Side-by-side comparison
GPT-4o Audio vs Jamba Large 1.7
| GPT-4o Audio | Jamba Large 1.7 | |
|---|---|---|
| Provider | OpenAI | AI21 |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 128,000 | 256,000 |
| 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 → | 2.5000 | 2.0000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 10.0000 | 8.0000 |
Frequently asked questions
Which is cheaper, GPT-4o Audio or Jamba Large 1.7?
Jamba Large 1.7 is cheaper than GPT-4o Audio on a 50/50 input/output blend by about $1.25 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, GPT-4o Audio or Jamba Large 1.7?
Jamba Large 1.7 has the larger context window at 256k tokens versus 128k tokens for GPT-4o Audio. That means Jamba Large 1.7 can ingest about 2.0x as much text per request.
What is the difference between GPT-4o Audio and Jamba Large 1.7?
GPT-4o Audio 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.