Side-by-side comparison
o3 vs Jamba Large 1.7
| o3 | Jamba Large 1.7 | |
|---|---|---|
| Provider | OpenAI | AI21 |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 200,000 | 256,000 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | vision, tools, json_mode | tools, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 2.0000 | 2.0000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 8.0000 | 8.0000 |
Frequently asked questions
Which is cheaper, o3 or Jamba Large 1.7?
o3 is cheaper than Jamba Large 1.7 on a 50/50 input/output blend by about $0 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, o3 or Jamba Large 1.7?
Jamba Large 1.7 has the larger context window at 256k tokens versus 200k tokens for o3. That means Jamba Large 1.7 can ingest about 1.3x as much text per request.
What is the difference between o3 and Jamba Large 1.7?
o3 comes from OpenAI; Jamba Large 1.7 comes from AI21. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.