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How to Read 2026 Coding Benchmarks: SWE-bench, Terminal-Bench, DeepSWE, Aider Explained

The same model can score 20 points apart on different harnesses, 32% of SWE-bench Pro verifier judgments were found to be wrong, and DeepSWE's 113 tasks make most models score zero. This guide decodes six major coding benchmarks — what they test, which are easy to game, and which ones you should care about.

Why MoE Wins: The Architecture Behind Every 2026 Frontier Model

Nearly every frontier open-source model in 2026 is MoE: Ornith 35B activates only 3B to beat 31B dense models, MiniMax M3 uses 456B total but 45.9B active to hit SWE-bench Pro 59%, DeepSeek V4 runs 1.6T total with 49B active. This post explains why MoE dominates coding and agentic benchmarks using four case studies.

Open-Source AI Licensing Guide: What MIT, Apache 2.0, and Llama License Actually Allow

'Open-source' in AI doesn't mean what it means in software. MIT and Apache 2.0 let you do almost anything; the Llama License requires a separate deal above 700M MAU; old Gemma terms let Google change rules unilaterally (Gemma 4 switched to Apache 2.0). This guide maps what you can and can't do by license type.

MiniMax: The Chat App Company That Built a Coding Model to Rival Frontier Labs

MiniMax started as a consumer chat app company, then M2.5 scored 80.2% on SWE-bench Verified at 1/10-1/20 the cost of Claude Opus; M3 (456B total / 45.9B active) became the first open-weight model to clear 59% on SWE-bench Pro, with 1M context powered by their novel Sparse Attention mechanism.

Nous Research: From Research Collective to Open-Source AI Ecosystem Rebel

Nous Research doesn't pretrain — they fine-tune and do RL. Hermes 4 scores 96.3% on MATH-500, NousCoder-14B improves Qwen3-14B's coding ability by 7% using only 24K training samples. But the real moat is Hermes Agent: 236K GitHub stars, #19 globally, 3,000 contributors.

Ornith: The Open-Source Coding Dark Horse Built on Self-Improvement RL

DeepReinforce's Ornith 1.5 family, trained with self-improvement RL: the 397B flagship scores 86.0 on SWE-bench Verified, matching Claude Opus 4.8; the 35B-A3B activates only 3B parameters per token yet leads every coding benchmark in its class; the 9B runs on phones. MIT-licensed, fully open-source.

ai project

2026 Q1 Open-Source LLM Landscape: From Frontier Models to On-Device, a Complete Survey

2026 Q1 saw a full-blown open-source model explosion: on the LLM front, GLM-5, Kimi K2.5, and Qwen3.5 caught up with closed-source models; Embedding and Reranker are dominated by Qwen3 and BGE; speech has Voxtral TTS and Whisper V3; image has FLUX.2; and video has Wan 2.2 rivaling Sora. This is the complete navigation map.