Alibaba shipped two flagships in the Qwen 3.7 line within two weeks: Qwen3.7-Max, the text-only reasoning model, and Qwen3.7-Plus, the multimodal version that adds vision and costs a fraction of the price. They share the same 1M-token context and the same 35-hour autonomous ceiling, so the choice isn’t obvious from the spec sheet alone.
This guide puts them side by side on benchmarks, price, speed, and the daily-driver decision. If you want the background on each model first, see our Qwen 3.7 Plus overview and the broader what Qwen 3.7 is guide. Whichever you pick, you’ll call it over an API and need to test the responses; that’s where Apidog comes in, covered at the end.
The short answer
Default to Plus. It matches Max on tool use, edges it on terminal tasks, adds image and video input, and costs about six times less. For most workloads that decision is already made on price alone.
Choose Max only when you’re optimizing purely for text. It keeps a small lead on pure-text leaderboards and runs a bit faster on text-only cold starts. If your work never touches a screenshot or a document image, that edge can matter. For everything else, Plus wins.
The core difference
Max is the pure-text flagship. It reasons, codes, and runs long agentic chains, all from text input. Plus takes the same backbone and adds eyes: it accepts images and video, and it grounds GUIs well enough to return exact click coordinates from a screenshot. Then it undercuts Max on price.
So the trade is narrow. You give up a slight text-quality and latency edge, and you gain vision plus a much cheaper bill.
Benchmarks
The numbers tell a consistent story. Plus trails Max slightly on pure text, ties on tool use, and pulls ahead the moment vision enters.
| Benchmark | Qwen 3.7 Plus | Qwen 3.7 Max |
|---|---|---|
| LM Arena (text) | #15 | #13 |
| LM Arena (coding) | #12 | #10 |
| Vision Arena | #16 | Not applicable |
| SWE-Bench Pro | ~60% | 60.6% |
| Terminal-Bench (2.0 Terminus) | 70.3 | 69.7 |
| ScreenSpot Pro (GUI grounding) | 79.0 | None |
| MCP-Atlas (tool use) | 76.4 | 76.4 |
Three things stand out.
SWE-Bench Pro is effectively a tie. Plus lands around 60% against Max’s 60.6%. On real software tasks, the vision parameters don’t cost Plus any meaningful coding ability. Our Qwen 3.7 vs GPT-5.5 vs Opus 4.7 comparison shows where that sits against the Western flagships.
Plus actually wins Terminal-Bench, 70.3 to 69.7. For shell-heavy agent work, the cheaper model is also the slightly stronger one.
GUI grounding is the real separator. ScreenSpot Pro 79.0 is frontier-tier, and Max can’t run it at all. If your agent has to look at a screen, only one of these models qualifies. As always, treat vendor benchmark numbers as direction, not gospel; the SWE-bench site explains what each suite measures.
Pricing
This is where the gap is wide.
| Qwen 3.7 Plus | Qwen 3.7 Max | |
|---|---|---|
| Input / 1M tokens | $0.40 | $2.50 |
| Output / 1M tokens | $1.60 | $7.50 |
| Cached input / 1M | $0.08 | $0.25 |
Plus is roughly six times cheaper on input and nearly five times cheaper on output. For high-volume or long-running agents, that ratio decides budgets. The cheaper model also reads images, which makes Max a hard sell unless you specifically need its text edge.
One caveat for Plus: images and video are tokenized and share the 1M context budget, so a screenshot-heavy or video workload spends more per call than the per-token rate suggests. Downscale images and sample video sparingly. Our notes on reducing agent token costs and the 2026 Chinese LLM price war cover the wider cost picture. The official rates live on the Model Studio pricing page.
Specs and speed
| Qwen 3.7 Plus | Qwen 3.7 Max | |
|---|---|---|
| Input modalities | Text, image, video | Text only |
| Context window | 1M (shared with vision) | 1M |
| Autonomous run ceiling | 35 hours | 35 hours |
| Text-only latency | Baseline | ~7–15% faster on cold paths |
| Weights | Proprietary, API-only | Proprietary, API-only |
The latency line is Max’s quiet advantage. On text-only cold starts it responds noticeably faster, which adds up in chat-style products where time-to-first-token is visible to users; independent analysis tracks the speed and intelligence trade-off in detail. Both models are closed-weight and run only through Alibaba Cloud Model Studio, so neither is an option if you need to self-host.
Which should you pick
Pick Qwen 3.7 Plus if:
- Your work touches images, screenshots, PDFs, or video.
- You’re building computer-use or GUI agents that read a screen.
- Cost matters, which on these numbers means almost always.
Pick Qwen 3.7 Max if:
- You’re tuning purely for text-only SWE-Bench Pro scores.
- You need the fastest text response in a latency-sensitive product.
- You never send visual input and want every point of text quality.
For most teams, Plus is the sensible default and Max is the specialist. The cost gap is large enough that you’d want a concrete reason to pay six times more for a text-only model.
To make that concrete, here’s how common workloads map:
| Workload | Pick | Why |
|---|---|---|
| Screenshot QA or visual regression agent | Plus | Needs GUI grounding; only Plus sees the screen |
| Invoice, receipt, or scanned-PDF extraction | Plus | Document images require vision input |
| High-volume text classification | Plus | Same text quality, a fraction of the cost |
| Low-latency customer-support chatbot | Max | Faster text-only cold starts matter to users |
| Long autonomous coding run | Either | They tie on SWE-Bench Pro, so let cost decide |
The pattern repeats: unless a workload is text-only and latency-sensitive, the cheaper multimodal model is the safer default.
Testing both with Apidog
Both models share the same OpenAI-compatible Model Studio endpoint, so swapping between them is a one-line model-ID change. That makes them easy to compare directly: send the same prompt to qwen3.7-plus and qwen3.7-max, line up the responses, and see whether the price gap is worth it for your task.
Apidog is built for that loop. Fire requests at both models, inspect the raw JSON side by side, store your Model Studio key per environment, and mock the endpoints so your app keeps building. For multimodal Plus requests, our Qwen 3.7 Plus API guide shows the image and video payload format, and the base Qwen 3.7 API guide covers the text path. When either model is chaining tool calls in an agent run, Apidog’s AI agent debugger shows the full sequence.
Download Apidog to test and compare both Qwen 3.7 models before you wire one into production.
FAQ
Is Qwen 3.7 Plus better than Max? For most workloads, yes, because it adds vision and costs far less while matching Max on coding and tool use. Max keeps a small lead on pure-text leaderboards and text-only latency.
How much cheaper is Plus? About six times cheaper on input ($0.40 vs $2.50 per million tokens) and nearly five times cheaper on output ($1.60 vs $7.50).
Do they share the same context window? Yes, both have a 1M-token window. On Plus, images and video consume tokens from that same budget.
Can Max process images? No. Max is text-only. If you need image or video input, you need Plus.
Are either of them open source? No. Both are proprietary and run only through Alibaba Cloud Model Studio. You can’t download or self-host the weights.
Which is faster? Max is roughly 7 to 15% faster on text-only cold paths. For mixed or vision work, Plus is the only option anyway.
The bottom line
Qwen 3.7 Max and Plus aren’t really competing for the same job. Max is the text purist with a thin speed-and-quality edge; Plus is the cheaper, multimodal generalist that wins almost everywhere price or vision matters. Start with Plus, and reach for Max only when a text-only workload justifies the premium. Either way, test the API in Apidog so what you ship behaves the way the benchmarks promise.
