Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). AI21 Jamba 1.5 Large delivers a 92% cost reduction over GPT-5.5 Pro.
| Traffic Volume Tier | GPT-5.5 Pro Monthly | AI21 Jamba 1.5 Large Monthly | Monthly Savings by picking AI21 Jamba 1.5 Large |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $525.60 | $40.00 | Save $485.60 / mo |
| 10,000 reqs/mo (Small App) | $5,256.00 | $400.00 | Save $4,856.00 / mo |
| 100,000 reqs/mo (Growth Production) | $52,560.00 | $4,000.00 | Save $48,560.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $525,600.00 | $40,000.00 | Save $485,600.00 / mo |
AI21 Jamba 1.5 Large is 92% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), AI21 Jamba 1.5 Large costs $0.04 per request compared to $0.5256 on GPT-5.5 Pro.
GPT-5.5 Pro offers a context window of 512,000 tokens (max output: 64,000), while AI21 Jamba 1.5 Large offers 256,000 tokens (max output: 4,096).
At 100,000 requests per month, using AI21 Jamba 1.5 Large saves $48,560.00 every month (or $582,720.00 annually) compared to GPT-5.5 Pro.
Output is the expensive side — prefer models with cheap output for autocomplete-style calls. Cache repository context between keystrokes; diffs change far less than the full file. Measure acceptance rate: paying for output users delete is pure waste.