PaddleOCR VL 1.5 vs Llama 3.3 70B Instruct
At a Glance
| Compare |  PaddleOCR VL 1.5Baidu | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.10Openrouter ↗ · Sep 23, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.32Openrouter ↗ · Sep 23, 2026 |
| Context windowMaximum documented tokens | 131K | 131K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 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 | Llama-3.3-70B-Instruct |
|---|---|---|
| Developer | Baidu | Meta |
| Family | Paddleocr VL 1 5 | Llama 3 3 70b Instruct |
| Model | PaddleOCR-VL-1.5 | Llama-3.3-70B-Instruct |
| Version | PaddleOCR-VL-1.5 | Llama-3.3-70B-Instruct |
| Lifecycle | active | active |
| Released | 2026-01-29 | 2024-12-06 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 131K | 131K |
| Total parameters | 958.6M | 70.6B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | llama3.3 |
| Open weights | Yes | Yes |
| API available | Unknown | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation | chat, generation, tools |
PaddleOCR VL 1.5 Capabilities
Llama 3.3 70B Instruct Capabilities
Related Comparisons
All text 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 Llama 3.3 70B Instruct FAQs
Is PaddleOCR VL 1.5 or Llama 3.3 70B Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both PaddleOCR VL 1.5 and Llama 3.3 70B Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, PaddleOCR VL 1.5 or Llama 3.3 70B Instruct?+
Only Llama 3.3 70B Instruct has a directly sourced input price: $0.10 per million tokens. Only Llama 3.3 70B Instruct has a directly sourced output price: $0.32 per million tokens.
Which has a larger context window, PaddleOCR VL 1.5 or Llama 3.3 70B Instruct?+
Neither model has a larger sourced context window in this comparison. PaddleOCR VL 1.5 is 131K and Llama 3.3 70B Instruct is 131K.
Which performs better in benchmarks, PaddleOCR VL 1.5 or Llama 3.3 70B Instruct?+
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 Llama 3.3 70B Instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. PaddleOCR VL 1.5 is open weight; Llama 3.3 70B Instruct is open weight.
Can PaddleOCR VL 1.5 and Llama 3.3 70B Instruct understand images?+
PaddleOCR VL 1.5 is documented with image input; Llama 3.3 70B Instruct is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, PaddleOCR VL 1.5 or Llama 3.3 70B Instruct?+
Neither has a larger sourced maximum output. PaddleOCR VL 1.5 is — and Llama 3.3 70B Instruct is —.
Do PaddleOCR VL 1.5 and Llama 3.3 70B Instruct support reasoning and tool use?+
PaddleOCR VL 1.5: image input. Llama 3.3 70B Instruct: tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, PaddleOCR VL 1.5 or Llama 3.3 70B Instruct?+
PaddleOCR VL 1.5 has 0 sourced provider routes; Llama 3.3 70B Instruct has 3, so Llama 3.3 70B Instruct has broader tracked availability.
Which offers better value, PaddleOCR VL 1.5 or Llama 3.3 70B Instruct?+
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.