PaddleOCR VL 1.5 vs GLM 5.2
At a Glance
| Compare |  PaddleOCR VL 1.5Baidu |  GLM 5.2Z.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #23 of 4661.9 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 41.3–74.6 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #12 of 44$0.056 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #7 of 3861.3 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 51.0–67.6 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.75Deepinfra ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $2.40Deepinfra ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 131K | 1,049K |
| 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 | GLM-5.2 |
|---|---|---|
| Developer | Baidu | Z.ai |
| Family | Paddleocr VL 1 5 | Glm 5 2 |
| Model | PaddleOCR-VL-1.5 | GLM-5.2 |
| Version | PaddleOCR-VL-1.5 | GLM-5.2 |
| Lifecycle | active | active |
| Released | 2026-01-29 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 131K | 1,049K |
| Total parameters | 958.6M | 753.3B |
| Active parameters | Unknown | Unknown |
| 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), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation | chat, generation, reasoning, tools |
PaddleOCR VL 1.5 Capabilities
GLM 5.2 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 GLM 5.2 FAQs
Is PaddleOCR VL 1.5 or GLM 5.2 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both PaddleOCR VL 1.5 and GLM 5.2, 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 5.2?+
Only GLM 5.2 has a directly sourced input price: $0.75 per million tokens. Only GLM 5.2 has a directly sourced output price: $2.40 per million tokens.
Which has a larger context window, PaddleOCR VL 1.5 or GLM 5.2?+
GLM 5.2 has the larger sourced context window. PaddleOCR VL 1.5 supports 131K and GLM 5.2 supports 1,049K.
Which performs better in benchmarks, PaddleOCR VL 1.5 or GLM 5.2?+
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 5.2 be self-hosted?+
Both models have the same recorded self-hosting status: supported. PaddleOCR VL 1.5 is open weight; GLM 5.2 is open weight.
Can PaddleOCR VL 1.5 and GLM 5.2 understand images?+
PaddleOCR VL 1.5 is documented with image input; GLM 5.2 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 5.2?+
Neither has a larger sourced maximum output. PaddleOCR VL 1.5 is — and GLM 5.2 is —.
Do PaddleOCR VL 1.5 and GLM 5.2 support reasoning and tool use?+
PaddleOCR VL 1.5: image input. GLM 5.2: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, PaddleOCR VL 1.5 or GLM 5.2?+
PaddleOCR VL 1.5 has 0 sourced provider routes; GLM 5.2 has 5, so GLM 5.2 has broader tracked availability.
Which offers better value, PaddleOCR VL 1.5 or GLM 5.2?+
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.