LLM Reference

Qwen3-Coder-Next

qwen3-coder-next

Researched 17d ago

Last refreshed 2026-05-11. Next refresh: weekly.

CodingRAGAgentsLong contextClassificationJSON / Tool use

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

Decision context: Coding task fit, 2 tracked provider routes, and research from 2026-05-04.

Use it for

  • Teams evaluating coding, rag, and agents
  • Workloads that can use a 256K context window
  • Buyers comparing 2 tracked provider routes

Do not use it for

  • Vision or document-understanding workloads

Cheapest output

$0.800

OpenRouter per 1M tokens

Provider routes

2

Tracked API hosts

Quality / dollar

Unknown

No task benchmark coverage yet

Freshness

2026-05-04

Researched 17d ago

fresh

Top use-case fit

Coding

Included by capability and metadata signals in the decision map.

RAG

Included by capability and metadata signals in the decision map.

Agents

Included by capability and metadata signals in the decision map.

Provider price ladder

Compare all 2
ProviderInput / 1MOutput / 1MRoute
OpenRouter$0.120$0.800
Serverless
AWS Bedrock$0.500$1.20
Serverless

Benchmark peer barsfor Coding

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

Migration checks

No linked migration route is available for this model yet.

About

Qwen3-Coder-Next is an ultra-sparse Mixture-of-Experts coding agent model from Alibaba's Qwen team, released February 3, 2026 under Apache 2.0. It has 80B total parameters with 3B active at inference, delivering substantially higher throughput than comparable dense models. It supports a native 256K context window, function calling, structured outputs, Claude Code, Qwen Code, Cline, Kilo, and other scaffold templates. Benchmarks reported in the DAT-3724 datapack include SWE-Bench Pro 44.3%, SWE-Bench Resolved 70.6%, and TerminalBench 2 36.2%.

Qwen3-Coder-Next has a 250K-token context window.

Qwen3-Coder-Next input tokens at $0.12/1M, output at $0.8/1M.

Capabilities

ReasoningFunction CallingTool UseStructured OutputsCode Execution

Rankings

Specifications

Released2026-02-03
Parameters80B total, 3B active
Context256K
Architecturemoe
Specializationcode
LicenseApache 2.0
Trainingpretrained

Created by

AI research institute of Alibaba Group.

Hangzhou, Zhejiang, China
Founded 2017
Website