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Gpt 3 5 Turbo 0125 vs GPT-3.5 Turbo 16k
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.
GPT-3.5 Turbo 16k has about 1× the context window of the other in this pair.
GPT-3.5 Turbo 16k has 0% more context capacity (16K vs 16K tokens). Gpt 3 5 Turbo 0125 is 83% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use GPT-3.5 Turbo 16k. Its 16K context fits entire documents without chunking (vs 16K).
RAG / high-volume retrieval
Use Gpt 3 5 Turbo 0125. Input tokens are 83% cheaper — critical when sending large retrieved contexts.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Gpt 3 5 Turbo 0125 | GPT-3.5 Turbo 16k |
|---|---|---|
| Context window | 16,384 tokens (16K) | 16,385 tokens (16K) |
| Max output tokens | 4,096 tokens (4K) | 4,096 tokens (4K) |
| Speed tier | Balanced | Balanced |
| Vision | No | No |
| Function calling | Yes | Yes |
| Extended thinking | No | No |
| Prompt caching | No | No |
| Batch API | Yes | Yes |
| Release date | N/A | Aug 2023 |
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 | Gpt 3 5 Turbo 0125 in | Gpt 3 5 Turbo 0125 out | GPT-3.5 Turbo 16k in | GPT-3.5 Turbo 16k out |
|---|---|---|---|---|
| Azure | $0.500/M | $1.50/M | — | — |
| Openai | $0.500/M | $1.50/M | $3.00/M | $4.00/M |
| Openrouter | — | — | $3.00/M | $4.00/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