comparison
GPT-5.4 mini vs Mistral Large 3
Token pricing, context window and real monthly cost, side by side. Mistral Large 3 is the cheaper of the two for a typical workload — about 2.3× less.
Positioned by published specs — size, context and modality — not measured performance; a smaller model can sometimes outperform a larger one on your task.
By size & context, Mistral Large 3 (Flagship) sits above GPT-5.4 mini (Mini) yet costs less. On spec it's the straightforward pick; choose GPT-5.4 mini only for something it offers that Mistral Large 3 doesn't — modality, provider or ecosystem.
| metric | GPT-5.4 mini | Mistral Large 3 |
|---|---|---|
| Input / 1M | $0.75 | $0.50 |
| Output / 1M | $4.50 | $1.50 |
| Context | 400K | 262K |
| Technical class | Mini | Flagship |
| Cost @ typical workload | $675/mo | $300/mo |
| Modality | text + image | text + image |
| Price source | list | list |
| Provider | OpenAI | Mistral |
Snapshot . Cost uses a typical workload; tune it in the calculator. How we measure →
Which should you pick?
On a typical workload, Mistral Large 3 costs $300/mo against GPT-5.4 mini's $675/mo — roughly 2.3× cheaper. But the ranking depends on your output-to-input ratio: output is the pricier direction for both, so an output-heavy job (code generation, long answers) widens the gap while an input-heavy one (summarization, retrieval) narrows it. If you need to fit more in a single prompt, GPT-5.4 mini has the larger 400K-token window (~600 pages). By technical class (size and context, not measured capability), Mistral Large 3 is the larger class (Flagship) and the cheaper of the two — on spec, the stronger pick here.
These are list and routed market prices, not measured outcomes. Two models at the same rate can still cost different amounts to finish the same task, because verbose or reasoning-heavy models emit more tokens. That gap is exactly what measured cost-per-task captures. The technical-class read above is likewise spec-based — size, context and modality, not measured performance — so a smaller model can still outperform a larger one on your specific task.
Frequently asked questions
Is GPT-5.4 mini or Mistral Large 3 cheaper?
For a typical workload (1,500 input + 500 output tokens × 200,000 requests/month), Mistral Large 3 costs $300/mo versus $675/mo for GPT-5.4 mini — about 2.3× less. Because output is priced higher than input, the winner can flip if your workload writes much more or less than this; check your own numbers in the calculator.
What's the main difference between GPT-5.4 mini and Mistral Large 3?
On price, GPT-5.4 mini is $0.75/$4.50 per 1M (in/out) and Mistral Large 3 is $0.50/$1.50. By technical class (size & context) it's Mini (GPT-5.4 mini) versus Flagship (Mistral Large 3). GPT-5.4 mini has the larger context window at 400K tokens.
Is Mistral Large 3 in the same class as GPT-5.4 mini?
No. By size & context, Mistral Large 3 is a Flagship and GPT-5.4 mini is a Mini — 2 classes apart, so they're not drop-in substitutes. This is a spec comparison (size, context, modality), not a measured-performance one; a smaller model can still win on a task it's suited to.
More comparisons
- GPT-5.5 vs GPT-5.4 mini
- GPT-5.4 vs GPT-5.4 mini
- GPT-5.4 mini vs GPT-5.4 nano
- GPT-5.4 mini vs gpt-oss-120b
- Claude Sonnet 4.6 vs GPT-5.4 mini
- Claude Haiku 4.5 vs GPT-5.4 mini
Related
- GPT-5.4 mini and Mistral Large 3 — full specs and price history.
- API cost calculator — compare on your own workload.
- All comparisons — the full head-to-head index.