Llama 3.2 90B Vision vs Amazon Titan Embed v2

Compare Llama 3.2 90B Vision 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

All Meta models All Amazon models What is an LLM API? Python Quickstart What is inference?
Feature Llama 3.2 90B Vision Amazon Titan Embed v2
CategoryVisionEmbedding
Parameters90B~200M
Context Window128K8K
Input Price$0.06/1M tokens$0.001/1M tokens
Output Price$0.10/1M tokensN/A/1M tokens
Latency~500ms~15ms

Choose Llama 3.2 90B Vision when:

  • ✓ Chart image analysis
  • ✓ Document scanning
  • ✓ Visual Q&A
Key Strengths:

Vision + language, Open weights, Good reasoning

Choose Amazon Titan Embed v2 when:

  • ✓ AWS RAG pipelines
  • ✓ Enterprise search
  • ✓ Document indexing
Key Strengths:

AWS native, Low cost, Reliable

Verdict: Llama 3.2 90B Vision 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. Llama 3.2 90B Vision excels at Chart image 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

Llama 3.2 90B Vision costs $0.06/1M input tokens and $0.10/1M 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

Llama 3.2 90B Vision has a 128K context window with ~500ms latency. Amazon Titan Embed v2 offers 8K context at ~15ms. Llama 3.2 90B Vision has the larger context window.

Best For

Llama 3.2 90B Vision (Vision) is optimized for: Chart image analysis, Document scanning, Visual Q&A. 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 Llama 3.2 90B Vision
response_a = client.chat.completions.create(
    model="llama-3-2-90b-vision",
    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"}]
)

Start Building with XALEN

200+ AI models. One API. Pay-as-you-go.

Get API Key Try in Playground

Frequently Asked Questions

Which is better, Llama 3.2 90B Vision or Amazon Titan Embed v2?

Llama 3.2 90B Vision (Vision, 90B) offers Vision + language. Amazon Titan Embed v2 (Embedding, ~200M) offers AWS native. Choose Llama 3.2 90B Vision for Chart image analysis or Amazon Titan Embed v2 for AWS RAG pipelines.

How much does Llama 3.2 90B Vision cost vs Amazon Titan Embed v2?

Llama 3.2 90B Vision: $0.06/1M input, $0.10/1M 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 Llama 3.2 90B Vision and Amazon Titan Embed v2 by changing the model parameter. No code changes needed.

Related Comparisons

Llama 3.2 90B Vision vs Vedika Vision Llama 3.2 90B Vision vs Text Embedding 3 Large Llama 3.2 90B Vision vs Llama 3.2 11B Vision Llama 3.2 90B Vision vs Qwen 2.5 VL 72B Llama 3.2 90B Vision vs Qwen 2.5 VL 7B Llama 3.2 90B Vision vs Pixtral Large

Last updated: 2026-05-21. Pricing and specifications may change. Check pricing page for latest rates.