PaddleOCR-VL-1.5 vs Olmo-3-7B-Think
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | PaddleOCR-VL-1.5 | Olmo-3-7B-Think |
|---|---|---|
| Developer | Baidu | Ai2 |
| Family | Paddleocr VL 1 5 | Olmo 3 7b Think |
| Model | PaddleOCR-VL-1.5 | Olmo-3-7B-Think |
| Version | PaddleOCR-VL-1.5 | Olmo-3-7B-Think |
| Lifecycle | active | active |
| Released | 2026-01-29 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 131,072 | 65,536 |
| Total parameters | 958,588,736 | 7,298,011,136 |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Unknown | Unknown |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Unknown |
| Capabilities | chat, generation | chat, generation, reasoning |
13 comparable fields · 8 material differences · Pair passes the primary-source comparison gate
PaddleOCR-VL-1.5 Capabilities
Olmo-3-7B-Think Capabilities
Internal Comparison Graph
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
PaddleOCR-VL-1.5Baidu | vs |  GLM-OCRZ.ai | cross-developer peersimage, text |
PaddleOCR-VL-1.5Baidu | vs | cross-developer peersimage, text | |
PaddleOCR-VL-1.5Baidu | vs |  Ministral-3-3B-Instruct-2512Mistral AI | cross-developer peersimage, text |
PaddleOCR-VL-1.5Baidu | vs | Ministral-3-8B-Instruct-2512Mistral AI | cross-developer peersimage, text |
PaddleOCR-VL-1.5Baidu | vs |  Gemma 4 12BGoogle Deepmind | cross-developer peersimage, text |
PaddleOCR-VL-1.5Baidu | vs | Ministral-3-14B-Base-2512Mistral AI | cross-developer peersimage, text |
| vs | family variantstext | ||
| vs | family variantstext | ||
| vs | family variantstext | ||
| vs | cross-developer peerstext | ||
| vs |  Phi-4-mini-reasoningMicrosoft | cross-developer peerstext | |
| vs | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
PaddleOCR-VL-1.5 vs Olmo-3-7B-Think FAQs
Is PaddleOCR-VL-1.5 or Olmo-3-7B-Think better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both PaddleOCR-VL-1.5 and Olmo-3-7B-Think, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, PaddleOCR-VL-1.5 or Olmo-3-7B-Think?+
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 Olmo-3-7B-Think?+
PaddleOCR-VL-1.5 has the larger sourced context window. PaddleOCR-VL-1.5 supports 131,072 and Olmo-3-7B-Think supports 65,536.
Which performs better in benchmarks, PaddleOCR-VL-1.5 or Olmo-3-7B-Think?+
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 Olmo-3-7B-Think be self-hosted?+
Both models have the same recorded self-hosting status: supported. PaddleOCR-VL-1.5 is open weight; Olmo-3-7B-Think is open weight.
Can PaddleOCR-VL-1.5 and Olmo-3-7B-Think understand images?+
PaddleOCR-VL-1.5 is documented with image input; Olmo-3-7B-Think is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, PaddleOCR-VL-1.5 or Olmo-3-7B-Think?+
Neither has a larger sourced maximum output. PaddleOCR-VL-1.5 is — and Olmo-3-7B-Think is 32,768.
Do PaddleOCR-VL-1.5 and Olmo-3-7B-Think support reasoning and tool use?+
PaddleOCR-VL-1.5: image input. Olmo-3-7B-Think: reasoning. Feature support does not establish relative quality.
Which is available from more inference providers, PaddleOCR-VL-1.5 or Olmo-3-7B-Think?+
PaddleOCR-VL-1.5 has 0 sourced provider routes; Olmo-3-7B-Think has 0, a tie.
Which offers better value, PaddleOCR-VL-1.5 or Olmo-3-7B-Think?+
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.