Mistral Large 3 vs GPT-4.1
European hosting against the mainstream mid tier.
Monthly cost by workload shape
The same volume costs very different amounts depending on the shape of the traffic. No caching or batch applied.
| Workload | Mistral Large 3 | GPT-4.1 | Difference | |
|---|---|---|---|---|
Customer support chatbot 50K/day · 800 in / 300 out | $1,293.70 | $6,088.00 | Mistral Large 3saves $4,794.30 | Model it → |
RAG / search answers 20K/day · 3000 in / 400 out | $1,278.48 | $5,600.96 | Mistral Large 3saves $4,322.48 | Model it → |
Agent / tool-use loop 8.0K/day · 2500 in / 600 out | $523.57 | $2,386.50 | Mistral Large 3saves $1,862.93 | Model it → |
Batch summarization 100K/day · 5000 in / 500 out | $9,893.00 | $42.6K | Mistral Large 3saves $32.7K | Model it → |
Code assistant 15K/day · 2000 in / 800 out | $1,004.52 | $4,748.64 | Mistral Large 3saves $3,744.12 | Model it → |
Frequently asked questions
Which is cheaper, Mistral Large 3 or GPT-4.1?
At a typical 3:1 input-to-output ratio, Mistral Large 3 is cheaper: $0.750 blended per 1M tokens against $3.50. That holds at every input:output ratio — one model is cheaper on both rates.
What is the price difference between Mistral Large 3 and GPT-4.1?
Mistral Large 3: $0.500 input, $1.50 output per 1M tokens. GPT-4.1: $2.00 input, $8.00 output. That is 4.0x on input and 5.3x on output.
Does prompt caching change the answer?
Mistral Large 3 caches input at $0.050/1M and GPT-4.1 caches at $0.500/1M. For input-heavy workloads with repeated context, caching can matter more than the base rate difference — model both in the calculator rather than comparing rate cards.
This page compares published prices only. It makes no claim about which model performs better on any task — that depends entirely on your evaluations.
Rate cards decide nothing on their own.
Most teams run several models at once. Finitizer TokenOps shows what each one actually costs you in production, by team and feature, so a routing decision is made on data rather than on a price page.