Model profile
Qwen3.5-35B-A3B
Evidence coverage
28 of 323 tracked benchmarks are published. 14 are verified and 14 provisional. 7 of 8 categories are measured.
- Published / tracked
- 28 / 323
- Verified
- 14
- Provisional
- 14
- Categories with evidence
- 7 / 8
Evidence by category
- Agentic5 benchmarksMixed evidence
- Coding3 benchmarksMixed evidence
- Reasoning3 benchmarksMixed evidence
- Knowledge9 benchmarksMixed evidence
- Math0 benchmarksNot measured
- Multilingual1 benchmarkVerified
- Multimodal5 benchmarksMixed evidence
- Inst. Following2 benchmarksMixed evidence
Qwen3.5-35B-A3B ranks #72 out of 200 models on the public leaderboard with an overall score of 56.97/100. It also ranks #46 out of 99 on the verified leaderboard. While not a frontier model, it offers specific advantages depending on the use case.
Qwen3.5-35B-A3B is a open weight model with a 262K token context window. It uses explicit chain-of-thought reasoning, which typically improves performance on math and complex reasoning tasks at the cost of higher latency and token usage.
This profile currently has 28 of 323 tracked benchmarks. BenchLM only exposes non-generated benchmark rows publicly, so missing categories stay blank until a sourced evaluation is available.
Its strongest category is Multilingual (#11), while its weakest is Agentic (#74). This performance profile makes it a well-rounded choice across a range of tasks.
Peer position
Exact provisional scores and ranks for the closest listed peers. A score can appear before a model clears the evidence threshold for a rank, so equal scores can have different rank states.
Range 56.59–57.25
- Gemini 2.5 ProGoogleCompare#7057.25Gemini 2.5 Pro is #70 with a score of 57.25.
- Qwen3.5 397BAlibabaCompare#7157.01Qwen3.5 397B is #71 with a score of 57.01.
- Qwen3.5-35B-A3BCurrent modelAlibaba#7256.97Qwen3.5-35B-A3B is #72 with a score of 56.97.
- GPT-5.3-Codex-SparkOpenAICompare#7356.91GPT-5.3-Codex-Spark is #73 with a score of 56.91.
- Kimi K2.6Moonshot AICompare#7456.79Kimi K2.6 is #74 with a score of 56.79.
- GPT-5.4 miniOpenAICompare#7556.77GPT-5.4 mini is #75 with a score of 56.77.
- Grok 4 Fast (Reasoning)xAICompare#7656.59Grok 4 Fast (Reasoning) is #76 with a score of 56.59.
Category percentile
More
Relative position among models eligible for each sourced category. A higher percentile means a stronger position within that category's ranked cohort; 100 is highest.
- Multilingual17%Eligible cohort rank #11 of 13Category score 21.1
- Inst. Following50%Eligible cohort rank #16 of 31Category score 84.5
- Knowledge67%Eligible cohort rank #18 of 52Category score 75.3
- Multimodal0%Eligible cohort rank #29 of 29Category score 0.0
- Coding49%Eligible cohort rank #63 of 122Category score 50.1
- Agentic38%Eligible cohort rank #74 of 119Category score 45.3
Category evidence
Scores and ranks appear only where this model has published benchmark evidence. Categories without displayable source records remain not measured.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #74 of 119Percentile 38thWeight 22%5 benchmarksMixed sources | 45.3 | #74 of 119 | 38th | 22% | 5 benchmarks | Mixed sources |
| CodingRank #63 of 122Percentile 49thWeight 20%3 benchmarksMixed sources | 50.1 | #63 of 122 | 49th | 20% | 3 benchmarks | Mixed sources |
| ReasoningRank Not rankedWeight 17%3 benchmarksMixed sources | 72.0 | Not ranked | Not available | 17% | 3 benchmarks | Mixed sources |
| KnowledgeRank #18 of 52Percentile 67thWeight 12%9 benchmarksMixed sources | 75.3 | #18 of 52 | 67th | 12% | 9 benchmarks | Mixed sources |
| MathWeight 5%0 benchmarksNot measured | Not measured | Not ranked | Not available | 5% | 0 benchmarks | Not measured |
| MultilingualRank #11 of 13Percentile 17thWeight 7%1 benchmarkVerified | 21.1 | #11 of 13 | 17th | 7% | 1 benchmark | Verified |
| MultimodalRank #29 of 29Percentile 0thWeight 12%5 benchmarksMixed sources | 0.0 | #29 of 29 | 0th | 12% | 5 benchmarks | Mixed sources |
| Inst. FollowingRank #16 of 31Percentile 50thWeight 5%2 benchmarksMixed sources | 84.5 | #16 of 31 | 50th | 5% | 2 benchmarks | Mixed sources |
Chatbot Arena performance
Scroll horizontally to inspect confidence intervals and vote counts.
| View | Elo | Confidence interval | Votes |
|---|---|---|---|
| Text Overall | 1396 | ±4.3 | 29,157 |
| Coding | 1435 | ±7.3 | 7,988 |
| Math | 1399 | ±14.1 | 1,758 |
| Instruction Following | 1388 | ±6.7 | 9,282 |
| Creative Writing | 1344 | ±9.3 | 4,456 |
| Multi-turn | 1395 | ±8.7 | 5,204 |
| Hard Prompts | 1413 | ±5.3 | 18,332 |
| Hard Prompts (English) | 1423 | ±6.9 | 8,988 |
| Longer Query | 1402 | ±6.5 | 11,063 |
Benchmark Details
Rows below have a displayable published verification record. Each source link and provenance note remains in the page HTML while its category is closed. Source-unverified manual rows and generated rows stay hidden.
Agentic5 benchmarks
τ²-Bench Tool-Agent-User Evaluation
Gert Labs Composite Game Benchmark
Coding3 benchmarks
Software Engineering Benchmark Verified
Artificial Analysis SciCode
Reasoning3 benchmarks
Artificial Analysis Long Context Reasoning
Critical Physics Tasks
Knowledge9 benchmarks
Massive Multitask Language Understanding Professional
SuperGPQA: Scaling LLM Evaluation Across 285 Graduate Disciplines
Graduate-Level Google-Proof Q&A
Artificial Analysis GPQA Diamond
Artificial Analysis Humanity's Last Exam
Artificial Analysis Omniscience Index
Artificial Analysis Omniscience Accuracy
Artificial Analysis Omniscience Hallucination Rate
Multilingual1 benchmark
Multimodal5 benchmarks
Massive Multi-discipline Multimodal Understanding
Multimodal Multi-disciplinary Video Understanding
Artificial Analysis MMMU-Pro
Inst. Following2 benchmarks
Instruction-Following Eval
Artificial Analysis IFBench
Frequently Asked Questions
How does Qwen3.5-35B-A3B perform overall in AI benchmarks?
Qwen3.5-35B-A3B currently ranks #72 out of 200 models on BenchLM's provisional leaderboard with an overall score of 56.97. It also ranks #46 out of 99 on the verified leaderboard. It is created by Alibaba. Its published context window is 262K.
Is Qwen3.5-35B-A3B good for knowledge and understanding?
Qwen3.5-35B-A3B ranks #18 out of 52 models in knowledge and understanding benchmarks with an average score of 75.3. There are stronger options in this category.
Is Qwen3.5-35B-A3B good for coding and programming?
Qwen3.5-35B-A3B ranks #63 out of 122 models in coding and programming benchmarks with an average score of 50.1. There are stronger options in this category.
Is Qwen3.5-35B-A3B good for reasoning and logic?
Qwen3.5-35B-A3B has visible benchmark coverage in reasoning and logic, but BenchLM does not currently assign it a global category rank there.
Is Qwen3.5-35B-A3B good for agentic tool use and computer tasks?
Qwen3.5-35B-A3B ranks #74 out of 119 models in agentic tool use and computer tasks benchmarks with an average score of 45.3. There are stronger options in this category.
Is Qwen3.5-35B-A3B good for multimodal and grounded tasks?
Qwen3.5-35B-A3B ranks #29 out of 29 models in multimodal and grounded tasks benchmarks with an average score of 0. There are stronger options in this category.
Is Qwen3.5-35B-A3B good for instruction following?
Qwen3.5-35B-A3B ranks #16 out of 31 models in instruction following benchmarks with an average score of 84.5. There are stronger options in this category.
Is Qwen3.5-35B-A3B good for multilingual tasks?
Qwen3.5-35B-A3B ranks #11 out of 13 models in multilingual tasks benchmarks with an average score of 21.1. There are stronger options in this category.
Is Qwen3.5-35B-A3B open source?
Yes, Qwen3.5-35B-A3B is an open weight model created by Alibaba, meaning it can be downloaded and run locally or fine-tuned for specific use cases.
Does Qwen3.5-35B-A3B have full benchmark coverage on BenchLM?
Not yet. Qwen3.5-35B-A3B currently has 28 published benchmark scores out of the 323 benchmarks BenchLM tracks. BenchLM only exposes non-generated public benchmark rows, so missing categories stay blank until a sourced evaluation is available.
What is the context window size of Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B has a published context window of 262K, which determines how much text it can process in a single interaction.
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