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Amazon Titan Text Premier vs Qwen Plus 2025 07 14
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
Qwen Plus 2025 07 14
Context window
129K
129,024 tokens · ~97K 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.
Qwen Plus 2025 07 14 has about 3.1× the context window of the other in this pair.
Qwen Plus 2025 07 14 has 207% more context capacity (129K vs 42K tokens). Qwen Plus 2025 07 14 is 19% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Qwen Plus 2025 07 14. Its 129K context fits entire documents without chunking (vs 42K).
RAG / high-volume retrieval
Use Qwen Plus 2025 07 14. Input tokens are 19% 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 | Qwen Plus 2025 07 14 |
|---|---|---|
| Context window | 42,000 tokens (42K) | 129,024 tokens (129K) |
| Max output tokens | 32,000 tokens (32K) | 16,384 tokens (16K) |
| Speed tier | Balanced | Balanced |
| Vision | No | No |
| Function calling | No | Yes |
| Extended thinking | No | Yes |
| 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 | Qwen Plus 2025 07 14 in | Qwen Plus 2025 07 14 out |
|---|---|---|---|---|
| Alibaba Cloud | — | — | $0.400/M | $1.20/M |
| Aws Bedrock | $0.500/M | $1.50/M | — | — |
Frequently asked questions
Powered by Mem0
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