Side-by-side comparison
Jamba Large 1.7 vs GPT-3.5 Turbo 16k
OpenAI
GPT-3.5 Turbo 16k
🔧 Tools
{} JSON
Input / 1M
$3.0000
Output / 1M
$4.0000
| Jamba Large 1.7 | GPT-3.5 Turbo 16k | |
|---|---|---|
| Provider | AI21 | OpenAI |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 256,000 | 16,385 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | tools, json_mode | tools, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 2.0000 | 3.0000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 8.0000 | 4.0000 |
Frequently asked questions
Which is cheaper, Jamba Large 1.7 or GPT-3.5 Turbo 16k?
GPT-3.5 Turbo 16k is cheaper than Jamba Large 1.7 on a 50/50 input/output blend by about $1.5 per 1M tokens. Exact savings depend on your input-vs-output ratio — use the cost calculator on this page for a workload-specific estimate.
Which has a larger context window, Jamba Large 1.7 or GPT-3.5 Turbo 16k?
Jamba Large 1.7 has the larger context window at 256k tokens versus 16k tokens for GPT-3.5 Turbo 16k. That means Jamba Large 1.7 can ingest about 15.6x as much text per request.
What is the difference between Jamba Large 1.7 and GPT-3.5 Turbo 16k?
Jamba Large 1.7 comes from AI21; GPT-3.5 Turbo 16k comes from OpenAI. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.