LLMqwen

Qwen: Qwen3.7 Max

Qwen3.7-Max is the flagship model in Alibaba's Qwen3.7 series. It supports text input and output and is designed for agent-centric workloads, with particular strengths in coding, office and productivity tasks,...

Anyone in the Space can @-mention Qwen: Qwen3.7 Max with the team's shared context — pooled credits, one chat, one memory.

All models

Starter is free forever — 1 Space, 100 credits/month, 1 MCP. No card.

Verdict

Qwen3.7 Max delivers a massive 1M token context window at $1.25/$3.75 per Mtok — roughly half the cost of GPT-4o for long-context work. It handles extended document analysis and multi-file codebases without the context-splitting gymnastics required by smaller windows. The trade-off: limited public benchmark data makes it harder to predict performance on nuanced reasoning or creative tasks relative to Claude or GPT-4 class models. Reach for this when you need to ingest entire repositories or lengthy transcripts and cost per token matters more than proven top-tier reasoning.

Best for

  • Analyzing full codebases in one pass
  • Processing 500+ page legal documents
  • Multi-document research synthesis
  • Cost-sensitive long-context summarization
  • Ingesting lengthy meeting transcripts

Strengths

The 1M token window lets you load entire repositories, multi-chapter documents, or dozens of files without chunking strategies. At $1.25 input per Mtok, it undercuts GPT-4o and Claude Sonnet 3.5 on cost for long-context tasks by roughly 50%. Qwen models historically perform well on multilingual tasks and code generation, making this a strong candidate for international teams working with large codebases or mixed-language corpora.

Trade-offs

Lack of public benchmark scores makes it difficult to gauge performance on complex reasoning, creative writing, or instruction-following relative to Claude Sonnet 4.5 or GPT-4o. Qwen models sometimes lag behind frontier models on nuanced multi-step logic and edge-case handling. The proprietary license limits transparency into training data and fine-tuning approaches, which matters for compliance-sensitive teams.

Specifications

Provider
qwen
Category
llm
Context length
1,000,000 tokens
Max output
65,536 tokens
Modalities
text
License
proprietary
Released
2026-05-21

Pricing

Input
$1.25/Mtok
Output
$3.75/Mtok
Model ID
qwen/qwen3.7-max

Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool — one plan, one balance for everyone.

Team cost calculator

Estimated monthly spend
$35.20
17.6M tokens / month
5 seats · 80 msgs/day

Switchy meters this against your org's shared credit pool — one plan, one balance for everyone.

Providers

ProviderContextInputOutputP50 latencyThroughput30d uptime
qwen1000k$1.25/Mtok$3.75/Mtok

Performance

Performance snapshots are collected daily. Check back after the next ingestion run.

Benchmarks

Public benchmark scores are not available yet for this model. Check back after the next ingestion run.

Works well with

Top MCPs

Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.

How Switchy teams use it

Not enough Spaces have used this model yet to share anonymised team stats. We wait for at least 50 distinct Spaces per week before publishing any aggregate.

Starter prompts

Codebase Architecture Summary

You have access to the entire codebase below. Produce a concise architecture summary: list the main modules, their responsibilities, key dependencies between them, and any design patterns you observe. Focus on what a new engineer needs to understand the system quickly.
Open in a Space →

Multi-Document Policy Comparison

I'm providing three policy documents in full. Compare them section by section and produce a table showing where they differ on key terms, coverage limits, and exclusions. Highlight any clauses unique to one document.
Open in a Space →

Meeting Transcript Action Items

Below is a full meeting transcript. Extract all action items, decisions made, and open questions. For each action item, note who is responsible and any mentioned deadline. Preserve context so I understand why each decision was reached.
Open in a Space →

Research Paper Synthesis

I've included five research papers below. Synthesize their findings into a cohesive summary: what do they agree on, where do they conflict, and what gaps remain unaddressed? Cite each paper by its title when referencing specific claims.
Open in a Space →

Long-Form Content Outline

Using the source material provided, create a detailed outline for a comprehensive report. Each section should have 3-5 subsections with brief descriptions of what content belongs there. Ensure logical flow and no redundancy across sections.
Open in a Space →
Data last verified 2 hours ago.Sources aggregated hourly to weekly. See docs/architecture/model-directory.md.