Model comparison
Inkling-Small vs Llama 3.2 1B
Inkling-Small is the stronger model overall, scoring 46.5 to 19.9 on the Noometry Index. Llama 3.2 1B costs 9.0× less per token, which makes it the better buy when Inkling-Small's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
MetaSummary
- They share 18 benchmarks with published results for both. Inkling-Small scores higher in 8 categories and Llama 3.2 1B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Inkling-Small leads 48.3 to 7.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 90% for Inkling-Small and 0.6% for Llama 3.2 1B.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.45 / $1.20 for Inkling-Small.
- Inkling-Small accepts more context: 524K tokens versus 60K.
Side by side
| Inkling-Small | Llama 3.2 1B | |
|---|---|---|
| Provider | Thinking Machines Lab | Meta |
| Noometry Index | 46.5 | 19.9 |
| Released | 2026-07-15 | 2024-09-24 |
| Weights | Open | Open |
| Context window | 524K | 60K |
| Max output | 1.05M | 54K |
| Input $ / M tokens | $0.45 | $0.027 |
| Output $ / M tokens | $1.20 | $0.20 |
| Results tracked | 33 | 22 |
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Category by category
Coding Inkling-Small leads
Inkling-Small: 43.3 (#80), Llama 3.2 1B: 20.6 (#333)
| Benchmark | Inkling-Small | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1451 | 1071 |
| LMArena WebDev | 1408 | — |
| SciCode | 48.7% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
Agentic & Tool Use Not comparable
Inkling-Small: —, Llama 3.2 1B: 14.5 (#142)
| Benchmark | Inkling-Small | Llama 3.2 1B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 10.8% |
| BALROG | — | 6.6% |
Reasoning Inkling-Small leads
Inkling-Small: 39.0 (#53), Llama 3.2 1B: 15.6 (#304)
| Benchmark | Inkling-Small | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 18% | 0% |
| LMArena Hard Prompts | 1424 | 1044 |
| Epoch Capabilities Index | 150.13 | 101.95 |
| ARC-AGI-2 | 40.1% | — |
| ARC-AGI-1 | 84% | — |
| CritPt | 8.3% | — |
| Mystery Game Puzzles | 6% | — |
Math Inkling-Small leads
Inkling-Small: 44.9 (#65), Llama 3.2 1B: 9.7 (#308)
| Benchmark | Inkling-Small | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 90% | 0.6% |
| LMArena Math | 1453 | 1086 |
| FrontierMath (Tiers 1-3) | 46.3% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 6% | — |
Knowledge Inkling-Small leads
Inkling-Small: 48.3 (#71), Llama 3.2 1B: 7.2 (#307)
| Benchmark | Inkling-Small | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 88.5% | 23.9% |
| LMArena Expert | 1442 | 1007 |
| SimpleQA Verified | 19.1% | — |
Multimodal Not comparable
Inkling-Small: 38.4 (#59), Llama 3.2 1B: —
| Benchmark | Inkling-Small | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1235 | — |
Multilingual Inkling-Small leads
Inkling-Small: 51.7 (#102), Llama 3.2 1B: 23.9 (#287)
| Benchmark | Inkling-Small | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1400 | 973 |
| LMArena Chinese | 1458 | 958 |
| LMArena German | 1412 | 1014 |
| LMArena Russian | 1392 | 941 |
| LMArena French | 1438 | — |
| LMArena Japanese | 1399 | — |
| LMArena Korean | 1355 | — |
| LMArena Spanish | 1426 | — |
Instruction Following Inkling-Small leads
Inkling-Small: 73.9 (#112), Llama 3.2 1B: 52.6 (#285)
| Benchmark | Inkling-Small | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1397 | 1031 |
Long Context Inkling-Small leads
Inkling-Small: 42.7 (#113), Llama 3.2 1B: 31.9 (#269)
| Benchmark | Inkling-Small | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1401 | 1050 |
Writing & Preference Inkling-Small leads
Inkling-Small: 59.8 (#102), Llama 3.2 1B: 21.4 (#305)
| Benchmark | Inkling-Small | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1413 | 1055 |
| LMArena Creative Writing | 1330 | 1033 |
| EQ-Bench Creative Writing | 1491 | 200 |
| LMArena Multi-Turn | 1420 | 1030 |
Frequently asked questions
Is Inkling-Small better than Llama 3.2 1B?
Inkling-Small is the stronger model overall, scoring 46.5 to 19.9 on the Noometry Index. Llama 3.2 1B costs 9.0× less per token, which makes it the better buy when Inkling-Small's lead doesn't matter for your workload.
Which is cheaper, Inkling-Small or Llama 3.2 1B?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; Inkling-Small lists at $0.45 and $1.20.
Is Inkling-Small or Llama 3.2 1B better for coding?
Inkling-Small scores higher on coding benchmarks: 43.3 versus 20.6 in the Noometry coding category.
Which has the bigger context window?
Inkling-Small does, with 524K tokens against 60K.
How many benchmarks do Inkling-Small and Llama 3.2 1B share?
18 benchmarks have published results for both models. Inkling-Small has 33 scored results on Noometry and Llama 3.2 1B has 22.