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Cohere Releases North Small Translate: A 218B MoE Translation Model That Scores 83.6 on WMT26 Across 50 Languages

Cohere released North Small Translate, an open-weight 218B MoE (25B active) translation model scoring 83.6 on WMT26 across 50 languages.

Cohere and Cohere Labs released North Small Translate, a decoder-only sparse Mixture-of-Experts translation model with 218B total and 25B active parameters, 128 experts with 8 activated per token plus shared experts, and 16K-token input and output context. In Cohere's vendor-reported WMT26 evaluation, judged by GPT-5.6-Sol, it scores 83.6 averaged across 50 languages (84.36 in an agentic multi-pass mode), ahead of DeepL NextGen (81.37), Qwen 3.5 397B A17B (81.56), GLM 5.2 (76.50), and Google Translate (68.20). The model was built with RWS's Language Weaver team, post-trained specifically for translation, and reports 112 output tokens per second versus 81 for Gemma 4 31B, with long-document xCOMET-XL scores of 48.9 versus 21.3 for Google Translate. It is available free on Cohere's Chat V2 API until rate limits, with three self-hosting checkpoints including a 4-bit NVFP4 variant running on 1x B200 or 2x H100.

MarkTechPostSep 11, 06:57 UTC · 5d agoModel release

[AINews] NVIDIA buys HuggingFace for $13B, as OpenAI publishes their HF incident retro

Z.ai released open-weight GLM-5.3-Flash (320B/18B active, 1M context, MIT) while Nvidia confirmed buying Hugging Face for $13B.

Z.ai formally launched GLM-5.3-Flash, the model previously previewed as Ox Alpha: 320B total parameters with 18B active, a 1M-token context window, natively multimodal, MIT-licensed, and claimed on par with Claude Opus 4.8 on coding. Artificial Analysis scored it 57 on its Intelligence Index at $0.09 per task, roughly 7.5x cheaper than GLM-5.3, and it scored 84.3% on Terminal-Bench 2.1. Nvidia's $13B acquisition of Hugging Face (~80x its $150M ARR) was confirmed, nearly double its initial $7B January offer. The roundup also notes Qwen shipping an impressive Flash model on Chinese chips as part of a broader open-model narrative.

Latent SpaceAug 27, 01:50 UTC · 20d agoModel release1

[AINews] OpenAI to reach AGI bar by end-2026

OpenAI chief scientist Jakub Pachocki says unreleased Astra model meets the 'Automated AI Research Intern' goal; Altman expects internal AGI declaration by December 2026.

OpenAI chief scientist Jakub Pachocki says the unreleased Astra model fulfills the September 2026 'Automated AI Research Intern' target. Sam Altman told TIME he expects OpenAI to declare AGI achieved internally by December 2026. The roundup also covers Zhipu's GLM-5.3-Flash (320B total parameters, 18B active, 1M context), Google's Gemini Omni 1.1 Flash video model topping the Text-to-Video Arena, and the $399 open-source Microduck biped robot from Pollen Robotics and Hugging Face.

Latent SpaceAug 28, 07:12 UTC · 19d agoAI industry

[AINews] Hot Chips: OpenAI’s Jalapeño, Cerebras CS-5, Groq 3 LPX, Apple M6

OpenAI unveiled Jalapeno custom inference chip claiming 1.5-1.9x better perf-per-watt than NVIDIA GB200/GB300, deploying in-house by year-end.

At the 37th Hot Chips conference, OpenAI published first benchmark details for its custom Jalapeno inference chip, claiming 1.5-1.9x more work per watt, 1.7-3.6x lower end-to-end latency, and 2.1-4.1x higher interactive-workload performance versus NVIDIA GB200/GB300, with the 700W-rated part staying at or below 550W in tests. Deployment into OpenAI's own infrastructure begins by year-end, with Gen 2 deep in development and Gen 3 underway. OpenAI also said GPT-Astra and Codex helped write low-level kernels, reportedly 1.5-1.8x faster than human-expert code for selected attention and MoE blocks. Cerebras CS-5, Groq 3 LPX and Apple M6 were also featured at the conference.

Latent SpaceAug 27, 01:31 UTC · 20d agoAI industry

[AINews] DeepSeek v4.1-Flash: 763B-P8B-D16B novel causal Encoder–Decoder architecture with vision marks the Return of the Whale

DeepSeek released V4.1-Flash, an open-weight 763B-parameter model with a novel causal encoder-decoder architecture, 1M context, vision input, and MIT license.

DeepSeek launched V4.1-Flash, an open-weight MIT-licensed model using a novel causal encoder-decoder architecture with 763B total parameters and asymmetric active parameters: 8B for prefill and 16B for decode. It supports 1M-token context and text+image input, priced at $0.30 per 1M input and $1.20 per 1M output tokens with a 50% off-peak discount. Artificial Analysis scored it 40 on its Intelligence Index, above DeepSeek V4 Pro 0813, and Vals ranked it the #1 open-weight model ahead of Kimi K3. Baseten shipped day-0 support and Ollama began rolling it out to paid subscribers.

Latent SpaceSep 12, 05:56 UTC · 4d agoModel release 7 sources1