arabic

Cohere and Cohere Labs released Cohere Transcribe Arabic, a 2B parameter automatic speech recognition model optimized for Arabic dialects and Arabic-English code-switching. The open-source model achieves a 25.9% average word error rate across major Arabic ASR benchmarks, outperforming models up to 30B parameters.

Mistral AI has released Saba, a 24B-parameter model trained specifically for Arabic and South Asian languages including Tamil. The model runs on single-GPU systems at over 150 tokens per second and is available via API or for on-premises deployment.

TII UAE released QIMMA, an Arabic LLM leaderboard that validates benchmark quality before evaluating models. The validation pipeline, using Qwen3-235B and DeepSeek-V3 plus human review, discarded 3.1% of ArabicMMLU samples and found systematic quality issues across 14 benchmarks.