Qwen3 14B vs LFM2.5 24B-A2B
Qwen3 14B scores 6 on the intelligence index, and LFM2.5 24B-A2B has not been placed on it. Both take the same machine to start: the cheapest here that runs either is the Mac mini M6, 24GB, at $1,099. On it, LFM2.5 24B-A2B is about 2.7× quicker: 24 tok/s against 8.9, both estimated from memory bandwidth. At 500k tokens a day the Mac mini M6, 24GB pays for itself in 70 years running Qwen3 14B, against 73 years running LFM2.5 24B-A2B.
| Qwen3 14B | LFM2.5 24B-A2B | |
|---|---|---|
| Intelligence index | 6 | not placed |
| Class | Below every hosted tier | not yet placed |
| Weights | 9.0 GB | 15 GB |
| Needs at 32k | 14 GB | 15 GB |
| Quantisation | Q4_K_M | Q4_K_M |
| Parameters | 14.8B | 24B (2.5B active) |
| Max context | 40k | 128k |
| API price per 1M | $0.1 in / $0.22 out | $0.05 in / $0.7 out |
| Licence | Apache 2.0 | LFM 1.0 (see repo) |
| Machines here that run it | 33 of 37 | 33 of 37 |
| Cheapest machine that runs it | Mac mini M6, 24GB $1,099 | Mac mini M6, 24GB $1,099 |
Run Qwen3 14B on the Mac mini M6, 24GB · or LFM2.5 24B-A2B
Jump to: Side by side on the Mac mini M6, 24GB · How much use it takes to pay for the machine · Memory is not what separates them · The assumptions behind both columns
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.
| Qwen3 14B | LFM2.5 24B-A2B | |
|---|---|---|
| Speed at 32k | 8.9 tok/s estimated | 24 tok/s estimated |
| Pay-back on this machine | Pays back in 70 years | Pays back in 73 years |
| API cost per month | $1.64 | $1.38 |
Run Qwen3 14B on the Mac mini M6, 24GB · or LFM2.5 24B-A2B
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. Qwen3 14B pays for it sooner at every level up to 4M tokens a day. Above that LFM2.5 24B-A2B does.
| A day's use | Qwen3 14B | LFM2.5 24B-A2B |
|---|---|---|
| 50ka few chats a day | 701 years | 729 years |
| 200klight assistant use | 175 years | 182 years |
| 1Ma moderate coding-assistant day | 35 years | 36 years |
| 4Mheavy coding with an agent | 8.8 years | 9.1 years |
| 20Magents running most of the day | 2.9 yearsits ceiling | 22 months |
The Mac mini M6, 24GB generates at most 12.3M 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 LFM2.5 24B-A2B
Memory is not what separates them
Qwen3 14B needs 14 GB of memory at 32k of context and LFM2.5 24B-A2B needs 15 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.
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. One more model needs much the same memory: Ministral 3 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 Qwen3 14B · every machine that runs LFM2.5 24B-A2B · every other match-up · both against the frontier · the quickest pay-back at each level of use