Alibaba (Qwen), open weights

# Qwen3.8 27B

> Qwen3.8 27B by Alibaba (Qwen), released August 2026. Ranked #68 of 354 with a Noometry Index of 46.0. API: $0.99 in / $1.49 out per M tokens. 262K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/qwen3-8-27b
- Last updated: 2026-10-10
- Title: Qwen3.8 27B Benchmarks, Price & Rank (October 2026)

Qwen3.8 27B by Alibaba (Qwen) ranks 68th 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 37th. API pricing starts at $0.99 per million input tokens and $1.49 per million output tokens, with a 262K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #68 of 354
- **Index score:** 46.0
- **Evidence:** Confirmed 31 results
- **Provider:** [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba)
- **Released:** August 14, 2026
- **Weights:** Open weights
- **Reasoning:** Yes
- **Context window:** 262K
- **Max output:** 33K
- **Input price:** $0.99 / M
- **Output price:** $1.49 / M
- **Blended price:** $1.11 / M
- **Output speed:** Not measured
- **Value:** #114 of 219
- **Knowledge cutoff:** Unknown
- **Input:** text, image, video
- **Hugging Face:** [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B)

## Category scores

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

Qwen3.8 27B category scores

1.  Coding 50.5
2.  Agentic & Tool Use 32.9
3.  Reasoning 41.0
4.  Math 37.1
5.  Knowledge 41.6
6.  Multimodal 41.3
7.  Multilingual 53.7
8.  Instruction Following 75.8
9.  Long Context 44.3
10.  Writing & Preference 65.8
11.  020406080

Qwen3.8 27B category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 50.5 | #44 | 3 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 32.9 | #57 | 1 |
| [Reasoning](https://noometry.com/best/reasoning) | 41.0 | #54 | 8 |
| [Math](https://noometry.com/best/math) | 37.1 | #161 | 2 |
| [Knowledge](https://noometry.com/best/knowledge) | 41.6 | #109 | 1 |
| [Multimodal](https://noometry.com/best/multimodal) | 41.3 | #37 | 1 |
| [Multilingual](https://noometry.com/best/multilingual) | 53.7 | #60 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 75.8 | #53 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 44.3 | #70 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 65.8 | #43 | 4 |

## Strengths and weaknesses

Categories where Qwen3.8 27B places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Qwen3.8 27B: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 50.5 | +11.8 | #44 of 340, top 13% |
| [Writing & Preference](https://noometry.com/best/writing) | 65.8 | +12.0 | #43 of 312, top 14% |
| [Reasoning](https://noometry.com/best/reasoning) | 41.0 | +17.4 | #54 of 350, top 16% |

### Weakest categories

Qwen3.8 27B: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Math](https://noometry.com/best/math) | 37.1 | +0.5 | #161 of 327, top 50% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 32.9 | +2.5 | #57 of 154, top 38% |
| [Knowledge](https://noometry.com/best/knowledge) | 41.6 | +4.3 | #109 of 314, top 35% |

## Closest competitors

The models ranked just above and below Qwen3.8 27B. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Qwen3.8 27B
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [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-qwen3-8-27b) |
| [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-qwen3-8-27b) |
| [GLM-5](https://noometry.com/models/glm-5) | #66 | 46.1 | $1.55 | 23 | [Compare](https://noometry.com/compare/glm-5-vs-qwen3-8-27b) |
| [Qwen3.5 397B-A17B](https://noometry.com/models/qwen3-5-397b-a17b) | #67 | 46.0 | $1.35 | 9 | [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-qwen3-8-27b) |
| [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-qwen3-8-27b) |
| [Qwen3.5 Max Preview](https://noometry.com/models/qwen3-5-max-preview) | #71 | 45.3 | — | — | [Compare](https://noometry.com/compare/qwen3-5-max-preview-vs-qwen3-8-27b) |
| [Qwen3.7 Plus](https://noometry.com/models/qwen3-7-plus) | #72 | 45.3 | $0.70 | — | [Compare](https://noometry.com/compare/qwen3-7-plus-vs-qwen3-8-27b) |

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

Qwen3.8 27B Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1593 | #23 of 113, top 21% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SciCode](https://noometry.com/benchmarks/scicode) | 39.8% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 38.1% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 35.6% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 46.6% | #57 of 121, top 48% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1482 | #44 of 294, top 15% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Agentic & Tool Use

Qwen3.8 27B Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 47.5% | #28 of 49, top 58% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Qwen3.8 27B Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 22.8% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 13.2% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 1.5% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 42.4% | #35 of 83, top 43% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 54.5% | #60 of 91, top 66% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 69.2% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 68.7% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 34% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 87.5% | #31 of 83, top 38% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 0% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 0% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 0.3% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 5.4% | #57 of 134, top 43% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1460 | #48 of 297, top 17% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 88% | #46 of 151, top 31% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 41.4% | #43 of 125, top 35% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Surface Evolver Bench](https://noometry.com/benchmarks/surface-evolver-bench) | 45% | #18 of 25, top 72% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 149.38 | #57 of 213, top 27% |  | [Epoch AI](https://epoch.ai/eci) | 2026-08-14 |

### Math

Qwen3.8 27B Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 16% | #56 of 77, top 73% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1456 | #56 of 285, top 20% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

Qwen3.8 27B Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1482 | #39 of 273, top 15% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

Qwen3.8 27B Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1271 | #35 of 122, top 29% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |

### Multilingual

Qwen3.8 27B Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1430 | #60 of 297, top 21% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1504 | #43 of 285, top 16% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1465 | #45 of 223, top 21% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1438 | #59 of 231, top 26% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1384 | #74 of 211, top 36% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1393 | #63 of 213, top 30% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1415 | #88 of 283, top 32% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1448 | #54 of 226, top 24% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Qwen3.8 27B Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1439 | #50 of 298, top 17% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Qwen3.8 27B Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1450 | #53 of 291, top 19% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Qwen3.8 27B Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1441 | #60 of 297, top 21% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1384 | #101 of 295, top 35% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1671 | #36 of 115, top 32% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1441 | #73 of 295, top 25% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Qwen3.8 27B API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [cerebras](https://inference-docs.cerebras.ai/models/overview) | $0.99 | $1.49 | $0.99 | 2026-10-10 |
| [deepinfra](https://deepinfra.com/models) | $0.20 | $2.50 | $0.05 | 2026-10-10 |
| [groq](https://console.groq.com/docs/models) | $0.80 | $4 | — | 2026-10-10 |
| [openrouter](https://openrouter.ai/qwen/qwen3.8-27b) | $0.42 | $2.55 | $0.085 | 2026-10-10 |

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

## Compare Qwen3.8 27B

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

## 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
-   [Qwen3.5 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 Qwen3.8 27B?

Qwen3.8 27B by Alibaba (Qwen) ranks 68th 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 37th. API pricing starts at $0.99 per million input tokens and $1.49 per million output tokens, with a 262K-token context window.

### How much does Qwen3.8 27B cost?

Qwen3.8 27B costs $0.99 per million input tokens and $1.49 per million output tokens on cerebras, with cached input at $0.99.

### What is Qwen3.8 27B's context window?

Qwen3.8 27B accepts up to 262K tokens of input and can write up to 33K tokens in one response.

### Is Qwen3.8 27B open source?

Yes. Qwen3.8 27B's weights are downloadable from Hugging Face (Qwen/Qwen3.8-27B); check the license for commercial terms.

### What are Qwen3.8 27B's strengths and weaknesses?

Relative to other ranked models, Qwen3.8 27B places best in coding, writing & preference, reasoning and lowest in math, agentic & tool use, knowledge.

### What is Qwen3.8 27B best at?

Its best category is multimodal, where it ranks 37th on Noometry.

### Cite this page

Noometry. (2026). Qwen3.8 27B benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/qwen3-8-27b

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/qwen3-8-27b.md).