Side-by-side comparison
Mistral Large vs Jamba Large 1.7
| Mistral Large | Jamba Large 1.7 | |
|---|---|---|
| Provider | Mistral | 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 → | 2.0000 | 2.0000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 6.0000 | 8.0000 |
Frequently asked questions
Which is cheaper, Mistral Large or Jamba Large 1.7?
Mistral Large is cheaper than Jamba Large 1.7 on a 50/50 input/output blend by about $1 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, Mistral Large or Jamba Large 1.7?
Jamba Large 1.7 has the larger context window at 256k tokens versus 128k tokens for Mistral Large. That means Jamba Large 1.7 can ingest about 2.0x as much text per request.
What is the difference between Mistral Large and Jamba Large 1.7?
Mistral Large comes from Mistral; 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.