LLM Reference

Qwen3.5-35B-A3B

Researched 44d ago

Last refreshed 2026-05-22. Next refresh: weekly.

RAGAgentsLong contextClassificationJSON / Tool use

Qwen3.5-35B-A3B is worth evaluating for rag, agents, and long context when its provider route and context window match the workload.

Decision context: RAG task fit, 2 tracked provider routes, and research from 2026-04-19.

Use it for

  • Teams evaluating rag, agents, and long context
  • Workloads that can use a 262k context window
  • Buyers comparing 2 tracked provider routes

Do not use it for

  • Vision or document-understanding workloads

Cheapest output

$1.00

OpenRouter per 1M tokens

Provider routes

2

Tracked API hosts

Quality / dollar

Unknown

No task benchmark coverage yet

Freshness

2026-04-19

Researched 44d ago

aging

Top use-case fit

RAG

Included by capability and metadata signals in the decision map.

Agents

Included by capability and metadata signals in the decision map.

Long context

Included by capability and metadata signals in the decision map.

Provider price ladder

Compare all 2
ProviderInput / 1MOutput / 1MRoute
OpenRouter$0.139$1.00
Serverless
Novita AI$0.250$2.00
Serverless

Benchmark peer barsfor RAG

No task-mapped benchmark peers are available for this model yet.

Migration checks

No linked migration route is available for this model yet.

About

Alibaba's Qwen3.5-35B-A3B is a Mixture-of-Experts model released February 24, 2026, with 35B total parameters and 3B active during inference. Part of the Qwen3.5 series with a 262K native context window (extendable to ~1M tokens). Optimized for high inference throughput (78+ tokens/second on NVIDIA hardware). Open-source under Apache 2.0.

Qwen3.5-35B-A3B is a model in the Qwen3.5 family. The structured metadata tracks a 262k-token context window, reasoning, function calling, tool use, and structured outputs. This page tracks provider routes through OpenRouter and Novita AI, with the cheapest tracked route listed at $0.139 input and $1 output per 1M tokens. Headline tracked benchmarks include Google-Proof Q&A 84.5 and SWE-rebench 53.7.

Capabilities

ReasoningFunction CallingTool UseStructured Outputs

Benchmark Scores(2)

Scores are benchmark-specific and are direction-aware: the same numeric gap can mean very different outcomes across suites. Use the leaderboard context and this model's provider route to decide whether the winning margin is meaningful for your workload.
BenchmarkScoreVersionSource
Google-Proof Q&A84.5diamondhttps://pricepertoken.com/leaderboards/benchmark/gpqa
SWE-rebench53.7pass@1 (best of 5 runs)https://swe-rebench.com/leaderboard

Rankings

Show all 20 popular comparisonssorted by 7-day search impressions

Specifications

FamilyQwen3.5
Released2026-02-24
Parameters35B
Context262k
ArchitectureMixture of Experts

Created by

AI research institute of Alibaba Group.

Hangzhou, Zhejiang, China
Founded 2017
Website