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 Project 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 Project, 100 credits/month, 1 MCP. No card.

Verdict

Qwen3.7 Plus delivers a massive 1M token context window at $0.32/Mtok input — roughly 10× cheaper than GPT-4o for long-document work. It handles text and image inputs, making it viable for multimodal analysis at scale. The trade-off: no public benchmarks yet, so quality on complex reasoning or code generation remains unproven against Western models. Reach for this when context length and cost matter more than battle-tested performance on nuanced tasks.

Best for

Long-document analysis under budget constraints
High-volume multimodal batch processing
Extracting structured data from large PDFs
Cost-sensitive summarization of transcripts or logs
Vision tasks on invoice or receipt images

Strengths

The 1M token window lets you process entire codebases, legal documents, or multi-hour transcripts in a single call. At $0.32/Mtok input, it undercuts most frontier models by an order of magnitude, making it economical for high-throughput pipelines. Multimodal support means you can mix text and images without switching models, useful for document understanding workflows where screenshots or scanned pages appear alongside prose.

Trade-offs

Absence of public benchmarks makes it hard to gauge performance on math, code, or adversarial prompts relative to Claude or GPT-4o. Qwen models historically trail Western counterparts on English-language nuance and creative writing, though they excel at Chinese and structured extraction. Output pricing at $1.28/Mtok is 4× the input rate, so verbose responses erode the cost advantage. Proprietary license limits transparency into training data and safety mitigations.

Specifications

Provider: qwen
Category: llm
Context length: 1,000,000 tokens
Max output: 131,072 tokens
Modalities: text, image
License: proprietary
Released: 2026-06-03

Pricing

Input: $0.32/Mtok
Output: $1.28/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

$10.70

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	$0.32/Mtok	$1.28/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 Projects have used this model yet to share anonymised team stats. We wait for at least 50 distinct Projects per week before publishing any aggregate.

Starter prompts

Extract Invoice Line Items

You are given an invoice image. Extract all line items into a JSON array with fields: description, quantity, unit_price, total. Return only valid JSON, no commentary.

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Summarize 500-Page Deposition

Summarize this deposition transcript in 300 words. Identify the three most-contested factual claims and note which witnesses supported or contradicted each. Use bullet points for clarity.

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Codebase Dependency Audit

Review this codebase and list every external dependency. For each, note the import location and whether the package appears unmaintained (no updates in 2+ years). Output as a markdown table.

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Multi-Document Contradiction Check

These documents describe the same project. Identify any contradictory statements about timelines, budgets, or deliverables. Quote the conflicting passages and cite the document name for each.

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Batch Receipt Categorization

Classify this receipt image: return a JSON object with fields category (meals, travel, supplies, or other), vendor_name, date, and total_amount. If any field is unclear, set it to null.

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