Ornith 1.0 Beats Claude Opus 4.7 — and It's Free #aimodel #coding

Ornith 1.0 Beats Claude Opus 4.7 — and It's Free #aimodel #coding

Open Earth: A New Contender in AI Coding Models

Introduction to Open Earth

  • An open-source model named Open Earth has matched Claude Opus in coding capabilities, available for download.
  • The model is 9 billion parameters and occupies 5 GB, making it suitable for laptops without requiring an API key or rate limits.
  • Open Earth's flagship version scores 77.5 on Terminal Bench and 82.4 on SweetBench verified.

Performance Comparison

  • In comparison, Claude Opus scores lower at 70.3 on Terminal Bench and 80.8 on SweetBench.
  • Open Earth outperforms other leading models of similar size like MiniMax and DeepSeek across multiple benchmarks.
  • It offers four different sizes: 9 billion, 31 billion, a mixture of experts at 35 billion, and a flagship model at 397 billion.

Unique Training Methodology

  • Unlike traditional models that use human-written harnesses for training, Open Earth allows the model to create its own scaffolding for tasks.
  • This self-directed approach includes monitoring mechanisms to prevent cheating by locking the environment and using a frozen judge to ensure integrity during training.

Model Specifications

  • The largest model (397 billion parameters) requires data center hardware; however, the smaller version (9 billion parameters) can run on personal laptops with a score of 69.4 on SweetBench verified.
  • All versions are MIT licensed, allowing commercial use and fine-tuning without restrictions.

Conclusion & Discussion Prompt

  • With Open Earth's competitive performance against Claude Opus, users are prompted to consider whether they would switch from closed models to this new open-source alternative.
Video description

Ornith-1.0: the open-source coding LLM that matches Claude Opus 4.7 — and runs on your laptop. This open-weights agentic-coding model hits 77.5 on Terminal-Bench and 82.4 on SWE-bench Verified, beating Claude Opus on both, then ships a 9B size you can `ollama run` locally. MIT licensed, four sizes, and it taught itself the harness. ---- 🚀 DYNAMOUS AI COMMUNITY Want to learn agentic coding with live daily events and workshops? Check out Dynamous AI: https://dynamous.ai/?code=646a60 Get 10% off here 👉 https://shorturl.smartcode.diy/dynamous_ai_10_percent_discount ⚡ HOSTINGER — RELIABLE HOSTING FOR YOUR PROJECTS (10% OFF) Whether you're shipping a portfolio, a side project, n8n flows, or AI agents — I use Hostinger for fast, affordable VPS + web hosting. Get 10% off here 👉 https://hostinger.com/DIYSMARTCODE (Affiliate link — costs you nothing, supports the channel.) ---- What you'll see in this 2-minute breakdown: → Ornith-1.0 — an open-source family of agentic-coding models from DeepReinforce → `ollama run ornith` — the 9B size is 5.6 GB, no API key, weights on your machine → Benchmarks vs Claude Opus 4.7: 77.5 vs 70.3 on Terminal-Bench, 82.4 vs 80.8 on SWE-bench Verified → Not cherry-picked: 78.9 multilingual SWE-bench, 77.1 ClawEval, clears Minimax & DeepSeek → Four sizes: 9B Dense, 31B Dense, 35B MoE, 397B MoE — one self-improving recipe → Self-scaffolding RL: the model writes its own harness, then the reward trains both → Reward-hacking defense: frozen environment + deterministic monitor + frozen LLM judge → The honest part: the 397B flagship needs datacenter hardware — the 9B runs on your laptop → MIT licensed, top to bottom: commercial use and fine-tuning, no strings The receipts: → Tech blog: https://deep-reinforce.com/ornith_1_0.html → Run it: https://ollama.com/library/ornith → Weights: https://huggingface.co/collections/deepreinforce-ai/ornith-10 → Announcement: https://x.com/ornith_ Open weights now trade blows with Claude on coding. So — would you switch your daily driver to an open model you run yourself, or stay closed? Drop your pick in the comments. #ornith #ornith1 #deepreinforce #opensourceai #agenticcoding #localllm #ollama #claudeopus #openweights #swebench #terminalbench #aimodel #codingai #llm2026 #mixtureofexperts #selfhostedai #aicoding #mitlicense #opensourcellm #diysmartcode