Leaderboard
Small language models, ranked.
The field below lists transformer-based small language models from the Intelligence Index leaderboard, with the top three, the models immediately around Myosotis-1, and Myosotis-1 itself highlighted for comparison.
Intelligence Index — small LM landscape
145 entries · sorted by index ↓
| Rank | Model | Params | Int index | HellaSwag (%) | ARC-easy (%) | ARC-challenge (%) | PIQA (%) | ArithMark-3 (%) |
|---|---|---|---|---|---|---|---|---|
| 1 | SmolLM2-135MHuggingFace · 135M | 135M | 27.13 | 43.22 | 58.63 | 29.69 | 68.44 | 39.20 |
| 2 | SmolLM-135MHuggingFace · 135M | 135M | 25.74 | 42.70 | 56.31 | 29.01 | 68.28 | 36.80 |
| 3 | GPT-X2.5-135MAxiomic Labs · 135M | 135M | 25.17 | 40.57 | 51.81 | 29.18 | 69.42 | 38.40 |
| ⋮ · 85 models omitted (ranks 4–88) · ⋮ | ||||||||
| 89 | GPT-S-5MAxiomic Labs · 5.2M | 5.2M | 6.90 | 27.46 | 33.21 | 21.16 | 57.24 | 30.20 |
| 90 | tinyLM-8M-expUser01110 · 8.1M | 8.1M | 6.82 | 27.65 | 35.65 | 23.61 | 56.51 | 27.50 |
| 91 | Myosotis-1-base◈ our first modelFWKV · recurrent | 101m | 6.82 | 26.21 | 34.34 | 21.59 | 55.33 | 35.00 |
| 92 | Atom 3.4MUCR · 3.4M | 3.4M | 6.37 | 27.69 | 33.33 | 22.10 | 55.17 | 31.60 |
| 93 | Pythia-14MEleutherAI · 14M | 14M | 6.25 | 26.20 | 32.28 | 20.99 | 55.88 | 33.40 |
| ⋮ · 52 models omitted (ranks 94–145) · ⋮ | ||||||||
Percentages are raw scores on each benchmark. Myosotis-1 uses acc_norm for HellaSwag and ARC and acc for PIQA, matching the leaderboard convention. ArithMark-3 was not evaluated, so its cell and its weight are dropped from the index.
Methodology
How the Intelligence Index is calculated
Each benchmark is first adjusted for its random-chance floor, so chance performance maps to 0 and perfect performance maps to 100.
N(score, chance) = 100 × (score − chance) ÷ (100 − chance)
Int Index = (HellaSwag + ARC + PIQA + 0.65 × ArithMark-3) ÷ 3.65
| Benchmark | Weight | Chance | Notes |
|---|---|---|---|
| HellaSwag | 1.00 | 25 | acc_norm |
| Combined ARC | 1.00 | 25 | mean of ARC-Easy + ARC-Challenge, before normalization |
| PIQA | 1.00 | 50 | acc |
| ArithMark-3 | 0.65 | 25 | not evaluated for Myosotis-1 → shown as “—” |
- Combined ARC is the mean of ARC-Easy and ARC-Challenge before normalization.
- When a component is unavailable, its weight is removed from both the numerator and the denominator; at least two components are required.
- Scores below chance may be negative.
- Myosotis-1 was not evaluated on ArithMark-3, so its 0.65 weight is dropped: (HellaSwag + ARC + PIQA) ÷ 3 = 6.71.
- Myosotis-1 is a recurrent FWKV model, shown on a leaderboard of transformer-based models for comparison.