Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Mistral Large 3 delivers a 75% cost reduction over Qwen 3.8 Max (2.4T MoE).
| Traffic Volume Tier | Mistral Large 3 Monthly | Qwen 3.8 Max (2.4T MoE) Monthly | Monthly Savings by picking Mistral Large 3 |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $5.76 | $23.04 | Save $17.28 / mo |
| 10,000 reqs/mo (Small App) | $57.60 | $230.40 | Save $172.80 / mo |
| 100,000 reqs/mo (Growth Production) | $576.00 | $2,304.00 | Save $1,728.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $5,760.00 | $23,040.00 | Save $17,280.00 / mo |
Mistral Large 3 is 75% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), Mistral Large 3 costs $0.00576 per request compared to $0.023 on Qwen 3.8 Max (2.4T MoE).
Mistral Large 3 offers a context window of 1,000,000 tokens (max output: 64,000), while Qwen 3.8 Max (2.4T MoE) offers 256,000 tokens (max output: 32,768).
At 100,000 requests per month, using Mistral Large 3 saves $1,728.00 every month (or $20,736.00 annually) compared to Qwen 3.8 Max (2.4T MoE).
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.