LLM Reference

Qwen3.6-Plus

Released
2026-04-01
Last refreshed
2026-06-29
Status
Researched 107d ago
Open sourceCommercial use: permittedMultimodalCodingRAGAgentsLong contextVisionClassificationJSON / Tool use

Qwen3.6-Plus is worth evaluating for coding, rag, and agents when its provider route and context window match the workload.

Use it for

  • Teams evaluating coding, rag, and agents
  • Workloads that can use a 1m context window
  • Buyers comparing 3 tracked provider routes

Do not use it for

  • Workloads where another current model has stronger sourced task evidence
Specifications
Family
Qwen3.6
Released
2026-04-01
Context
1m
Architecture
Decoder Only
Specialization
code
Openness
Open source
License
Apache 2.0OSI-approvedCommercial use: permitted
Weights
Unknown
Code
Unknown
Created by

AI research institute of Alibaba Group.

Hangzhou, Zhejiang, China
Founded 2017
Website
Pricing
Output / 1M
$1.95
Input / 1M
$0.325

Cheapest of 3 routes · Alibaba Cloud PAI-EAS · cache read $0.156

About

Qwen3.6-Plus is Alibaba Cloud's GA Qwen3.6 flagship for long-context reasoning, coding, tool use, and multimodal workflows. DashScope lists it with a 1M-token context window, structured output support, and standard public token pricing.

Top use-case fit: coding, agents, and build tasks

Coding

Q/$ C

2 relevant benchmarks in the decision map.

RAG

Included by capability and metadata signals in the decision map.

Agents

Q/$ B

2 relevant benchmarks in the decision map.

Provider price ladder

Compare all 3

Compare API pricing across 3 providers for input and output tokens, batch, and cached reads when available.

ProviderInput / 1MOutput / 1MCacheRoute
Alibaba Cloud PAI-EAS$0.325$1.95read $0.156
Serverless
OpenRouter$0.330$1.95-
Serverless
Vercel AI Gateway$0.500$3.00read $0.100
Serverless

Capabilities

VisionMultimodalJSON / Tool useStructured Outputs

Benchmark peer barsfor Coding

Benchmark scores(9)

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.
BenchmarkScoreVersionEvaluationSource
MMMU Pro78.8LLM-Stats aggregatorObserved 2026-06-07Source
SWE-bench Verified78.8SWE-bench VerifiedObserved 2026-05-12Source
Massive Multi-discipline Multimodal Understanding86.0MMMUObserved 2026-05-12Source
AIME 202695.3AIME 2026 (accuracy)Observed 2026-06-07Source
Google-Proof Q&A90.4llm-stats shows 0 (accuracy%)Observed 2026-06-07Source
LiveCodeBench87.1LiveCodeBench v6 (pass@1)Observed 2026-06-07Source
MCP-Atlas74.1llm-stats shows 0 (accuracy%)Observed 2026-06-07Source
MMLU PRO88.5MMLU-Pro (accuracy)Observed 2026-06-07Source
τ-bench76.8TAU-bench rank 16 of 37 (pass_rate)Observed 2026-06-07Source

Migration checks

No linked migration route is available for this model yet.