PaddleOCR VL 1.5 vs GLM 5.3
At a Glance
| Compare |  PaddleOCR VL 1.5Baidu |  GLM 5.3Z.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #16 of 4670.4 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 46.9–80.3 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #34 of 44$0.248 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #26 of 3850.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 38.2–54.9 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $1.20Deepinfra ↗ · Sep 21, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $4.00Deepinfra ↗ · Sep 21, 2026 |
| Context windowMaximum documented tokens | 131K | 1,000K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 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 |
|---|---|---|
| Developer | Baidu | Z.ai |
| Family | Paddleocr VL 1 5 | Glm 5 3 |
| Model | PaddleOCR-VL-1.5 | GLM-5.3 |
| Version | PaddleOCR-VL-1.5 | GLM-5.3 |
| Lifecycle | active | active |
| Released | 2026-01-29 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| 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 | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard) |
| Capabilities | chat, generation | agents, chat, reasoning, structured_outputs, tools |
PaddleOCR VL 1.5 Capabilities
GLM 5.3 Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
PaddleOCR-VL-1.5Baidu | vs | GLM-5.2Z.ai | cross-developer peerstext |
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 |  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 FAQs
Is PaddleOCR VL 1.5 or GLM 5.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both PaddleOCR VL 1.5 and GLM 5.3, 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?+
Only GLM 5.3 has a directly sourced input price: $1.20 per million tokens. Only GLM 5.3 has a directly sourced output price: $4.00 per million tokens.
Which has a larger context window, PaddleOCR VL 1.5 or GLM 5.3?+
GLM 5.3 has the larger sourced context window. PaddleOCR VL 1.5 supports 131K and GLM 5.3 supports 1,000K.
Which performs better in benchmarks, PaddleOCR VL 1.5 or GLM 5.3?+
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 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; GLM 5.3 is not marked open weight.
Can PaddleOCR VL 1.5 and GLM 5.3 understand images?+
PaddleOCR VL 1.5 is documented with image input; GLM 5.3 is not 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?+
Neither has a larger sourced maximum output. PaddleOCR VL 1.5 is — and GLM 5.3 is 131K.
Do PaddleOCR VL 1.5 and GLM 5.3 support reasoning and tool use?+
PaddleOCR VL 1.5: image input. GLM 5.3: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, PaddleOCR VL 1.5 or GLM 5.3?+
PaddleOCR VL 1.5 has 0 sourced provider routes; GLM 5.3 has 4, so GLM 5.3 has broader tracked availability.
Which offers better value, PaddleOCR VL 1.5 or GLM 5.3?+
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.