LLM Reference

Qwen3.5 Models by Alibaba

AlibabaApache 2.0Open source
14 models2025–2026Up to 1m ctxFrom $0.07/1M input

Details

ResearcherAlibaba
LicenseApache 2.0OSI-approved
Commercial useCommercial use: permitted
Models14
Released2025–2026
Max context1m

Capabilities

Vision12 of 14 models
Multimodal12 of 14 models
Reasoning5 of 14 models
Function Calling5 of 14 models
Tool Use6 of 14 models
Structured Outputs5 of 14 models

About

Qwen3.5 is a family of 14 AI models by Alibaba, released between 2025 and 2026.

Current Variants

Use-when guidance is based on each model's tracked capabilities, context window, release date, and replacement status.

14 in view

Use when the workload needs agents, 262k context, and reasoning.

2026-06agents262k contextreasoning
Qwen3.5-9BCurrent

Use when the workload needs 262k context, 9B parameters, and tool use.

2026-03262k context9B parameterstool use
Qwen3.5-4BCurrent

Use when the workload needs 262k context, 4B parameters, and multimodal inputs.

2026-03262k context4B parametersmultimodal inputs
Qwen3.5-2BCurrent

Use when the workload needs 262k context, 2B parameters, and multimodal inputs.

2026-03262k context2B parametersmultimodal inputs

Use when the workload needs 262k context, 800M parameters, and multimodal inputs.

2026-03262k context800M parametersmultimodal inputs

Use when the workload needs 262k context, 27B parameters, and reasoning.

2026-02262k context27B parametersreasoning

Use when the workload needs 262k context, 35B parameters, and reasoning.

2026-02262k context35B parametersreasoning

Use when the workload needs 262k context, 122B parameters, and reasoning.

2026-02262k context122B parametersreasoning

Use when the workload needs 1m context and multimodal inputs.

2026-021m contextmultimodal inputs

Use when the workload needs 262k context, 397B parameters, and reasoning.

2026-02262k context397B parametersreasoning

Use when the workload needs 1m context and multimodal inputs.

2026-021m contextmultimodal inputs

Use when the workload needs 256k context, 4B parameters, and multimodal inputs.

2025-11256k context4B parametersmultimodal inputs

Use when the workload needs 256k context, 32B parameters, and multimodal inputs.

2025-11256k context32B parametersmultimodal inputs

Use when the workload needs 256k context, 32B parameters, and multimodal inputs.

2025-11256k context32B parametersmultimodal inputs

Release Timeline

4 release groups
2026-06
1 current
Qwen-AgentWorld-35B-A3B
agents262k contextreasoning
Current
2026-03
4 current
Qwen3.5-0.8B
262k context800M parametersmultimodal inputs
Current
Qwen3.5-2B
262k context2B parametersmultimodal inputs
Current
Qwen3.5-4B
262k context4B parametersmultimodal inputs
Current
Qwen3.5-9B
262k context9B parameterstool use
Current
2026-02
6 current
Qwen3.5-122B-A10B
262k context122B parametersreasoning
Current
Qwen3.5-27B
262k context27B parametersreasoning
Current
Qwen3.5-35B-A3B
262k context35B parametersreasoning
Current
Qwen3.5-397B-A17B
262k context397B parametersreasoning
Current
Qwen3.5-Flash
1m contextmultimodal inputs
Current
Qwen3.5-Plus
1m contextmultimodal inputs
Current
2025-11
3 current
Qwen3.5-32B
256k context32B parametersmultimodal inputs
Current
Qwen3.5-32B-Instruct
256k context32B parametersmultimodal inputs
Current
Qwen3.5-4B-Instruct
256k context4B parametersmultimodal inputs
Current

Specifications(14 models)

Qwen3.5 model specifications comparison
ModelReleasedContextParametersVisionMultimodalReasoningFn CallingTool UseStructured Outputs
Qwen-AgentWorld-35B-A3B2026-06262k35B total, 3B activeNoNoYesNoYesNo
Qwen3.5-9B2026-03262k9BYesYesNoYesYesYes
Qwen3.5-4B2026-03262k4BYesYesNoNoNoNo
Qwen3.5-2B2026-03262k2BYesYesNoNoNoNo
Qwen3.5-0.8B2026-03262k800MYesYesNoNoNoNo
Qwen3.5-27B2026-02262k27BYesYesYesYesYesYes
Qwen3.5-35B-A3B2026-02262k35BNoNoYesYesYesYes
Qwen3.5-122B-A10B2026-02262k122BYesYesYesYesYesYes
Qwen3.5-Flash2026-021mYesYesNoNoNoNo
Qwen3.5-397B-A17B2026-02262k397BYesYesYesYesYesYes
Qwen3.5-Plus2026-021mYesYesNoNoNoNo
Qwen3.5-4B-Instruct2025-11256k4BYesYesNoNoNoNo
Qwen3.5-32B2025-11256k32BYesYesNoNoNoNo
Qwen3.5-32B-Instruct2025-11256k32BYesYesNoNoNoNo

Pricing

Qwen3.5 model pricing by provider
ModelProviderInput / 1MOutput / 1MType
Qwen3.5-FlashOpenRouter$0.07$0.26Serverless
Qwen3.5-9BTogether AI$0.1$0.15Serverless
Qwen3.5-FlashAlibaba Cloud PAI-EAS$0.1$0.4Serverless
Qwen3.5-9BOpenRouter$0.1$0.15Serverless
Qwen3.5-9BAlibaba Cloud PAI-EAS$0.1$0.15Serverless
Qwen3.5-FlashVercel AI Gateway$0.1$0.4Serverless
Qwen3.5-35B-A3BOpenRouter$0.139$1Serverless
Qwen3.5-27BOpenRouter$0.195$1.56Serverless
Qwen3.5-27BAlibaba Cloud PAI-EAS$0.195$1.56Serverless
Qwen3.5-35B-A3BNovita AI$0.25$2Serverless
Qwen3.5-27BDeepInfra$0.26$2.6Serverless
Qwen3.5-122B-A10BOpenRouter$0.26$2.08Serverless
Qwen3.5-122B-A10BAlibaba Cloud PAI-EAS$0.26$2.08Serverless
Qwen3.5-PlusOpenRouter$0.3$1.8Serverless
Qwen3.5-27BNovita AI$0.3$2.4Serverless
Qwen3.5-397B-A17BOpenRouter$0.39$2.34Serverless
Qwen3.5-397B-A17BAlibaba Cloud PAI-EAS$0.39$2.34Serverless
Qwen3.5-PlusAlibaba Cloud PAI-EAS$0.4$2.4Serverless
Qwen3.5-PlusVercel AI Gateway$0.4$2.4Serverless
Qwen3.5-122B-A10BNovita AI$0.4$3.2Serverless
Qwen3.5-397B-A17BTogether AI$0.6$3.6Serverless
Qwen3.5-397B-A17BNovita AI$0.6$3.6Serverless

Popular comparisons in this family

Frequently Asked Questions

What is Qwen3.5 used for?
Qwen3.5 is used for agents, text, and vision and multimodal work. The family description and listed model capabilities point to those workloads as the best fit.
How does Qwen3.5 compare to Tongyi DeepResearch?
Qwen3.5 by Alibaba is strongest where you need agents, while Tongyi DeepResearch by Alibaba is the closest related family to check for adjacent model selection. Qwen3.5 has 14 listed variants and reaches up to 1m context, while Tongyi DeepResearch reaches up to 131k context, so compare the specs and pricing tables before choosing a production model.
Which Qwen3.5 model should I use?
For the lowest listed input price, start with Qwen3.5-Flash through OpenRouter at $0.07/1M input tokens. For the most capable/latest local choice, evaluate Qwen3.5-27B with 262k context and reasoning, tool use, function calling, structured outputs, and multimodal inputs.

Models(14)