LA
LG AI Research
Language model · South Korea ·Dec 2024

EXAONE 3.5 2.4B

Language Open Weights
2.4B
Parameters
4
Benchmarks

About EXAONE 3.5 2.4B

EXAONE 3.5 2.4B is an AI model developed by LG AI Research, in the language category, released in 2024, made available as an open-weights model with 2.4B params.

On this page you'll find EXAONE 3.5 2.4B's full specifications, including 4 benchmark results. Review provider pricing and benchmark scores below, or compare EXAONE 3.5 2.4B head-to-head with other language models.

Links

Run EXAONE 3.5 2.4B locally — what to buy

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Sized to what EXAONE 3.5 2.4B actually needs (~8k context at Q4), not to the biggest GPU: Good = cheapest that runs it, Better = best value, Best = most headroom. Speed is a hardware estimate; anything past ~120 tok/s is shown as instant.

Software support

✓ measured · · compatible · — not supported. Informational only — speed is hardware-based.

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EXAONE 3.5 2.4B benchmark scores
Benchmark Category Score Variant
AlpacaEval 2 general 37.1% instruct-from-hf-readme
ArenaHard general 48.2% instruct-from-hf-readme
IFEval general 73.6% instruct-from-hf-readme
LiveBench general 33 instruct-from-hf-readme

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Frequently asked questions

What is EXAONE 3.5 2.4B?

EXAONE 3.5 2.4B is an AI model developed by LG AI Research, in the language category, released in 2024. It is tracked on GenAIList with its specifications, benchmark scores and provider pricing.

Who created EXAONE 3.5 2.4B?

EXAONE 3.5 2.4B was developed by LG AI Research and released in 2024.

Is EXAONE 3.5 2.4B open source or proprietary?

EXAONE 3.5 2.4B ships as an open-weights model: you can download and self-host the weights, though the licence may place some restrictions on use.

How much does EXAONE 3.5 2.4B cost?

Pricing for EXAONE 3.5 2.4B depends on the provider. See the providers table on this page for the latest API rates.

How does EXAONE 3.5 2.4B perform on benchmarks?

EXAONE 3.5 2.4B is benchmarked across 4 evaluations on GenAIList, including AlpacaEval 2 (37.1%). See the full benchmark table below and compare it with other models.

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