Compare
Amazon Titan Text Premier vs Phi 4 Multimodal
This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.
Model
Phi 4 Multimodal
Context window
131K
131,072 tokens · ~98K words
Context window · side by side
Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.
Phi 4 Multimodal has about 3.1× the context window of the other in this pair.
Phi 4 Multimodal has 212% more context capacity (131K vs 42K tokens). Phi 4 Multimodal is 84% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Phi 4 Multimodal. Its 131K context fits entire documents without chunking (vs 42K).
RAG / high-volume retrieval
Use Phi 4 Multimodal. Input tokens are 84% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Amazon Titan Text Premier. Its 32K max output lets you generate complete artifacts in one request.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Amazon Titan Text Premier | Phi 4 Multimodal |
|---|---|---|
| Context window | 42,000 tokens (42K) | 131,072 tokens (131K) |
| Max output tokens | 32,000 tokens (32K) | 4,096 tokens (4K) |
| Speed tier | Balanced | Balanced |
| Vision | No | Yes |
| Function calling | No | Yes |
| Extended thinking | No | No |
| Prompt caching | No | No |
| Batch API | No | No |
| Release date | N/A | N/A |
Pricing matrix
Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.
| Provider | Amazon Titan Text Premier in | Amazon Titan Text Premier out | Phi 4 Multimodal in | Phi 4 Multimodal out |
|---|---|---|---|---|
| Aws Bedrock | $0.500/M | $1.50/M | — | — |
| Azure | — | — | $0.080/M | $0.320/M |
Frequently asked questions
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Use a smaller model.
Get better results.
Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.
Example: a multi-turn chat session
80% less to send — works with any model