---
title: "Qwen: Qwen3.6 Plus — price, capability and what beats it · Undominated.ai"
canonical: https://undominated.ai/models/qwen__qwen3.6-plus/
description: "Qwen: Qwen3.6 Plus: $0.325/M in, $1.95/M out. Independent capability scores, context-tier pricing, and the models that are both better and cheaper."
---

# Qwen: Qwen3.6 Plus — price, capability and what beats it · Undominated.ai

> Qwen: Qwen3.6 Plus: $0.325/M in, $1.95/M out. Independent capability scores, context-tier pricing, and the models that are both better and cheaper.

[Leaderboard](/) / Qwen

# Qwen3.6 Plus

Qwen · released 2026-04-02

 $0.731 per million tokens, balanced Balanced Summarise Chat Code gen Agentic

## MiniMax M3 is both better and cheaper.

It scores **+4.9** higher and costs **28% less** ($0.525/M against $0.731/M) on this workload — and it does everything this model does.

[DeepSeek V4 Flash 0731](/models/deepseek__deepseek-v4-flash-0731) is cheaper still (86% less) but drops no image, video input.

### Every price dimension

| Input | $0.325 /M |
| --- | --- |
| Output | $1.95 /M |
| Cache write | $0.406 /M |

**Past 256,000 tokens the price changes.** Input goes to $1.30/M (4×) and output to $3.90/M. The headline rate does not apply to a long-context workload.

### Independent scores

| Intelligence | 40.5 |
| --- | --- |
| Coding | 54.5 |
| Agentic | 29 |
| LMArena Elo | 1436.8 default |

Sources are listed separately rather than averaged. Across the 62 models both have scored they correlate at r = 0.806 — close agreement overall, but four models rank very differently between them, and a blended score would hide exactly those.

#### Best rankings by task

 - dataviz #31 of 142 1251
- svg #31 of 98 1202
- codecategories #33 of 145 1260

### Capability

| Context window | 1M |
| --- | --- |
| Max output | 66K |
| Input modes | text, image, video |
| Tool use | yes |
| Reasoning | optional |

### Provenance

| Price source | openrouter.ai |
| --- | --- |
| Fetched | 2026-08-24 |
| Quality data | verified |

Qwen 3.6 Plus builds on a hybrid architecture that combines efficient linear attention with sparse mixture-of-experts routing, enabling strong scalability and high-performance inference. Compared to the 3.5 series, it delivers...