Open models as coding tools: how they compare on the coding index

Developers are often the first team to ask for AI, and the team with the most sensitive data: your source code. Can open models running on your own GPU servers power the coding agents your developers want to use? This article compares the open models in the Loadcore library on the Artificial Analysis Coding Index, puts them next to hosted models, and shows how to connect them to opencode, aider and Continue.

The coding index in brief

The Artificial Analysis Coding Index combines several coding benchmarks into one score. Artificial Analysis runs the tests itself, so models are measured the same way instead of relying on each vendor's own numbers. That makes it a useful first filter. It does not replace testing on your own codebase.

The scores below are from the index as mirrored by BenchLM on 6 November 2026.

Open models in the Loadcore library

Two library models are not in the index yet:

• Qwen3 Coder Next reports 70.6% on SWE-bench Verified and 44.3% on SWE-bench Pro on its model card. These are different benchmarks, so do not compare them with the index scores above.
• DeepSeek V4.1 Flash is not listed. Its predecessor, DeepSeek V4 Flash 0731, scores 69.1.

The Nemotron score is for NVIDIA's NVFP4 build. The library offers the Q8_0 and BF16 GGUF files.

How they compare with hosted models

For reference, hosted models on the same index:

What stands out:

• GLM-5.3 is within 7 points of the best hosted model. It scores above Claude Sonnet 5 and GPT-5.4. The catch is size: even the 2-bit file is 253.9 GB, so it needs a dedicated multi-GPU server.
• Qwen3.8 27B is the best value. At 68.1 it is 3.4 points behind Claude Sonnet 5, and the 4-bit file fits on a single 24 GB card. For most teams this is where to start.
• Qwen3.8 Flash Next sits in between. It scores 73.0 with only 6B active parameters, so it is fast, but it needs about 111 GB of memory at 4-bit.
• General models score lower on coding. Gemma 4 and gpt-oss 120B are strong at writing, summarizing and reasoning, but trail on this index. Run them next to a coding model rather than instead of one.

Read the numbers with care

• Benchmarks are not your codebase. An index measures common tasks in common languages. Test on real tickets from your own repositories before you decide.
• Quantization costs a little. Index scores are measured on the full models. A 4-bit GGUF usually loses a little quality; 2-bit files lose more. Use the largest quantization your hardware allows.
• Context matters for agents. Coding agents send large prompts with files, tool definitions and history. Give the instance at least 64K tokens of context, more for large repositories, and leave GPU memory for the KV cache.
• Use the recommended sampling settings. Each model page lists the vendor's recommended temperature and sampling values. Wrong settings can make a good model look bad.

Connecting your coding tools

The Loadcore Agent serves an OpenAI-compatible API, so any coding tool that supports a custom OpenAI endpoint works. Each developer uses their own sk-lc- key, which keeps access and usage per person.

opencode (opencode.json):

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "loadcore": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Loadcore",
      "options": {
        "baseURL": "http://your-server:9090/v1",
        "apiKey": "{env:LOADCORE_API_KEY}"
      },
      "models": {
        "your-model.gguf": { "name": "Qwen3.8 27B" }
      }
    }
  }
}

aider:

export OPENAI_API_BASE=http://your-server:9090/v1
export OPENAI_API_KEY=sk-lc-your-key
aider --model openai/your-model.gguf

Continue and other editors: add an OpenAI-compatible provider with the same base URL, key and model file name.

Which model for which team

Whatever you pick, run a two-week pilot with a few developers and compare against the hosted tool they use today. How to give your team a private ChatGPT describes a staged rollout, and the self-hosted Claude alternative page covers the wider comparison.

Sources: Artificial Analysis leaderboards, BenchLM AA Coding Index mirror (6 November 2026), Qwen3-Coder-Next model card. Model sizes are the GGUF file sizes listed on each model page.