Ling 3.0 tiny vs Granite 4.2 8B
Ling 3.0 tiny and Granite 4.2 8B both score 12 on the intelligence index. The cheapest machine here that runs Granite 4.2 8B is the Mac mini M6, 24GB, at $1,099. Ling 3.0 tiny runs on the Mac mini M6, 16GB at $899, $200 less. On the Mac mini M6, 24GB, the cheapest machine here that runs both, Ling 3.0 tiny is about 4.2× quicker: 50 tok/s against 12, both estimated from memory bandwidth. At 500k tokens a day the Mac mini M6, 24GB pays for itself in 97 years running Ling 3.0 tiny, against 122 years running Granite 4.2 8B.
| Ling 3.0 tiny | Granite 4.2 8B | |
|---|---|---|
| Intelligence index | 12 | 12 |
| Class | Below every hosted tier | Below every hosted tier |
| Weights | 4.8 GB | 5.3 GB |
| Needs at 32k | 5.0 GB | 11 GB |
| Quantisation | Q4_K_M | Q4_K_M |
| Parameters | 7.9B (1.3B active) | 8B |
| Max context | 128k | 128k |
| API price per 1M | $0.06 in / $0.15 outpriced as Granite 4.2 8B | $0.06 in / $0.15 out |
| Licence | MIT | Apache 2.0 |
| Machines here that run it | 37 of 37 | 33 of 37 |
| Cheapest machine that runs it | Mac mini M6, 16GB $899 | Mac mini M6, 24GB $1,099 |
Run Ling 3.0 tiny on the Mac mini M6, 16GB · or Granite 4.2 8B on the Mac mini M6, 24GB
Jump to: Side by side on the Mac mini M6, 24GB · 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 Mac mini M6, 24GB
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 Mac mini M6, 24GB is the cheapest machine here that runs both, so this is the pair doing the same work on the same hardware.
| Ling 3.0 tiny | Granite 4.2 8B | |
|---|---|---|
| Speed at 32k | 50 tok/s estimated | 12 tok/s estimated |
| Pay-back on this machine | Pays back in 97 years | Pays back in 122 years |
| API cost per month | $1.00priced as Granite 4.2 8B | $1.00 |
Run Ling 3.0 tiny on the Mac mini M6, 24GB · or Granite 4.2 8B
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. Ling 3.0 tiny 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 | Ling 3.0 tiny | Granite 4.2 8B |
|---|---|---|
| 50ka few chats a day | 974 years | 1,216 years |
| 200klight assistant use | 243 years | 304 years |
| 1Ma moderate coding-assistant day | 49 years | 61 years |
| 4Mheavy coding with an agent | 12 years | 15 years |
| 20Magents running most of the day | 2.4 years | 3.7 yearsits ceiling |
The Mac mini M6, 24GB generates at most 16.4M tokens a day on Granite 4.2 8B, 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 Ling 3.0 tiny at 20M tokens a day · or Granite 4.2 8B
Machines that run one and not the other
Ling 3.0 tiny needs 5.0 GB of memory at 32k of context and Granite 4.2 8B needs 11 GB. That puts Ling 3.0 tiny on 4 of the 37 machines priced here that Granite 4.2 8B does not, starting at $899.
| Machine | Price | Memory | Speed on Ling 3.0 tiny | Pay-back |
|---|---|---|---|---|
| Mac mini M6, 16GB | $899 | 16 GB | 45 tok/s estimated | Pays back in 80 years |
| MacBook Air M5 (13-inch), 16GB | $1,299 | 16 GB | 45 tok/s estimated | Pays back in 116 years |
| MacBook Air M5 (15-inch), 16GB | $1,499 | 16 GB | 45 tok/s estimated | Pays back in 134 years |
| MacBook Pro M5 (14-inch), 16GB | $1,999 | 16 GB | 45 tok/s estimated | Pays back in 178 years |
Ling 3.0 tiny is also head to head with gpt-oss-120b above it on the leaderboard. Granite 4.2 8B is also head to head with GLM-4.5-Air below it on the leaderboard. One more model needs much the same memory: Qwen3 8B.
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 Ling 3.0 tiny · every machine that runs Granite 4.2 8B · every other match-up · both against the frontier · the quickest pay-back at each level of use