Grok Build 0.1: The Coding Model Behind the grok-code-fast Aliases
Grok Build 0.1 now answers to the grok-code-fast-1 aliases. What the 256K agentic coding model offers, how its two price tiers work, and how to test it.
ReadPractical writing for developers building with large language models — how they work, how to pick one, and how to keep the bill predictable.
Grok Build 0.1 now answers to the grok-code-fast-1 aliases. What the 256K agentic coding model offers, how its two price tiers work, and how to test it.
ReadHermes 3 405B is Nous Research's full fine-tune of Llama 3.1 405B, built to follow the system prompt. Where it beats stock Llama, where it trails, and how to prompt it.
ReadHermes 3 70B swaps some of Llama 3.1 70B Instruct's maths and format-following for steerability. The scorecard, the 8K training caveat, and when to pick 405B instead.
ReadNous Research turned Llama 3.1 405B into a hybrid reasoner that rarely refuses. What the thinking toggle changes, what a dense 405B costs, and when it still fits.
ReadAn 80B MoE with 13B active, dual thinking modes and a licence that excludes the EU, UK and South Korea. What Hunyuan-A13B is, and where it still fits in late 2026.
ReadTencent's smallest Hy-MT2 model is a dense 1.8B translator, Apache 2.0, that quantises to 440 MB. What it is good for, what an 8K window costs you, and when to size up.
ReadTencent says its flagship translation MoE outperforms DeepSeek V4 Pro and Kimi K2.6 at translation. How to read that claim, what 3B active costs, and how to test it.
ReadHy-MT2-7B is Tencent's mid-size translator, with seven prompt templates for glossaries, style and structured data. How to use them, self-host it, and pick it.
ReadHy3 is a 295B MoE with 21B active and a 256K window. The final release improved on the preview and dropped its restrictive licence for Apache 2.0. What changed and why.
ReadHy4 preview is a 770B MoE with 49B active, a 1M window and Apache 2.0 weights. What it scores against Hy3, why it reasons by default, and how to plan around a preview.
ReadInkling is a 975B-total, 41B-active open-weight MoE that reads text, images and audio under Apache 2.0. What it does well, what it costs, and why Small may beat it.
ReadInkling Small is a 276B-total, 12B-active open model that beats its larger sibling on most coding and reasoning scores. Where it wins, where it loses, and what it costs.
ReadShowing 145–156 of 731 articles