GPT-5.5 vs Qwen3.7-Max
GPT-5.5 and Qwen3.7-Max are frontier closed-weight agentic models with 1M-token context windows. GPT-5.5 brings stronger terminal-agent results and vision input; Qwen3.7-Max brings lower tracked token prices and a small SWE-bench Pro edge for repository-scale coding.
Pick GPT-5.5 for terminal-heavy agents, tool orchestration, and multimodal workflows: it leads Terminal-Bench 2.0 at 82.7% versus 69.7% and supports image input. Pick Qwen3.7-Max when repository-scale coding and token cost matter more: it leads SWE-bench Pro at 60.6% versus 58.6%, and OpenRouter currently lists a 50% promotional rate of $1.25/M input and $3.75/M output. Treat Qwen3.7-Max pricing as freshness-sensitive because Alibaba's official pricing page did not list the 3.7 series at integration time.
Decision scorecard
Local evidence first| Signal | GPT-5.5 | Qwen3.7-Max |
|---|---|---|
| Best for | reasoning-heavy apps, multimodal apps, and tool-calling agents | reasoning-heavy apps, tool-calling agents, and long-context analysis |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 1.1M | 1M |
| Cheapest output | $30/1M tokens | $3.75/1M tokens |
| Provider routes | 3 tracked | 4 tracked |
| Shared benchmarks | Google-Proof Q&A leader | 4 rows |
Decision tradeoffs
- GPT-5.5 leads the largest shared benchmark signal on Google-Proof Q&A by 1.2 points.
- GPT-5.5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.5 uniquely exposes Vision, Multimodal, and Structured outputs in local model data.
- Local decision data tags GPT-5.5 for Coding, RAG, and Agents.
- Qwen3.7-Max leads the largest shared benchmark signal on SWE-bench Pro by 2 points.
- Qwen3.7-Max has the lower cheapest tracked output price at $3.75/1M tokens.
- Qwen3.7-Max has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Qwen3.7-Max for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GPT-5.5
$11,500
Cheapest tracked route/tier: OpenAI API 0-272K input tokens
Qwen3.7-Max
$1,938
Cheapest tracked route/tier: Novita AI
Estimated monthly gap: $9,563. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Vercel AI Gateway and OpenRouter; start route-level A/B tests there.
- Qwen3.7-Max is $26.25/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Multimodal, and Structured outputs before moving production traffic.
- Provider overlap exists on OpenRouter and Vercel AI Gateway; start route-level A/B tests there.
- GPT-5.5 is $26.25/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GPT-5.5 adds Vision, Multimodal, and Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-23 | 2026-05-20 |
| Context window | 1.1M | 1M |
| Parameters | — | — |
| Architecture | decoder only | decoder only |
| License | Proprietary | Proprietary |
| Knowledge cutoff | 2025-12 | - |
Pricing and availability
| Pricing attribute | GPT-5.5 | Qwen3.7-Max |
|---|---|---|
| Input price |
| $1.25/1M tokens |
| Output price |
| $3.75/1M tokens |
| Providers |
Capabilities
| Capability | GPT-5.5 | Qwen3.7-Max |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | Yes | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | No |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | GPT-5.5 | Qwen3.7-Max |
|---|---|---|
| Google-Proof Q&A | 93.6 | 92.4 |
| Chatbot Arena | 1488.0 | 1475.0 |
| Terminal-Bench 2.0 | 82.7 | 69.7 |
| SWE-bench Pro | 58.6 | 60.6 |
Deep dive
The clearest agentic-coding split is Terminal-Bench 2.0. GPT-5.5 scores 82.7%, while Qwen3.7-Max scores 69.7%, which makes GPT-5.5 the stronger first pick for terminal use, multi-step debugging, shell workflows, and tool-chaining systems.
The repository-repair signal cuts the other way. Qwen3.7-Max scores 60.6% on SWE-bench Pro versus GPT-5.5 at 58.6%, so teams focused on multi-file patch generation should not dismiss it just because GPT-5.5 is stronger on terminal-agent orchestration.
Vision is a hard capability boundary. GPT-5.5 is tracked with image input support; Qwen3.7-Max is text-only in the sourced records. Screenshot analysis, document images, visual QA, and diagram-heavy workflows should start with GPT-5.5 unless another vision model handles that stage.
The current cost view favors Qwen3.7-Max on hosted routes, but it needs caveats. OpenRouter lists Qwen3.7-Max at a 50% promotional price, and the handoff notes that Alibaba's official pricing page had not published 3.7-series pricing. Artificial Analysis also flagged unusually verbose Qwen3.7-Max reasoning outputs, which can narrow the real-world savings on output-heavy workloads.
For production selection, read the benchmark table by workload shape. GPT-5.5 is the safer default for agentic terminal pipelines and multimodal product work. Qwen3.7-Max is the cheaper, credible alternative for text-only software engineering when you can validate provider pricing and output-token volume with your own prompts.
FAQ
Which is better for agentic coding, GPT-5.5 or Qwen3.7-Max?
GPT-5.5 is the stronger pick for terminal-based agentic coding because it scores 82.7% on Terminal-Bench 2.0 versus 69.7% for Qwen3.7-Max. Qwen3.7-Max is still competitive for repository-scale code repair, where it scores 60.6% on SWE-bench Pro versus GPT-5.5 at 58.6%.
Does Qwen3.7-Max support vision or image input?
No. Qwen3.7-Max is tracked as text-only in the sourced records. GPT-5.5 supports image input, so document-image, screenshot, diagram, and visual QA pipelines should start with GPT-5.5 or pair Qwen3.7-Max with a separate vision model.
Which model is cheaper to run?
Qwen3.7-Max is cheaper on the currently tracked hosted pricing. OpenRouter lists it at $1.25/M input and $3.75/M output during a 50% promotion, while GPT-5.5 lists $5/M input and $30/M output on standard tracked routes. Recheck Qwen3.7-Max pricing before launch estimates because the discount and Alibaba direct pricing are freshness-sensitive.
Are GPT-5.5 and Qwen3.7-Max both 1M-token models?
Yes. GPT-5.5 is tracked with a 1,050,000-token context window, and Qwen3.7-Max is tracked with a 1,000,000-token context window. GPT-5.5 has the larger max output window at 128K tokens versus 65,536 for Qwen3.7-Max.
Is Qwen3.7-Max open source?
No. Qwen3.7-Max is tracked as a closed-weight proprietary Alibaba model. It is available through hosted providers such as Alibaba Cloud, OpenRouter, Vercel AI Gateway, and Novita, but the sourced records do not show released open weights.
Which model should I test first?
Start with GPT-5.5 if your workload includes terminal agents, tool orchestration, image inputs, or broad production defaults. Start with Qwen3.7-Max if the workload is text-only, code-heavy, and cost-constrained, then measure actual output-token volume because its reasoning mode can be verbose.
Continue comparing
Last reviewed: 2026-05-27. Data sourced from public model cards and provider documentation.