标签：#Engineering

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

Aaron Erickson discusses the evolution of AI workflows, shifting from "vibe checking" to building reliable, multi-agent frameworks. He explains how to combine deterministic software guardrails with agentic discovery, optimize agent hierarchies, leverage time-series foundation models, and implement rigorous evaluation pyramids to ensure architecture scales effectively in production. By Aaron Erickson

Deep Research Agentic Systems are AI Agents designed to conduct multi-step research for complex tasks using dynamic reasoning, multi-hop information retrieval, and generate structured analytical reports. Sarang Kulkarni from Thoughtworks spoke at Arc of AI Conference 2026 on how to deploy multi-agent research systems for deep reasoning, and the lessons learned from developing Deep Research Agents. By Srini Penchikala

See how OpenAI, Thrive, and Crete built a self-improving tax agent with Codex, automating filings, improving accuracy, and accelerating workflows.

This article explores Kafka's transition toward a cloud-native architecture, examining how tiered storage, FinOps telemetry, elastic consumer scaling, virtual clusters, and Share Groups reshape the operational and economic model of event streaming platforms. It also analyzes emerging diskless-storage proposals and their architectural trade-offs. By Viquar Khan

Microsoft has introduced a new AI-driven vulnerability discovery system called MDASH, a multi-model agentic security platform designed to automate large-scale code auditing across Windows and other Microsoft software environments. The system combines more than 100 specialized AI agents that work together to scan, validate, debate, and prove vulnerabilities across complex codebases. By Robert Krzaczyński

Sergiu Petean discusses the strategic journey of evolving DevOps into platform engineering within heavily regulated enterprise environments. He explains how to maximize efficiency using dynamic reference architectures, align platform KPIs directly with board-level business goals, reduce cognitive load via custom team topologies, and maintain innovation sovereignty through open-source technology. By Sergiu Petean

Over the past few years, my workflow as an engineer has changed a lot. I went from the occasional...

How the GitHub Issues team used client-side caching, smart prefetching, and service workers to make navigation feel instant. The post From latency to instant: Modernizing GitHub Issues navigation performance appeared first on The GitHub Blog .

Learn how OpenAI built a safe, effective sandbox to enable Codex on Windows with controlled file access and network limits.