Ministral 3 14B vs Qwen3 14B

Ministral 3 14B and Qwen3 14B both score 6 on the intelligence index. Both take the same machine to start: the cheapest here that runs either is the Mac mini M6, 24GB, at $1,099. On it, Ministral 3 14B is about 1.1× quicker: 9.4 tok/s against 8.9, both estimated from memory bandwidth. At 500k tokens a day the Mac mini M6, 24GB pays for itself in 34 years running Ministral 3 14B, against 70 years running Qwen3 14B.

Run Ministral 3 14B on the Mac mini M6, 24GB · or Qwen3 14B

Ratings are coarse on purpose: they say what a model is usable for, not where it places to the decimal.

Side by side on the Mac mini M6, 24GB

The cheapest machine that runs either model is the same one, so this is the pair doing the same work on the same hardware: Mac mini M6, 24GB, at $1,099.

Run Ministral 3 14B on the Mac mini M6, 24GB · or Qwen3 14B

How much use it takes to pay for the machine

Everything above is at 500k tokens a day. Pay-back moves with how much you actually run, so here are both models at the five levels of use the calculator names, on the Mac mini M6, 24GB. Ministral 3 14B pays for it sooner at every level of use, so which of them to run does not turn on how hard you work it.

The Mac mini M6, 24GB cannot generate 20M tokens a day on either model: at most 13M on Ministral 3 14B and 12.3M on Qwen3 14B. Both figures on that row are for the most it can do.

Run Ministral 3 14B at 20M tokens a day · or Qwen3 14B

Memory is not what separates them

Ministral 3 14B needs 14 GB of memory at 32k of context and Qwen3 14B needs 14 GB. Every machine priced here that runs one runs the other, at 32k of context. So the choice between them is what each is good at, how fast it runs and what the same work costs on an API, not what you have to buy to hold it.

Ministral 3 14B is also head to head with Llama 4 Scout 17B-16E above it on the leaderboard and Qwen3 8B below it. One more model needs much the same memory: gpt-oss-20b. Qwen3 14B is also head to head with Mistral Small 3.2 24B Instruct above it on the leaderboard and Llama 4 Scout 17B-16E below it.

The assumptions behind both columns

Both columns use the same usage: 500k tokens a day at 15:1 input to output, 32k of context, $0.17 per kWh, and today's API prices held flat. Speeds marked estimated are worked out from memory bandwidth rather than measured, and pay-back scales with them. Where nobody rents an open model by the token, its API prices are the nearest hosted model's, named beside them. Machines are the 37 here with a published price that are still sold. Change any of it in the calculator.

More head to head: every machine that runs Ministral 3 14B · every machine that runs Qwen3 14B · every other match-up · both against the frontier · the quickest pay-back at each level of use