GPT-5.5 Pro vs Qwen3.7 Max
At a Glance
| Compare |  GPT-5.5 ProOpenAI |  Qwen3.7 MaxQwen |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #13 of 44$0.057 per LiveBench case |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $30.00Openai ↗ · Sep 3, 2026 | $1.475Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | $180.00Openai ↗ · Sep 3, 2026 | $4.425Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 1,050K | 1,000K |
| Model facts checked | Sep 3, 2026View model evidence → | Sep 3, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | GPT-5.5 Pro | Qwen3.7 Max |
|---|---|---|
| Developer | OpenAI | Qwen |
| Family | Gpt 5 5 | Qwen3 7 |
| Model | GPT-5.5 Pro | Qwen3.7 Max |
| Version | GPT-5.5 Pro | Qwen3.7 Max |
| Lifecycle | active | active |
| Released | 2026-04-23 | 2026-05-20 |
| Knowledge cutoff | 2025-12-01 | Unknown |
| Input modalities | Text, Image | Text, Image, Video |
| Output modalities | Text | Text |
| Context window | 1,050K | 1,000K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Openai (Standard), Openrouter (Standard) | Alibaba Cloud Model Studio (Standard), Deepinfra (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | agents, chat, generation, reasoning, structured_outputs, tools, vision |
GPT-5.5 Pro Capabilities
Qwen3.7 Max Capabilities
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Primary Evidence
Sources and Freshness
Questions
GPT-5.5 Pro vs Qwen3.7 Max FAQs
Is GPT-5.5 Pro or Qwen3.7 Max better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GPT-5.5 Pro and Qwen3.7 Max, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GPT-5.5 Pro or Qwen3.7 Max?+
GPT-5.5 Pro is $30.00 and Qwen3.7 Max is $1.475 per million tokens, so Qwen3.7 Max is cheaper on this metric. GPT-5.5 Pro is $180.00 and Qwen3.7 Max is $4.425 per million tokens, so Qwen3.7 Max is cheaper on this metric.
Which has a larger context window, GPT-5.5 Pro or Qwen3.7 Max?+
GPT-5.5 Pro has the larger sourced context window. GPT-5.5 Pro supports 1,050K and Qwen3.7 Max supports 1,000K.
Which performs better in benchmarks, GPT-5.5 Pro or Qwen3.7 Max?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can GPT-5.5 Pro or Qwen3.7 Max be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. GPT-5.5 Pro is not marked open weight; Qwen3.7 Max is not marked open weight.
Can GPT-5.5 Pro and Qwen3.7 Max understand images?+
GPT-5.5 Pro is documented with image input; Qwen3.7 Max is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, GPT-5.5 Pro or Qwen3.7 Max?+
GPT-5.5 Pro has the larger sourced maximum output: GPT-5.5 Pro supports 128K and Qwen3.7 Max supports 66K output tokens.
Do GPT-5.5 Pro and Qwen3.7 Max support reasoning and tool use?+
GPT-5.5 Pro: reasoning, tool calling, and image input. Qwen3.7 Max: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, GPT-5.5 Pro or Qwen3.7 Max?+
GPT-5.5 Pro has 2 sourced provider routes; Qwen3.7 Max has 4, so Qwen3.7 Max has broader tracked availability.
Which offers better value, GPT-5.5 Pro or Qwen3.7 Max?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.