Llama 4 Scout 17B-16E vs Ministral 3 14B
Llama 4 Scout 17B-16E and Ministral 3 14B both score 6 on the intelligence index. The cheapest machine here that runs Llama 4 Scout 17B-16E is the Framework Desktop, 128GB, at $3,449. Ministral 3 14B runs on the Mac mini M6, 24GB at $1,099, $2,350 less. On the Framework Desktop, 128GB, the cheapest machine here that runs both, Llama 4 Scout 17B-16E is about 1.1× quicker: 16 tok/s against 14, Llama 4 Scout 17B-16E's measured and the other estimated from memory bandwidth. At 500k tokens a day the Framework Desktop, 128GB pays for itself in 110 years running Ministral 3 14B, against 215 years running Llama 4 Scout 17B-16E.
| Llama 4 Scout 17B-16E | Ministral 3 14B | |
|---|---|---|
| Intelligence index | 6 | 6 |
| Class | Below every hosted tier | Below every hosted tier |
| Weights | 65 GB | 8.2 GB |
| Needs at 32k | 68 GB | 14 GB |
| Quantisation | Q4_K_M | Q4_K_M |
| Parameters | 108.6B (17B active) | 14B |
| Max context | 10M | 256k |
| API price per 1M | $0.1 in / $0.3 out | $0.2 in / $0.2 out |
| Licence | Llama 4 Community License | Apache 2.0 |
| Machines here that run it | 13 of 37 | 33 of 37 |
| Cheapest machine that runs it | Strix Halo Framework Desktop, 128GB $3,449 | Mac mini M6, 24GB $1,099 |
Run Llama 4 Scout 17B-16E on the Framework Desktop, 128GB · or Ministral 3 14B on the Mac mini M6, 24GB
Jump to: Side by side on the Framework Desktop, 128GB · How much use it takes to pay for the machine · Machines that run one and not the other · The assumptions behind both columns
Side by side on the Framework Desktop, 128GB
The table above gives each model the cheapest machine that runs it, and those are two different machines, so nothing in it is a like-for-like race. The Strix Halo Framework Desktop, 128GB is the cheapest machine here that runs both, so this is the pair doing the same work on the same hardware.
| Llama 4 Scout 17B-16E | Ministral 3 14B | |
|---|---|---|
| Speed at 32k | 16 tok/s measured | 14 tok/s estimated |
| Pay-back on this machine | Pays back in 215 years | Pays back in 110 years |
| API cost per month | $1.71 | $3.04 |
Run Llama 4 Scout 17B-16E on the Framework Desktop, 128GB · or Ministral 3 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 Framework Desktop, 128GB. 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.
| A day's use | Llama 4 Scout 17B-16E | Ministral 3 14B |
|---|---|---|
| 50ka few chats a day | 2,152 years | 1,097 years |
| 200klight assistant use | 538 years | 274 years |
| 1Ma moderate coding-assistant day | 108 years | 55 years |
| 4Mheavy coding with an agent | 27 years | 14 years |
| 20Magents running most of the day | 5.4 years | 2.8 yearsits ceiling |
The Framework Desktop, 128GB generates at most 19.5M tokens a day on Ministral 3 14B, so that column's figure at 20M tokens a day is for the most it can do, not for the whole of what was asked.
Run Llama 4 Scout 17B-16E at 20M tokens a day · or Ministral 3 14B
Machines that run one and not the other
Ministral 3 14B needs 14 GB of memory at 32k of context and Llama 4 Scout 17B-16E needs 68 GB. That puts Ministral 3 14B on 20 of the 37 machines priced here that Llama 4 Scout 17B-16E does not, starting at $1,099.
| Machine | Price | Memory | Speed on Ministral 3 14B | Pay-back |
|---|---|---|---|---|
| Mac mini M6, 24GB | $1,099 | 24 GB | 9.4 tok/s estimated | Pays back in 34 years |
| Strix Halo Framework Desktop, 32GB | $1,269 | 32 GB | 14 tok/s estimated | Pays back in 40 years |
| Mac mini M6, 32GB | $1,299 | 32 GB | 9.4 tok/s estimated | Pays back in 40 years |
| AMD Radeon AI PRO R9700, 32GB | $1,299card only | 32 GB | 41 tok/s estimated | Pays back in 40 years |
| Mac mini M5 Pro, 24GB | $1,699 | 24 GB | 17 tok/s estimated | Pays back in 53 years |
| Strix Halo Corsair AI Workstation 300, 64GB | $1,700 | 64 GB | 14 tok/s estimated | Pays back in 54 years |
14 more, on the Ministral 3 14B page.
Llama 4 Scout 17B-16E is also head to head with Qwen3 14B above it on the leaderboard. One more model needs much the same memory: gpt-oss-120b. Ministral 3 14B is also head to head with Qwen3 8B below it on the leaderboard. Two more models need much the same memory: gpt-oss-20b and Qwen3 14B.
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 Llama 4 Scout 17B-16E · every machine that runs Ministral 3 14B · every other match-up · both against the frontier · the quickest pay-back at each level of use