LLM Comparison
ERNIE 4.5 VL 28B A3B vs GPT-5.5
Side-by-side specs, pricing & capabilities · Updated October 2026
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2/6 modelsSame tier:
| Organization | ||
| OpenTools Score | 85 4.9 | |
| Family | ERNIE | GPT |
| Status | Current | Current |
| Release Date | Aug 2025 | Apr 2026 |
| Context Window | 30K tokens | 1.1M tokens |
| Input Price | $0.14/M tokens | $5.00/M tokens |
| Output Price | $0.56/M tokens | $30.00/M tokens |
| Pricing Notes | — | Cached input: $0.50/M tokens. Long context (>272K tokens): 2x input, 1.5x output. Batch API: 50% discount. Priority: 2.5x standard. |
| Capabilities | textvisioncode | textvisioncodetool-useextended-thinkingcomputer-useweb-search |
| Training Cutoff | — | Dec 01, 2025 |
| Max Output | 8K tokens | 128K tokens |
| API Identifier | baidu/ernie-4.5-vl-28b-a3b | openai/gpt-5.5 |
| Benchmarks | ||
| MMLU | — | 92.4openai |
| GPQA Diamond | — | 93.6openai |
| ARC-AGI-2 | — | 85openai |
| Terminal-Bench 2.0 | — | 82.7openai |
| SWE-bench Pro | — | 58.6openai |
| OSWorld-Verified | — | 78.7openai |
| BrowseComp | — | 84.4openai |
| MMMU-Pro | — | 81.2openai |
| FrontierMath Tier 4 | — | 35.4openai |
| HLE (with tools) | — | 52.2openai |
| GDPval | — | 84.9openai |
| Toolathlon | — | 55.6openai |
| CyberGym | — | 81.8openai |
| MRCR v2 512K-1M | — | 74openai |
| Artificial Analysis Intelligence Index v4.3 | — | 38.6292artificial-analysis |
| SciCode | — | 55.787artificial-analysis |
| Terminal-Bench 2.1 | — | 84.2697artificial-analysis |
| View ERNIE 4.5 VL 28B A3B | View GPT-5.5 | |
Cost Calculator
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| Model | Input | Output | Total / mo | vs Best |
|---|---|---|---|---|
| ERNIE 4.5 VL 28B A3BCheapest | $0.14 | $0.28 | $0.42 | — |
| GPT-5.5 | $5.00 | $15.00 | $20.00 | +4662% |
Baidu
ERNIE 4.5 VL 28B A3B
ERNIE 4.5 VL 28B A3B is a multimodal llm from Baidu. Supports up to 30,000 token context window. Available from $0.14/M input tokens.
OpenAI
GPT-5.5
GPT-5.5 is OpenAI's smartest and most intuitive model, built for agentic work like coding, research, and data analysis. It matches GPT-5.4 per-token latency while delivering higher intelligence with significantly fewer tokens. Supports a 1,050,000 token context window and five reasoning effort levels (none through xhigh).
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