PaddleOCR VL 1.5 vs Qwen3.7 Max
At a Glance
| Compare |  PaddleOCR VL 1.5Baidu |  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 | Not reported | $1.475Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $4.425Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 131K | 1,000K |
| Model facts checked | Aug 28, 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 | PaddleOCR-VL-1.5 | Qwen3.7 Max |
|---|---|---|
| Developer | Baidu | Qwen |
| Family | Paddleocr VL 1 5 | Qwen3 7 |
| Model | PaddleOCR-VL-1.5 | Qwen3.7 Max |
| Version | PaddleOCR-VL-1.5 | Qwen3.7 Max |
| Lifecycle | active | active |
| Released | 2026-01-29 | 2026-05-20 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image, Video |
| Output modalities | Text | Text |
| Context window | 131K | 1,000K |
| Total parameters | 958.6M | Unknown |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | Unknown | Yes |
| Self-hostable | Yes | No |
| Provider access | Unknown | Alibaba Cloud Model Studio (Standard), Deepinfra (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation | agents, chat, generation, reasoning, structured_outputs, tools, vision |
PaddleOCR VL 1.5 Capabilities
Qwen3.7 Max Capabilities
Related Comparisons
All image comparisons →| A | Pair | B | Context |
|---|---|---|---|
PaddleOCR-VL-1.5Baidu | vs |  GPT-6 AstraOpenAI | cross-developer peersimage, text |
PaddleOCR-VL-1.5Baidu | vs | Claude Fable 5.1Anthropic | cross-developer peersimage, text |
PaddleOCR-VL-1.5Baidu | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
PaddleOCR-VL-1.5Baidu | vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
PaddleOCR-VL-1.5Baidu | vs | Grok 4.6xAI | cross-developer peersimage, text |
PaddleOCR-VL-1.5Baidu | vs | Qwen3.8-MaxQwen | cross-developer peersimage, text |
PaddleOCR-VL-1.5Baidu | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | PaddleOCR-VL-1.5Baidu | cross-developer peersimage, text |
PaddleOCR-VL-1.5Baidu | vs |  GLM-5.3Z.ai | cross-developer peerstext |
PaddleOCR-VL-1.5Baidu | vs |  Hy4 previewTencent | cross-developer peerstext |
PaddleOCR-VL-1.5Baidu | vs | Seed 2.1 ProByteDance Seed | cross-developer peersimage, text |
PaddleOCR-VL-1.5Baidu | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
PaddleOCR VL 1.5 vs Qwen3.7 Max FAQs
Is PaddleOCR VL 1.5 or Qwen3.7 Max better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both PaddleOCR VL 1.5 and Qwen3.7 Max, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, PaddleOCR VL 1.5 or Qwen3.7 Max?+
Only Qwen3.7 Max has a directly sourced input price: $1.475 per million tokens. Only Qwen3.7 Max has a directly sourced output price: $4.425 per million tokens.
Which has a larger context window, PaddleOCR VL 1.5 or Qwen3.7 Max?+
Qwen3.7 Max has the larger sourced context window. PaddleOCR VL 1.5 supports 131K and Qwen3.7 Max supports 1,000K.
Which performs better in benchmarks, PaddleOCR VL 1.5 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 PaddleOCR VL 1.5 or Qwen3.7 Max be self-hosted?+
PaddleOCR VL 1.5 is the only model in this pair currently marked as self-hostable. PaddleOCR VL 1.5 is open weight; Qwen3.7 Max is not marked open weight.
Can PaddleOCR VL 1.5 and Qwen3.7 Max understand images?+
PaddleOCR VL 1.5 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, PaddleOCR VL 1.5 or Qwen3.7 Max?+
Neither has a larger sourced maximum output. PaddleOCR VL 1.5 is — and Qwen3.7 Max is 66K.
Do PaddleOCR VL 1.5 and Qwen3.7 Max support reasoning and tool use?+
PaddleOCR VL 1.5: 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, PaddleOCR VL 1.5 or Qwen3.7 Max?+
PaddleOCR VL 1.5 has 0 sourced provider routes; Qwen3.7 Max has 4, so Qwen3.7 Max has broader tracked availability.
Which offers better value, PaddleOCR VL 1.5 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.