3-5-397b-a17b)46.0
-   [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

## Frequently asked questions

### How good is Qwen3.5 27B?

Qwen3.5 27B by Alibaba (Qwen) ranks 127th of 354 ranked models on the Noometry Index as of October 2026, with a score of 41.9. Its strongest category is multimodal, where it ranks 59th. API pricing starts at $0.30 per million input tokens and $2.40 per million output tokens, with a 262K-token context window.

### How much does Qwen3.5 27B cost?

Qwen3.5 27B costs $0.30 per million input tokens and $2.40 per million output tokens on Alibaba (Qwen)'s own API.

### What is Qwen3.5 27B's context window?

Qwen3.5 27B accepts up to 262K tokens of input and can write up to 66K tokens in one response.

### Is Qwen3.5 27B open source?

Yes. Qwen3.5 27B's weights are downloadable from Hugging Face (Qwen/Qwen3.5-27B); check the license for commercial terms.

### What are Qwen3.5 27B's strengths and weaknesses?

Relative to other ranked models, Qwen3.5 27B places best in reasoning, writing & preference, long context and lowest in coding, knowledge, multimodal.

### What is Qwen3.5 27B best at?

Its best category is multimodal, where it ranks 59th on Noometry.

### Cite this page

Noometry. (2026). Qwen3.5 27B benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/qwen3-5-27b

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/qwen3-5-27b.md).