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The LLM benchmark aggregator.
SmolLM2 1.7B Instruct
Hugging Face · 1.7B
Strongest in Jailbreak resistance (#186 of 268), weakest in Human preference (#334 of 354). Above par in 0 of 8 scopes. Among the models it meets almost everywhere, it finishes behind Claude Opus 5.5 and Claude Opus 4.7 and ahead of Gemma 2B IT and PowerLM 3B.
§ 1 · Profile
What it is good at
Bars run from 50 — the average of the models each source lists — so right of the line is above par. The middle column is the gap to whoever leads that scope.
Safety40.5−20.9#317/3291/3
Jailbreak resistance48.2−16.8#186/2681/1
Harm refusal45.1−16.4#238/2921/2
Toxicity avoidance33.9−25.1#247/2681/1
Secure code26−40.2#268/2701/1
Fairness38.5−31.5#274/2921/2
Human preference29.3−39.5#334/3541/1
Human preference29.3−39.5#334/3541/1
§ 2 · Head to head
What it beats, and what beats it
The same models turn up scope after scope. Counted once: where both were placed, who finished higher. Bars run right for SmolLM2 1.7B Instruct, left for the other.
§ 3 · Sources
Where the numbers come from
2 publications, 7 figures. Every one links to the page it was read from.
LMArena Text 1115
Enkrypt · Jailbreak risk 14.3%Enkrypt · Harmful content risk 45.6%Enkrypt · CBRN risk 21.5%Enkrypt · Toxicity risk 17.2%Enkrypt · Bias risk 95.6%Enkrypt · Insecure code risk 94.2%
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[](https://publicai.io/model-index/m/smollm2-1-7b-instruct)