Lfm 2.5 1.2 B Instruct vs Lfm 2.5 1.2 B Thinking: cost comparison
Prices updated 2026-10-29 · computed from live provider data
Short answer: Lfm 2.5 1.2 B Thinking is cheaper on a
3:1 input/output blend — $0.028 per million tokens versus
$0.028, a 0% difference.
Both offer the same 32,768-token context window, so cost is the deciding factor.
Head to head
| Lfm 2.5 1.2 B Instruct | Lfm 2.5 1.2 B Thinking | |
|---|---|---|
| Input, per 1M tokens | $0.020 | $0.020 |
| Output, per 1M tokens | $0.050 | $0.050 |
| Blended 3:1, per 1M | $0.028 | $0.028 |
| Context window | 32,768 | 32,768 |
| Vendor | zerogpu | zerogpu |
Monthly cost at real volumes
| Workload (input / output) | Lfm 2.5 1.2 B Instruct | Lfm 2.5 1.2 B Thinking |
|---|---|---|
| 1M in / 250K out | $0.033 | $0.033 |
| 10M in / 2.5M out | $0.325 | $0.325 |
| 100M in / 25M out | $3.25 | $3.25 |
Which should you pick?
Because the context windows are identical, the cheaper option is the better default. Choose Lfm 2.5 1.2 B Thinking unless you are already standardised on zerogpu tooling and want to avoid a second integration.