Qwen3.7 Max: pricing, context and performance

Overview

Qwen3.7 Max is a language model from Alibaba Cloud (Qwen), released on May 21, 2026. That makes it 5 months old as of October 22, 2026. It is a closed model: you access it through an API, from Alibaba Cloud (Qwen) or one of the 32 providers listed on models.dev. It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money.

We file it under frontier models (24 models in this category on the site).

Pricing and budget

Qwen3.7 Max costs $1.71 per million input tokens and $5.14 per million output tokens (median of 32 providers, checked on October 22, 2026).

To put it in perspective: reading 20,000 tokens (about thirty pages) and writing 2,000 costs roughly $0.044; a thousand of those tasks, $44.48.

That is 2.8 times cheaper than the median output price of frontier models ($14.5).

To estimate your own budget, use the agent cost calculator.

What do independent benchmarks say?

According to results published by Epoch AI (checked on October 22, 2026), Qwen3.7 Max has a capability index of 86.6 out of 100, computed on 4 benchmarks from our basket.

That puts it at #15 in our weekly model ranking.

• GPQA Diamond (PhD-level science questions): 90.9% — the best tracked model, Gemini 4 Argon, reaches 96.4%
• OTIS Mock AIME (competition mathematics problems): 95.6% — the best tracked model, GPT-5.5 Pro, reaches 100%
• FrontierMath (very hard research-level mathematics): 64.6% — the best tracked model, GPT-6 Astra, reaches 93.7%
• SWE-bench Verified (fixing real software bugs): 77.3% — the best tracked model, Claude Opus 4.7, reaches 83.5%

What it can do

Its context window reaches 1,000,000 tokens, roughly 1,500 pages of text at 500 words per page: enough to load a whole book or a mid-sized codebase. A single answer can run to 50,000 tokens.

It accepts as input: text.

It can call tools (functions, APIs), which makes it a possible foundation for an AI agent.

When to choose it

Given its category, Qwen3.7 Max is mainly aimed at hard tasks where quality matters more than cost: deep reasoning, complex code, long analyses.

Its SWE-bench Verified result (77.3%, above the median of tracked models) makes it a serious candidate for coding agents.

Its very large context window makes it worth considering for analysing large sets of documents in a single request.

Before adopting it, test it on your own examples: benchmarks measure standard tasks, not your job.

Comparable models

In the same category, the models closest to Qwen3.7 Max are:

• Gemini 3 Pro Preview (Google DeepMind, index 87)
• Grok 4.6 (xAI, index 86.1)
• GPT-5.4 (OpenAI, index 87.2)
• Gemini 3.7 Flash (Google DeepMind, index 85.9)