# Best local coding model by memory

URL: https://insidethemachine.ai/best/local-coding-model
Data: models 2026-10-15, apps 2026-10-15. Picks: the site's best_coding per memory tier in models.json.

| memory GB | model | needs GB | Terminal-Bench 2.1 | AA | run |
|---|---|---|---|---|---|
| 8 | none scored | - | - | - | - |
| 16 | Ornith 1.5 9B | 8.8 | - | - | `ollama run ornith-1.5:9b` |
| 24 | NousCoder 14B | 12.3 | - | - | - |
| 32 | Qwen3.8 27B | 22.7 | 79.8% | 33.7 | `ollama run qwen3.8:27b` |
| 48 | Qwen3.8 27B | 22.7 | 79.8% | 33.7 | `ollama run qwen3.8:27b` |
| 64 | Qwen3.8 27B | 22.7 | 79.8% | 33.7 | `ollama run qwen3.8:27b` |
| 96 | Qwen3.8 27B | 22.7 | 79.8% | 33.7 | `ollama run qwen3.8:27b` |
| 128 | Qwen3.8 27B | 22.7 | 79.8% | 33.7 | `ollama run qwen3.8:27b` |
| 192 | Qwen3.8-Flash-Next (125B-A6B) | 140 | 86.1% | 39.8 | `ollama run qwen3.8-flash-next:125b-a6b` |
| 256 | Qwen3.8-Flash-Next (125B-A6B) | 140 | 86.1% | 39.8 | `ollama run qwen3.8-flash-next:125b-a6b` |
| 512 | GLM-5.3-Flash | 233 | 84.3% | 41.8 | `mlx_lm.generate --model mlx-community/GLM-5.3-Flash-4bit --prompt 'hello'` |

## Apps that run them locally

- OpenCode (terminal, MIT): All-round local coding in the terminal. Local: `ollama launch opencode`. Needs 64,000 context. https://opencode.ai
- Codex CLI (terminal, Apache-2.0): OpenAI's agent on gpt-oss locally. Local: `ollama launch codex` or `codex --oss -m gpt-oss:120b`. Needs 64,000 context. https://github.com/openai/codex
- Zed (desktop app, GPL-3.0-or-later): Fast editor with a built-in agent. Local: In settings set the Ollama `"api_url": "http://localhost:11434"`; Zed discovers pulled models. https://zed.dev
- Cline (IDE extension, Apache-2.0): Local coding agent inside VS Code. Local: Pick provider "Ollama" with Base URL `http://localhost:11434`, then enable "Use Compact Prompt". https://cline.bot
- Goose (desktop app, Apache-2.0): Extensible agent, desktop app or CLI. Local: `goose configure` > Configure Providers > Ollama (defaults to `localhost:11434`). https://goose-docs.ai
- Kilo Code (IDE extension, MIT): VS Code and JetBrains agent with local models. Local: Settings > Providers > add Ollama (no API key), then set "Context Window Size (num_ctx)". Needs 32,000 context. https://kilo.ai

## Questions

**What is the best local coding model for 16 GB?**
Ornith 1.5 9B (needs 8.8 GB).

**What is the best local coding model for 128 GB?**
Qwen3.8 27B (needs 22.7 GB at 4-bit, 36.5 GB at 8-bit, Terminal-Bench 2.1 79.8%).

**Is a local coding model as good as Claude?**
Not yet. Qwen3.8 27B scores 33.7 on the Artificial Analysis index; Claude Opus 5.5 scores 57.6. That is about 59%. Good for everyday work; use a paid model for the hardest tasks.

**Which app should I use for local coding?**
OpenCode: All-round local coding in the terminal (ollama launch opencode). Codex CLI: OpenAI's agent on gpt-oss locally (ollama launch codex or codex --oss -m gpt-oss:120b). Zed: Fast editor with a built-in agent (In settings set the Ollama "api_url": "http://localhost:11434"; Zed discovers pulled models).

**How much context does a coding agent need?**
Coding apps send a lot of text. OpenCode asks for at least 64k tokens of context. More context needs more memory on top of the model size.