Gemini 2.5 Pro vs Yi Large
Compare Gemini 2.5 Pro and Yi Large: pricing, performance, context window, latency, and best use cases. Side-by-side comparison on XALEN.
Updated 2026-05-21 · By Abhishek Raj · Our methodology
| Feature | Gemini 2.5 Pro | Yi Large |
|---|---|---|
| Category | Frontier | Open Source |
| Parameters | ~1.5T | 300B |
| Context Window | 2M | 200K |
| Input Price | $0.07/1M tokens | $0.06/1M tokens |
| Output Price | $0.21/1M tokens | $0.12/1M tokens |
| Latency | ~600ms | ~450ms |
Choose Gemini 2.5 Pro when:
- ✓ Classical text analysis
- ✓ Multi-document reports
- ✓ Research
2M context, Strong multimodal, Long text analysis
Choose Yi Large when:
- ✓ Long document analysis
- ✓ Research
- ✓ Complex tasks
200K context, Strong analysis, Good reasoning
Verdict: Gemini 2.5 Pro vs Yi Large
For cost efficiency, Yi Large wins at $0.06/1M input tokens. For speed, Yi Large is faster at ~450ms. Gemini 2.5 Pro excels at Classical text analysis while Yi Large is better for Long document analysis. Both are available on XALEN through a single API — try them in the Playground to see which fits your workload.
Detailed Analysis
Pricing Comparison
Gemini 2.5 Pro costs $0.07/1M input tokens and $0.21/1M output tokens. Yi Large costs $0.06 input and $0.12 output. Yi Large is 1.2x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.
Performance & Context
Gemini 2.5 Pro has a 2M context window with ~600ms latency. Yi Large offers 200K context at ~450ms. Gemini 2.5 Pro has the larger context window.
Best For
Gemini 2.5 Pro (Frontier) is optimized for: Classical text analysis, Multi-document reports, Research. Yi Large (Open Source) works best for: Long document analysis, Research, Complex tasks.
Try Both on XALEN
Both models are available through XALEN's OpenAI-compatible API. Switch between them by changing the model parameter:
from xalen import XALEN
client = XALEN(api_key="xln_test_YOUR_KEY")
# Use Gemini 2.5 Pro
response_a = client.chat.completions.create(
model="gemini-2-5-pro",
messages=[{"role": "user", "content": "Your question here"}]
)
# Use Yi Large
response_b = client.chat.completions.create(
model="yi-large",
messages=[{"role": "user", "content": "Your question here"}]
)Frequently Asked Questions
Which is better, Gemini 2.5 Pro or Yi Large?
Gemini 2.5 Pro (Frontier, ~1.5T) offers 2M context. Yi Large (Open Source, 300B) offers 200K context. Choose Gemini 2.5 Pro for Classical text analysis or Yi Large for Long document analysis.
How much does Gemini 2.5 Pro cost vs Yi Large?
Gemini 2.5 Pro: $0.07/1M input, $0.21/1M output. Yi Large: $0.06/1M input, $0.12/1M output. Both available on XALEN with batch processing at 50% discount.
Can I use both models on XALEN?
Yes. XALEN provides 200+ models through a single OpenAI-compatible API. Switch between Gemini 2.5 Pro and Yi Large by changing the model parameter. No code changes needed.
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Last updated: 2026-05-21. Pricing and specifications may change. Check pricing page for latest rates.