Yi Large vs Amazon Titan Embed v2
Compare Yi Large and Amazon Titan Embed v2: 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 | Yi Large | Amazon Titan Embed v2 |
|---|---|---|
| Category | Open Source | Embedding |
| Parameters | 300B | ~200M |
| Context Window | 200K | 8K |
| Input Price | $0.06/1M tokens | $0.001/1M tokens |
| Output Price | $0.12/1M tokens | N/A/1M tokens |
| Latency | ~450ms | ~15ms |
Choose Yi Large when:
- ✓ Long document analysis
- ✓ Research
- ✓ Complex tasks
200K context, Strong analysis, Good reasoning
Choose Amazon Titan Embed v2 when:
- ✓ AWS RAG pipelines
- ✓ Enterprise search
- ✓ Document indexing
AWS native, Low cost, Reliable
Verdict: Yi Large vs Amazon Titan Embed v2
For cost efficiency, Amazon Titan Embed v2 wins at $0.001/1M input tokens. For speed, Amazon Titan Embed v2 is faster at ~15ms. Yi Large excels at Long document analysis while Amazon Titan Embed v2 is better for AWS RAG pipelines. Both are available on XALEN through a single API — try them in the Playground to see which fits your workload.
Detailed Analysis
Pricing Comparison
Yi Large costs $0.06/1M input tokens and $0.12/1M output tokens. Amazon Titan Embed v2 costs $0.001 input and N/A output. Amazon Titan Embed v2 is 60.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.
Performance & Context
Yi Large has a 200K context window with ~450ms latency. Amazon Titan Embed v2 offers 8K context at ~15ms. Yi Large has the larger context window.
Best For
Yi Large (Open Source) is optimized for: Long document analysis, Research, Complex tasks. Amazon Titan Embed v2 (Embedding) works best for: AWS RAG pipelines, Enterprise search, Document indexing.
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 Yi Large
response_a = client.chat.completions.create(
model="yi-large",
messages=[{"role": "user", "content": "Your question here"}]
)
# Use Amazon Titan Embed v2
response_b = client.chat.completions.create(
model="amazon-titan-embed-v2",
messages=[{"role": "user", "content": "Your question here"}]
)Frequently Asked Questions
Which is better, Yi Large or Amazon Titan Embed v2?
Yi Large (Open Source, 300B) offers 200K context. Amazon Titan Embed v2 (Embedding, ~200M) offers AWS native. Choose Yi Large for Long document analysis or Amazon Titan Embed v2 for AWS RAG pipelines.
How much does Yi Large cost vs Amazon Titan Embed v2?
Yi Large: $0.06/1M input, $0.12/1M output. Amazon Titan Embed v2: $0.001/1M input, N/A/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 Yi Large and Amazon Titan Embed v2 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.