Magic vs Ollama

Magic AI Claims Frontier-Level Pretraining for Under $1M

Magic, an AI startup co-founded by Austrians Eric Steinberger and Sebastian De Ro, claims a major breakthrough in pretraining efficiency. Its new recipe reportedly matches DeepSeek V4 Pro's base model quality using 50x less compute, costing about $500,000 on Nvidia GB200 systems, and is over ten times more compute-efficient than leading open-weight models. Scaling to roughly $4 million, Magic says it outperforms all public base models on perplexity evaluations, comparing against DeepSeek V4 Pro, Kimi K2, and Nvidia's Nemotron 3 Ultra, while excluding closed models from Anthropic, Google, and OpenAI. The company has raised over $460 million, with a $320 million round valuing it at $1.5 billion, and partners with Google Cloud for tens of thousands of GB200 chips. Magic has not yet released a model, and all claims are self-reported.

• Magic claims its pretraining recipe matches DeepSeek V4 Pro's quality with 50x less compute, costing ~$0.5M on GB200, and is 10x more efficient than leading open models.
• Scaling the recipe to ~$4M, Magic says it beats all public base models on perplexity evaluations.
• Magic has raised over $460M, with a $320M round valuing it at $1.5B, and partners with Google Cloud for GB200 compute.

DeepSeek Launches V4.1 Flash with Novel Encoder-Decoder Architecture

DeepSeek released DeepSeek-V4.1-Flash, a 763B-parameter mixture-of-experts model employing a novel causal encoder-decoder architecture with 8B active parameters for prefill and 16B for decode. It features native vision understanding, 1M token context, an MIT license, and extreme inference efficiency, claiming up to 1/8 KV cache footprint versus V4 Flash. Independent evals (Artificial Analysis Index 40, Vals Index #1 open-weight) show it surpasses V4 Pro at lower cost. API pricing is $0.30/1M input and $1.20/1M output tokens. DeepSeek has soft-retired V4 Pro, routing traffic to V4.1 Flash. The model supports SSD offload and local deployment, with Ollama and Baseten offering day-0 support. Technical discussions highlight the architecture's novelty and potential impact on long-context agents.

• DeepSeek launched V4.1-Flash with a causal encoder-decoder architecture, 763B total params (8B prefill/16B decode active).
• Artificial Analysis Index scores V4.1-Flash at 40, above V4 Pro and below GLM-5.3-Flash.
• API pricing: $0.30 per 1M input tokens, $1.20 per 1M output tokens, cached input $0.006 per 1M.

Ollama's transparent pricing

Ollama's Pro, Max, and Team plans now use industry-standard per-token pricing with usage included on every plan.

Developer Builds Autonomous AI Agent to Hunt Paid Bounties

A developer built an autonomous AI agent that scans hundreds of online gig/bounty listings, filters scams and human-only tasks, generates deliverables using live market data and a local LLM, and notifies a human for approval. The stack uses free tools (Python orchestration, Ollama with a local model, Chart.js, public crypto APIs, GitHub Pages, Windows Task Scheduler) resulting in $0/month infrastructure cost. In 48 hours the agent found many listings but only a handful were actionable due to geo-walls, ghost sponsors, and other filters; the author highlights the need for revenue tracking and human-in-the-loop oversight.

• Author built an autonomous AI agent that scans 232+ listings across multiple platforms to find paid work and generate deliverables.
• Stack used: Python orchestration, Ollama with qwen3:4b (local LLM), Chart.js, CoinGecko, DeFiLlama, Solana RPC, GitHub Pages, and Windows Task Scheduler.
• Total stated infrastructure cost: $0/month.