Topic
Open-Weight Models
9 stories on this topic, newest first.
ModelsAnalysis
Apertus 1.5 opens the recipe, including the parts that did not work
The multimodal release makes its tokenizer, training stages and limitations inspectable. Its most useful contribution is the ability to trace behaviour back to decisions.
ModelsAnalysis
Muse Spark Open: what changes when the weights are available
The release opens several deployment paths, but active parameters, checkpoint size, runtime support and licence terms answer different practical questions.
ResearchAnalysis
Can one small model inherit several specialists' skills?
A new distillation study combines teacher feedback with parameter priors. The result sharpens the question of how to consolidate expertise without losing weaker domains.
ModelsComparison
Best LLM for coding in October 2026: ten models ranked on the evidence
Opus 5.5, GPT-6.1 Sol, Gemini 4 Argon and seven more ranked on Terminal-Bench 4.0, DeepSWE and cost per task, with each score's harness and source.
ModelsExplainer
Self-hosted LLM guide for 2026: VRAM maths, Ollama vs vLLM and the break-even
When a self-hosted LLM pays off, how to size GPU memory for weights and KV cache, which inference server fits which job, and what the hardware really costs.
ToolsComparison
Top 5 AI gateways for routing between vLLM, Ollama and cloud LLMs in 2026
Five AI gateways ranked for hybrid inference: routing between self-hosted vLLM, SGLang and Ollama servers and cloud LLMs, with health checks and failover.
ModelsComparison
Top 10 AI models in October 2026, ranked by what they are good at
Claude Opus 5.5, GPT-6.1 Sol, Gemini 3.8 Flash, Kimi K3 and six more, ranked on independent evals, coding, context, price and speed, with every figure sourced and dated.
ModelsComparison
Top 10 open-weight models in October 2026, ranked for teams that self-host
MiMo-V2.6-Pro, GLM-5.3, DeepSeek V4.1 and seven more, ranked on independent evals, licence terms, active parameters, hardware and serving support, each read from its own model card.
ModelsAnalysis
Open weights trail the frontier by months. The economics say the gap will not close
Epoch AI measures the lag at four months; Stanford's AI Index puts it at 3.3% on Arena. Who pays for frontier runs, who gives weights away and why, and what that means for builders.
