PaddleOCR VL 1.5 vs Bonsai 27B
At a Glance
| Compare |  PaddleOCR VL 1.5Baidu |  Bonsai 27BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 131K | 262K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 18, 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 | Bonsai 27B |
|---|---|---|
| Developer | Baidu | PrismML |
| Family | Paddleocr VL 1 5 | Bonsai 27b |
| Model | PaddleOCR-VL-1.5 | Bonsai 27B |
| Version | PaddleOCR-VL-1.5 | Bonsai 27B |
| Lifecycle | active | active |
| Released | 2026-01-29 | 2026-07-04 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 131K | 262K |
| Total parameters | 958.6M | 27B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Unknown | No |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Unknown |
| Capabilities | chat, generation | chat, generation, reasoning, tools, vision |
| Base model | Unknown | Qwen3.6 27B |
| Effective bit width | Unknown | 1 bit per weight |
| Language model size | Unknown | 3.53 GiB |
| Weight format | Unknown | Binary Q1_0 |
PaddleOCR VL 1.5 Capabilities
Bonsai 27B 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 Bonsai 27B FAQs
Is PaddleOCR VL 1.5 or Bonsai 27B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both PaddleOCR VL 1.5 and Bonsai 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, PaddleOCR VL 1.5 or Bonsai 27B?+
Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.
Which has a larger context window, PaddleOCR VL 1.5 or Bonsai 27B?+
Bonsai 27B has the larger sourced context window. PaddleOCR VL 1.5 supports 131K and Bonsai 27B supports 262K.
Which performs better in benchmarks, PaddleOCR VL 1.5 or Bonsai 27B?+
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 Bonsai 27B be self-hosted?+
Both models have the same recorded self-hosting status: supported. PaddleOCR VL 1.5 is open weight; Bonsai 27B is open weight.
Can PaddleOCR VL 1.5 and Bonsai 27B understand images?+
PaddleOCR VL 1.5 is documented with image input; Bonsai 27B is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, PaddleOCR VL 1.5 or Bonsai 27B?+
Neither has a larger sourced maximum output. PaddleOCR VL 1.5 is — and Bonsai 27B is —.
Do PaddleOCR VL 1.5 and Bonsai 27B support reasoning and tool use?+
PaddleOCR VL 1.5: image input. Bonsai 27B: 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 Bonsai 27B?+
PaddleOCR VL 1.5 has 0 sourced provider routes; Bonsai 27B has 0, a tie.
Which offers better value, PaddleOCR VL 1.5 or Bonsai 27B?+
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.