标签：#LLM

Something broke in the AI pricing market between January and May 2026. A year ago, "frontier model" meant "expensive model." Claude Opus was $15/$75 per million tokens. GPT-4 was $5/$15. If you wanted the best coding performance, you paid the best price. The correlation between quality and cost was loose, but it existed. That correlation is gone. The Numbers That Changed Everything Here's SWE-bench Verified — the benchmark that tests AI models against real GitHub issues from projects like Django, Flask, and scikit-learn — plotted against output price per million tokens: Model SWE-bench Output $/1M Score/Dollar ───────────────────────────────────────────────────────────────── Claude Opus 4.7 87.6% $25.00 3.5 Claude Opus 4.6 80.8% $25.00 3.2 Gemini 3.1 Pro 80.6% $15.00 5.4 GPT-5.2 80.0% $10.00 8.0 DeepSeek V4 Pro (Max) 80.6% $3.48 23.2 Kimi K2.6 80.2% $4.00 20.1 Qwen3.6 Plus 78.8% $3.00 26.3 MiniMax M2.5 80.2% $1.20 66.8 DeepSeek V4 Flash (Max) 79.0% $0.28 282.1 Read that last line again. DeepSeek V4 Flash scores 79% on SWE-bench at $0.28 per million output tokens. Claude Opus 4.7 scores 87.6% at $25.00. The performance gap is 8.6 percentage points. The price gap is 89x . For a team running 100 million tokens per month, that's the difference between $28/month and $2,500/month. For a 9-point improvement in code completion accuracy. It's Not Just One Outlier This isn't a DeepSeek anomaly. Look at the cluster of models scoring 78-80% on SWE-bench: DeepSeek V4 Pro : $3.48/1M output — open source, 1M context Kimi K2.6 : $4.00/1M output — open source, 256K context MiniMax M2.5 : $1.20/1M output — open source, 200K context Qwen3.6 Plus : $3.00/1M output — open source, 1M context MiMo-V2-Pro : $3.00/1M output — open source, 1M context Five models from five different Chinese labs, all scoring within 2 points of GPT-5.2 ($10.00/1M) and Gemini 3.1 Pro ($15.00/1M), all at 1/3 to 1/10 the price. And they're all open source. What Happened Three things converged: 1. Mixture-of-Exper

Problem Statement We have a misinformation problem. But more specifically, we have a speed problem. A journalist spots a suspicious claim. They search for sources. Cross-reference databases. Call experts. Write a verdict. Get it edited. Publish, maybe 6 hours later. Maybe 3 days later. Meanwhile, the original claim has been screenshot, reposted, quoted in newsletters, and cited in arguments across five platforms. I wanted to build something that closed that gap. Not a chatbot that guesses. A proper pipeline, one that retrieves real evidence, reasons from it, and tells you why it reached a verdict. That's what Sift is. What is Sift? Sift (Source Inspection & Fact-checking Tool) is an open-source multi-agent AI pipeline that takes any text, extracts every factual claim, retrieves grounded evidence, and returns auditable verdicts — TRUE, FALSE, or UNCERTAIN, with cited sources and full reasoning chains. Paste a news article. A politician's speech. A viral statistic. A WhatsApp forward. Sift breaks it into individual claims and fact-checks each one independently. Why Multi-Agent? The naive approach is to ask an LLM: "Is this claim true?" The problem: LLMs hallucinate. They have knowledge cutoffs. They're confidently wrong in ways that are hard to detect. And critically, they don't show their work. A single LLM call can't reliably handle the full pipeline of: Extracting structured claims from noisy text Retrieving dated, traceable evidence from live sources Reasoning across conflicting evidence without confabulating Adversarially reviewing its own conclusions for overconfidence Finding corrections when something is wrong Each of these is a distinct task that benefits from its own prompt, its own tools, and its own failure modes. That's why I built five separate agents, orchestrated with LangGraph. The 5-Agent Pipeline Agent 1 — Claim Extractor A single paragraph can contain 4-5 distinct factual claims. Generic LLMs miss them or conflate them. This agent uses LLaMA 3.3 70

I built a bot trained on my own X bookmarks and likes. Around 50,000 of them, accumulated over years...

This is the first post of series Building TinyAgent where we are going to build a small agent from...