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  4. Qwen3 Coder Next
Alibaba
Released February 3, 2026

Qwen3 Coder Next

Qwen3 Coder Next by Alibaba: open-weight MoE coding agent with 3B active of 80B total params, hybrid attention architecture, 256K context, and FIM support.

Visit AlibabaAnnouncement
Inputs
Text
Outputs
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Model Overview

Capabilities, design details, and architectural traits

Qwen3 Coder Next - Hybrid Attention MoE Built for Local Coding Agents

Qwen3 Coder Next is an open-weight MoE model built on top of Qwen3-Next-80B-A3B-Base, a base that introduces a hybrid attention architecture combining Gated DeltaNet (linear attention) layers and Gated Attention layers interleaved with Mixture-of-Experts blocks. It runs only in non-thinking mode - no <think> blocks are generated, making it a direct-output model designed for low-latency agent loops.

TraitDetail
ArchitectureHybrid layout: 12 cycles of 3x (Gated DeltaNet -> MoE) followed by 1x (Gated Attention -> MoE)
Active vs. total parameters3B activated out of 80B total, with 512 experts and 10 activated per token
Non-thinking mode onlyDoes not generate <think></think> blocks; enable_thinking=False is no longer required
Context length262,144 tokens natively
Fill-in-the-Middle (FIM)Supported for code insertion tasks across all Qwen3-Coder variants
Recovery from execution failuresDocumented training objective; the model is agentic-trained at scale on executable code tasks
LicenseApache 2.0 open-weight

Scaffold-Agnostic CLI/IDE Integration

Qwen3 Coder Next is explicitly designed to adapt to multiple scaffold templates, enabling direct drop-in use with CLI and IDE platforms including Claude Code, Qwen Code, Cline, Kilo, Trae, and others, without requiring per-platform fine-tuning.

Benchmark Performance

Independent evaluations · Artificial Analysis

21.3%
Intelligence
36.2%
Coding Index
8.9%
Agentic Index

Accuracy & Capability Details

GPQA - Graduate Science73.7%
Humanity's Last Exam10.1%
SciCode - Scientific Coding32.3%
Instruction Following35.2%
Long Context Reasoning42.3%
τ²-Bench - Agentic Tasks79.5%
TerminalBench - System Control18.2%
Specs
Context window
262Ktokens
Input pricing
$0.35per 1M tokens
Output pricing
$1.20per 1M tokens
Cached input
$0.35per 1M tokens

Prices in USD.

Compare Models Side-by-Side

Evaluate specifications, pricing, and independent benchmark indices

Model Details
General Info
ProviderAlibabaAnthropicAnthropic
Release DateFebruary 3, 2026July 24, 2026June 9, 2026
Knowledge Cutoff-May 2026-
Context & Limits
Context Window262K
1M
Best Context Window
1M
Best Context Window
Pricing (per 1M tokens)
Input Pricing
$0.35
Best Input Pricing
$5$10
Output Pricing
$1.20
Best Output Pricing
$25$50
Modalities
Inputs
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textimage
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Outputs
text
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Benchmarks (0-100)
Intelligence Index21.3
63.1
Best Intelligence Index
62.1
Coding Index36.2
78.0
Best Coding Index
76.5
Agentic Index8.9
59.2
Best Agentic Index
56.6
Qwen3 Coder Next
Claude Opus 5
Claude Fable 5

GPQA Benchmark

Graduate-level reasoning and expert Q&A evaluation.

74%
Qwen3 Coder Next
GPQA Benchmark
Score: 74%
Qwen3 Coder Next
93%
Claude Opus 5
GPQA Benchmark
Score: 93%
Claude Opus 5
93%
Claude Fable 5
GPQA Benchmark
Score: 93%
Claude Fable 5

Humanity's Last Exam

Extremely difficult logical reasoning and knowledge.

10%
Qwen3 Coder Next
Humanity's Last Exam
Score: 10%
Qwen3 Coder Next
55%
Claude Opus 5
Humanity's Last Exam
Score: 55%
Claude Opus 5
56%
Claude Fable 5
Humanity's Last Exam
Score: 56%
Claude Fable 5

Long Context Reasoning

Logical reasoning over long context windows.

42%
Qwen3 Coder Next
Long Context Reasoning
Score: 42%
Qwen3 Coder Next
76%
Claude Opus 5
Long Context Reasoning
Score: 76%
Claude Opus 5
77%
Claude Fable 5
Long Context Reasoning
Score: 77%
Claude Fable 5

Independent evaluation data provided by Artificial Analysis. To view the latest benchmarks and full details, visit their official site.

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