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Qwen2 5 72b vs Text Bison 001
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.
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.
Qwen2 5 72b has about 4× the context window of the other in this pair.
Qwen2 5 72b has 300% more context capacity (32K vs 8K tokens). Qwen2 5 72b is 4% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Qwen2 5 72b. Its 32K context fits entire documents without chunking (vs 8K).
RAG / high-volume retrieval
Use Qwen2 5 72b. Input tokens are 4% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Qwen2 5 72b. 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 | Qwen2 5 72b | Text Bison 001 |
|---|---|---|
| Context window | 32,768 tokens (32K) | 8,192 tokens (8K) |
| Max output tokens | 32,768 tokens (32K) | 1,024 tokens (1K) |
| Speed tier | Deep | Balanced |
| Vision | No | No |
| Function calling | Yes | No |
| 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 | Qwen2 5 72b in | Qwen2 5 72b out | Text Bison 001 in | Text Bison 001 out |
|---|---|---|---|---|
| Deepinfra | $0.120/M | $0.390/M | — | — |
| — | — | $0.125/M | $0.125/M | |
| Hyperbolic | $0.120/M | $0.300/M | — | — |
| Nebius | $0.130/M | $0.400/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