LLMqwen

Qwen: Qwen3.7 Plus

Qwen3.7-Plus is a cost-effective model in Alibaba's Qwen3.7 series. It supports text and image input with text output, building on the series' text capabilities with a comprehensive upgrade to its...

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

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

Verdict

Qwen3.7 Plus delivers a massive 1M token context window at $0.40 input per Mtok — roughly half the cost of comparable long-context models. It handles text and image inputs, making it viable for multimodal document analysis where cost matters more than bleeding-edge reasoning. The trade-off: without public benchmarks, you're flying blind on accuracy relative to Claude or GPT-4o. Reach for this when you need to process entire codebases or long PDFs on a tight budget and can validate outputs yourself.

Best for

Processing entire codebases under budget
Long PDF analysis with cost constraints
Multimodal document review at scale
High-volume summarization tasks

Strengths

The 1M token context window puts full novels, large codebases, or multi-hundred-page documents in scope without chunking. At $0.40 input per Mtok, it undercuts most long-context competitors by 50% or more. Multimodal support means you can feed screenshots, diagrams, and text in the same prompt — useful for technical documentation or financial reports with embedded charts.

Trade-offs

No public benchmarks means you can't compare reasoning quality, code generation accuracy, or instruction-following against Claude Sonnet, GPT-4o, or Gemini 1.5 Pro. Qwen models historically trail Western frontier labs on complex reasoning and nuanced instruction adherence. Output pricing at $1.60 per Mtok is competitive but not a standout. If accuracy is non-negotiable, you'll need to run your own evals before committing production workloads.

Specifications

Provider: qwen
Category: llm
Context length: 1,000,000 tokens
Max output: 65,536 tokens
Modalities: text, image
License: proprietary
Released: 2026-06-03

Pricing

Input: $0.40/Mtok
Output: $1.60/Mtok
Model ID: qwen/qwen3.7-plus

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

Seats5 peopleMessages / seat / day80Avg turn size2 ktokOutput share30 %

Estimated monthly spend

$13.38

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

Provider	Context	Input	Output	P50 latency	Throughput	30d uptime
qwen	1000k	$0.40/Mtok	$1.60/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

Summarize Large Codebase

Review this codebase and produce a structured summary: list the main architectural patterns, identify all external dependencies, flag potential technical debt, and note any security concerns. Organize by module.

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Multi-Document Synthesis

I'm providing three research papers. Synthesize their findings into a single executive summary, highlighting areas of consensus, contradictions, and gaps in the literature.

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Screenshot-Based Debugging

Here's a screenshot of an error message and the relevant code snippet. Explain what's causing the error, suggest a fix, and note any related issues in the code.

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Contract Review at Scale

Review this contract and extract: all payment terms, termination clauses, liability caps, and any unusual or high-risk provisions. Present findings in a table.

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Technical Documentation Audit

Audit this technical documentation for completeness and accuracy. Flag any missing setup steps, outdated API references, broken examples, or unclear diagrams.

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