PaddleOCR VL 1.5 vs GLM 5.3 Flash
At a Glance
| Compare |  PaddleOCR VL 1.5Baidu |  GLM 5.3 FlashZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #29 of 4654.6 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.4–69.8 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #2 of 44$0.0087 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #1 of 3877.3 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 68.2–84.9 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.075Z.ai ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.25Z.ai ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 131K | 1,000K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 2, 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 | GLM-5.3-Flash |
|---|---|---|
| Developer | Baidu | Z.ai |
| Family | Paddleocr VL 1 5 | Glm 5 3 Flash |
| Model | PaddleOCR-VL-1.5 | GLM-5.3-Flash |
| Version | PaddleOCR-VL-1.5 | GLM-5.3-Flash |
| Lifecycle | active | active |
| Released | 2026-01-29 | 2026-09-02 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image, Video, Document |
| Output modalities | Text | Text |
| Context window | 131K | 1,000K |
| Total parameters | 958.6M | 320B |
| Active parameters | Unknown | 18B |
| License | apache-2.0 | MIT |
| Open weights | Yes | Yes |
| API available | Unknown | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard) |
| Capabilities | chat, generation | agents, chat, computer-use, reasoning, structured_outputs, tools, vision |
PaddleOCR VL 1.5 Capabilities
GLM 5.3 Flash 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 GLM 5.3 Flash FAQs
Is PaddleOCR VL 1.5 or GLM 5.3 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both PaddleOCR VL 1.5 and GLM 5.3 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, PaddleOCR VL 1.5 or GLM 5.3 Flash?+
Only GLM 5.3 Flash has a directly sourced input price: $0.075 per million tokens. Only GLM 5.3 Flash has a directly sourced output price: $0.25 per million tokens.
Which has a larger context window, PaddleOCR VL 1.5 or GLM 5.3 Flash?+
GLM 5.3 Flash has the larger sourced context window. PaddleOCR VL 1.5 supports 131K and GLM 5.3 Flash supports 1,000K.
Which performs better in benchmarks, PaddleOCR VL 1.5 or GLM 5.3 Flash?+
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 GLM 5.3 Flash be self-hosted?+
Both models have the same recorded self-hosting status: supported. PaddleOCR VL 1.5 is open weight; GLM 5.3 Flash is open weight.
Can PaddleOCR VL 1.5 and GLM 5.3 Flash understand images?+
PaddleOCR VL 1.5 is documented with image input; GLM 5.3 Flash is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, PaddleOCR VL 1.5 or GLM 5.3 Flash?+
Neither has a larger sourced maximum output. PaddleOCR VL 1.5 is — and GLM 5.3 Flash is 131K.
Do PaddleOCR VL 1.5 and GLM 5.3 Flash support reasoning and tool use?+
PaddleOCR VL 1.5: image input. GLM 5.3 Flash: 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 GLM 5.3 Flash?+
PaddleOCR VL 1.5 has 0 sourced provider routes; GLM 5.3 Flash has 4, so GLM 5.3 Flash has broader tracked availability.
Which offers better value, PaddleOCR VL 1.5 or GLM 5.3 Flash?+
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.