Qwen3 14B vs Llama 4 Scout 17B-16E
Qwen3 14B and Llama 4 Scout 17B-16E 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. Qwen3 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 1.2× quicker: 16 tok/s against 13, 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 215 years running Llama 4 Scout 17B-16E, against 242 years running Qwen3 14B.
| Qwen3 14B | Llama 4 Scout 17B-16E | |
|---|---|---|
| Intelligence index | 6 | 6 |
| Class | Below every hosted tier | Below every hosted tier |
| Weights | 9.0 GB | 65 GB |
| Needs at 32k | 14 GB | 68 GB |
| Quantisation | Q4_K_M | Q4_K_M |
| Parameters | 14.8B | 108.6B (17B active) |
| Max context | 40k | 10M |
| API price per 1M | $0.1 in / $0.22 out | $0.1 in / $0.3 out |
| Licence | Apache 2.0 | Llama 4 Community License |
| Machines here that run it | 33 of 37 | 13 of 37 |
| Cheapest machine that runs it | Mac mini M6, 24GB $1,099 | Strix Halo Framework Desktop, 128GB $3,449 |
| Summarising | good | good |
| Translation | good | good |
| Everyday coding | usable | usable |
| Reasoning & maths | usable | usable |
| Agentic work | don’t | usable |
Run Qwen3 14B on the Mac mini M6, 24GB · or Llama 4 Scout 17B-16E on the Framework Desktop, 128GB
Ratings are coarse on purpose: they say what a model is usable for, not where it places to the decimal.
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.
| Qwen3 14B | Llama 4 Scout 17B-16E | |
|---|---|---|
| Speed at 32k | 13 tok/s estimated | 16 tok/s measured |
| Pay-back on this machine | Pays back in 242 years | Pays back in 215 years |
| API cost per month | $1.64 | $1.71 |
Run Qwen3 14B on the Framework Desktop, 128GB · or Llama 4 Scout 17B-16E
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. Llama 4 Scout 17B-16E 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 | Qwen3 14B | Llama 4 Scout 17B-16E |
|---|---|---|
| 50ka few chats a day | 2,417 years | 2,152 years |
| 200klight assistant use | 604 years | 538 years |
| 1Ma moderate coding-assistant day | 121 years | 108 years |
| 4Mheavy coding with an agent | 30 years | 27 years |
| 20Magents running most of the day | 6.5 yearsits ceiling | 5.4 years |
The Framework Desktop, 128GB generates at most 18.5M tokens a day on Qwen3 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 Qwen3 14B at 20M tokens a day · or Llama 4 Scout 17B-16E
Machines that run one and not the other
Qwen3 14B needs 14 GB of memory at 32k of context and Llama 4 Scout 17B-16E needs 68 GB. That puts Qwen3 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 Qwen3 14B | Pay-back |
|---|---|---|---|---|
| Mac mini M6, 24GB | $1,099 | 24 GB | 8.9 tok/s estimated | Pays back in 70 years |
| Strix Halo Framework Desktop, 32GB | $1,269 | 32 GB | 13 tok/s estimated | Pays back in 89 years |
| Mac mini M6, 32GB | $1,299 | 32 GB | 8.9 tok/s estimated | Pays back in 83 years |
| AMD Radeon AI PRO R9700, 32GB | $1,299card only | 32 GB | 39 tok/s estimated | Pays back in 84 years |
| Mac mini M5 Pro, 24GB | $1,699 | 24 GB | 16 tok/s estimated | Pays back in 114 years |
| Strix Halo Corsair AI Workstation 300, 64GB | $1,700 | 64 GB | 13 tok/s estimated | Pays back in 119 years |
14 more, on the Qwen3 14B page.
Qwen3 14B is also head to head with Mistral Small 3.2 24B Instruct above it on the leaderboard. One more model needs much the same memory: Ministral 3 14B. Llama 4 Scout 17B-16E is also head to head with Ministral 3 14B below it on the leaderboard. One more model needs much the same memory: gpt-oss-120b.
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 Qwen3 14B · every machine that runs Llama 4 Scout 17B-16E · every other match-up · both against the frontier · the quickest pay-back at each level of use