Alibaba (Qwen), open weights

# Qwen3-30B-A3B

> Qwen3-30B-A3B by Alibaba (Qwen), released April 2025. Ranked #179 of 354 with a Noometry Index of 38.9. API: $0.12 in / $0.50 out per M tokens. 41K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/qwen3-30b-a3b
- Last updated: 2026-10-10
- Title: Qwen3-30B-A3B Benchmarks, Price & Rank (October 2026)

Qwen3-30B-A3B by Alibaba (Qwen) ranks 179th of 354 ranked models on the Noometry Index as of October 2026, with a score of 38.9. Its strongest category is agentic & tool use, where it ranks 82nd. API pricing starts at $0.12 per million input tokens and $0.50 per million output tokens, with a 41K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #179 of 354
- **Index score:** 38.9
- **Evidence:** Confirmed 32 results
- **Provider:** [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba)
- **Released:** April 28, 2025
- **Weights:** Open weights
- **Reasoning:** Yes
- **Context window:** 41K
- **Max output:** 16K
- **Input price:** $0.12 / M
- **Output price:** $0.50 / M
- **Blended price:** $0.21 / M
- **Output speed:** 42 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #46 of 219
- **Knowledge cutoff:** Unknown
- **Input:** text
- **Hugging Face:** [Qwen/Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B)

## Category scores

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

Qwen3-30B-A3B category scores

1.  Coding 37.5
2.  Agentic & Tool Use 29.8
3.  Reasoning 22.2
4.  Math 37.4
5.  Knowledge 41.8
6.  Multilingual 49.5
7.  Instruction Following 72.0
8.  Long Context 31.0
9.  Writing & Preference 55.6
10.  020406080

Qwen3-30B-A3B category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 37.5 | #194 | 3 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 29.8 | #82 | 1 |
| [Reasoning](https://noometry.com/best/reasoning) | 22.2 | #204 | 6 |
| [Math](https://noometry.com/best/math) | 37.4 | #157 | 3 |
| [Knowledge](https://noometry.com/best/knowledge) | 41.8 | #105 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 49.5 | #132 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 72.0 | #142 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 31.0 | #283 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 55.6 | #143 | 4 |

## Strengths and weaknesses

Categories where Qwen3-30B-A3B places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Qwen3-30B-A3B: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Knowledge](https://noometry.com/best/knowledge) | 41.8 | +4.5 | #105 of 314, top 34% |
| [Multilingual](https://noometry.com/best/multilingual) | 49.5 | +2.1 | #132 of 297, top 45% |
| [Writing & Preference](https://noometry.com/best/writing) | 55.6 | +1.9 | #143 of 312, top 46% |

### Weakest categories

Qwen3-30B-A3B: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 31.0 | −9.9 | #283 of 296, top 96% |
| [Reasoning](https://noometry.com/best/reasoning) | 22.2 | −1.4 | #204 of 350, top 59% |
| [Coding](https://noometry.com/best/coding) | 37.5 | −1.2 | #194 of 340, top 58% |

## Closest competitors

The models ranked just above and below Qwen3-30B-A3B. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Qwen3-30B-A3B
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Mercury 2](https://noometry.com/models/mercury-2) | #175 | 39.1 | $0.38 | — | [Compare](https://noometry.com/compare/mercury-2-vs-qwen3-30b-a3b) |
| [Mistral Large 3](https://noometry.com/models/mistral-large-3) | #176 | 39.1 | $0.38 | 7 | [Compare](https://noometry.com/compare/mistral-large-3-vs-qwen3-30b-a3b) |
| [GLM-4.5-Air](https://noometry.com/models/glm-4-5-air) | #177 | 38.9 | $0.43 | 160 | [Compare](https://noometry.com/compare/glm-4-5-air-vs-qwen3-30b-a3b) |
| [MiniMax-M2.1](https://noometry.com/models/minimax-m2-1) | #178 | 38.9 | $0.52 | — | [Compare](https://noometry.com/compare/minimax-m2-1-vs-qwen3-30b-a3b) |
| [GLM-4.7-Flash](https://noometry.com/models/glm-4-7-flash) | #180 | 38.8 | $0.15 | — | [Compare](https://noometry.com/compare/glm-4-7-flash-vs-qwen3-30b-a3b) |
| [Qwen2.5 Plus 1127](https://noometry.com/models/qwen2-5-plus) | #181 | 38.8 | — | — | [Compare](https://noometry.com/compare/qwen2-5-plus-vs-qwen3-30b-a3b) |
| [Qwen3.6 Flash](https://noometry.com/models/qwen3-6-flash) | #182 | 38.8 | $0.42 | — | [Compare](https://noometry.com/compare/qwen3-30b-a3b-vs-qwen3-6-flash) |
| [Olmo 3 32b Think](https://noometry.com/models/olmo-3-32b-think) | #183 | 38.7 | — | — | [Compare](https://noometry.com/compare/olmo-3-32b-think-vs-qwen3-30b-a3b) |

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

Qwen3-30B-A3B Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SciCode](https://noometry.com/benchmarks/scicode) | 33.3% | #102 of 121, top 85% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 29.8% | #97 of 119, top 82% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1337 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1416 | #120 of 294, top 41% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Agentic & Tool Use

Qwen3-30B-A3B Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 41.4% | #22 of 49, top 45% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |

### Reasoning

Qwen3-30B-A3B Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 54.9% | #52 of 99, top 53% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 0.3% | #97 of 134, top 73% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 8% | #85 of 129, top 66% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 4% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 2% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 1% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1314 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1398 | #130 of 297, top 44% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 67.2% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 60.3% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 69.3% | #93 of 151, top 62% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 22.4% | #89 of 125, top 72% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 19.5% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 15.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 139.63 | #110 of 213, top 52% |  | [Epoch AI](https://epoch.ai/eci) | 2025-07-30 |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 137.42 |  |  | [Epoch AI](https://epoch.ai/eci) | 2025-07-29 |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 136.18 |  |  | [Epoch AI](https://epoch.ai/eci) | 2025-04-29 |

### Math

Qwen3-30B-A3B Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [MathArena Final-Answer Competitions](https://noometry.com/benchmarks/matharena) | 47.8% | #28 of 29, top 97% |  | [MathArena](https://matharena.ai/) |  |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 62.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 70.3% | #92 of 173, top 54% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 62.2% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 25.6% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1355 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1394 | #133 of 285, top 47% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

Qwen3-30B-A3B Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 55.6% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 61.7% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 70.1% | #98 of 186, top 53% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 50.4% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 |
| [Confabulations](https://noometry.com/benchmarks/confabulations) (lower is better) | 12.3% | #6 of 51, top 12% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/confabulations) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1396 | #127 of 273, top 47% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1314 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multilingual

Qwen3-30B-A3B Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1372 | #132 of 297, top 45% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1295 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1347 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1433 | #116 of 285, top 41% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1352 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1418 | #102 of 223, top 46% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1380 | #111 of 231, top 49% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1307 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1254 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1337 | #107 of 211, top 51% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1331 | #113 of 213, top 54% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1261 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1291 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1370 | #136 of 283, top 49% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1317 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1404 | #107 of 226, top 48% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Qwen3-30B-A3B Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1363 | #137 of 298, top 46% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1283 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Qwen3-30B-A3B Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 40.6% | #43 of 47, top 92% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1379 | #133 of 291, top 46% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1312 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Qwen3-30B-A3B Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1384 | #132 of 297, top 45% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1317 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1271 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1317 | #156 of 295, top 53% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Short-Story Creative Writing](https://noometry.com/benchmarks/lech-mazur-writing) | 75.3% | #24 of 39, top 62% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1307 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1378 | #136 of 295, top 47% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Qwen3-30B-A3B API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [deepinfra](https://deepinfra.com/models) | $0.12 | $0.50 | — | 2026-10-10 |
| [openrouter](https://openrouter.ai/qwen/qwen3-30b-a3b) | $0.12 | $0.50 | — | 2026-10-10 |

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

## Compare Qwen3-30B-A3B

-   [Qwen3-30B-A3B vs Qwen3 235B-A22B](https://noometry.com/compare/qwen3-235b-a22b-vs-qwen3-30b-a3b)
-   [Qwen3-30B-A3B vs MiniMax-M2.1](https://noometry.com/compare/minimax-m2-1-vs-qwen3-30b-a3b)
-   [Qwen3-30B-A3B vs GLM-4.7-Flash](https://noometry.com/compare/glm-4-7-flash-vs-qwen3-30b-a3b)
-   [Qwen3-30B-A3B vs GLM-4.5-Air](https://noometry.com/compare/glm-4-5-air-vs-qwen3-30b-a3b)
-   [Qwen3-30B-A3B vs Qwen2.5 Plus 1127](https://noometry.com/compare/qwen2-5-plus-vs-qwen3-30b-a3b)
-   [Qwen3-30B-A3B vs Mistral Large 3](https://noometry.com/compare/mistral-large-3-vs-qwen3-30b-a3b)
-   [Qwen3-30B-A3B vs Qwen3.6 Flash](https://noometry.com/compare/qwen3-30b-a3b-vs-qwen3-6-flash)
-   [Qwen3-30B-A3B vs GPT-6 Astra](https://noometry.com/compare/gpt-6-astra-vs-qwen3-30b-a3b)
-   [Qwen3-30B-A3B vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-qwen3-30b-a3b)
-   [Qwen3-30B-A3B vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-qwen3-30b-a3b)
-   [Qwen3-30B-A3B vs Kimi K3](https://noometry.com/compare/kimi-k3-vs-qwen3-30b-a3b)
-   [Qwen3-30B-A3B vs Grok 4.6](https://noometry.com/compare/grok-4-6-vs-qwen3-30b-a3b)
-   [Qwen3-30B-A3B vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-qwen3-30b-a3b)
-   [Qwen3-30B-A3B vs Muse Spark 1.3](https://noometry.com/compare/muse-spark-1-3-vs-qwen3-30b-a3b)

## 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 Qwen3-30B-A3B?

Qwen3-30B-A3B by Alibaba (Qwen) ranks 179th of 354 ranked models on the Noometry Index as of October 2026, with a score of 38.9. Its strongest category is agentic & tool use, where it ranks 82nd. API pricing starts at $0.12 per million input tokens and $0.50 per million output tokens, with a 41K-token context window.

### How much does Qwen3-30B-A3B cost?

Qwen3-30B-A3B costs $0.12 per million input tokens and $0.50 per million output tokens on deepinfra.

### What is Qwen3-30B-A3B's context window?

Qwen3-30B-A3B accepts up to 41K tokens of input and can write up to 16K tokens in one response.

### Is Qwen3-30B-A3B open source?

Yes. Qwen3-30B-A3B's weights are downloadable from Hugging Face (Qwen/Qwen3-30B-A3B); check the license for commercial terms.

### How fast is Qwen3-30B-A3B?

Qwen3-30B-A3B generated about 42 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are Qwen3-30B-A3B's strengths and weaknesses?

Relative to other ranked models, Qwen3-30B-A3B places best in knowledge, multilingual, writing & preference and lowest in long context, reasoning, coding.

### What is Qwen3-30B-A3B best at?

Its best category is agentic & tool use, where it ranks 82nd on Noometry.

### Cite this page

Noometry. (2026). Qwen3-30B-A3B benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/qwen3-30b-a3b

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/qwen3-30b-a3b.md).