Gemini 3.1 Pro Preview Custom Tools vs Yi-Lightning
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Gemini 3.1 Pro Preview Custom Tools and Yi-Lightning.
Gemini 3.1 Pro Preview Custom Tools
Gemini 3.1 Pro Preview Custom Tools is a variant of Gemini 3.1 Pro that improves tool selection behavior by preventing overuse of a general bash tool when more efficient third-party...
Yi-Lightning
Yi-Lightning is 01.AI's (ιΆδΈδΈη©) fastest and most cost-efficient model, purpose-built for high-throughput production workloads. It delivers competitive performance against GPT-4o-mini and Claude Haiku at a fraction of the cost, with exceptional bilingual Chinese-English capabilities. Yi-Lightning excels at classification, entity extraction, summarization, and high-frequency API tasks where latency and cost-per-call are critical constraints.
Technical Specifications
| Specification | Gemini 3.1 Pro Preview Custom Tools | Yi-Lightning |
|---|---|---|
| Provider | 01.AI | |
| Context Window | 1,048,756 tokens | 131,072 tokens |
| Agent Suitability | N/A | 82/100 |
| Time to First Token (TTFT) | N/A | 120 ms |
| Deployment Model | managed api | managed api |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2026-02-25 | 2025-10-01 |
API Pricing Comparison
Input Price per Million Tokens
Gemini 3.1 Pro Preview Custom Tools
$2.00
Yi-Lightning
$0.15
Output Price per Million Tokens
Gemini 3.1 Pro Preview Custom Tools
$12.00
Yi-Lightning
$0.30
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Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
Gemini 3.1 Pro Preview Custom Tools Quirks & Gotchas
No developer gotchas reported.
Yi-Lightning Quirks & Gotchas
- βΈBest cost-efficiency for high-volume bilingual applications
- βΈSelf-hostable via Ollama β excellent open-weight option for Asian-language pipelines