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InkubaLM vs SERENGETI

A verified, side-by-side comparison. Both records are status-checked by Wycord, so you are comparing what each actually offers today, not a stale listing.

InkubaLMSERENGETI
Category
AI Resources
AI Resources
Type
LLM
LLM
Country
🇿🇦 South Africa
🌍 Pan-African
Docs status
Docs live
Docs live
Licensing
Institutional only
Pricing
Free / open weights (CC BY-NC 4.0)
Free for research; commercial use requires contacting authors
Verified
Verified
Verified
Last verified
10 Oct 2026
10 Oct 2026
Tags
nlp, llm, african-languages, open-weights, low-resource
nlp, masked-language-model, 517-languages, afrocentric, ubc-nlp
Links
Website Docs GitHub
Website Docs GitHub
Summary
InkubaLM-0.4B is a 400M-parameter open-weights small language model built from scratch by Lelapa AI for five low-resource African languages (isiZulu, Yoruba, Swahili, isiXhosa, Hausa, plus English/French), using a LLaMA-style architecture trained on 2.4B tokens.
A massively multilingual masked language model covering 517 African languages and varieties across five scripts, achieving state-of-the-art results on the AfroNLU benchmark. Developed by the UBC Deep Learning and NLP Lab as an Afrocentric resource.

InkubaLM vs SERENGETI at a glance

InkubaLM and SERENGETI are both listed under LLM in Wycord's AI Resources index.

InkubaLM is based in South Africa and SERENGETI in Pan-African.

Both currently show the status "Docs live".

On access, InkubaLM is marked "No approval needed" and SERENGETI "Institutional only".

Over the last 30 days Wycord's probes measured 100% uptime for InkubaLM (36 checks) and 100% for SERENGETI (36 checks).

Average response time on live probes was 168 ms for InkubaLM and 162 ms for SERENGETI.

Uptime and latency come from Wycord's own probes of each resource's website, documentation and repository, run every six hours.

How they differ

  • Access: InkubaLM, No approval needed; SERENGETI, Institutional only.
  • Pricing: InkubaLM, Free / open weights (CC BY-NC 4.0); SERENGETI, Free for research; commercial use requires contacting authors.
  • Country: InkubaLM, South Africa; SERENGETI, Pan-African.
  • Average latency: InkubaLM, 168 ms; SERENGETI, 162 ms.

Topics

Both are tagged: nlp.

Only InkubaLM: llm, african-languages, open-weights, low-resource.

Only SERENGETI: masked-language-model, 517-languages, afrocentric, ubc-nlp.

Common questions

Which has better uptime, InkubaLM or SERENGETI?
They are level at 100% over the last 30 days.
Do InkubaLM or SERENGETI need approval before use?
InkubaLM is marked "No approval needed" and SERENGETI "Institutional only".
Is the documentation for InkubaLM and SERENGETI working?
Both show "Docs live" at the last check.
Where are InkubaLM and SERENGETI based?
InkubaLM is based in South Africa; SERENGETI is based in Pan-African.

More comparisons

Full details: InkubaLMFull details: SERENGETI