Tiel-Coder 35B A3B

Tiel-Coder is a community agentic coding model built on Ornith-1.5-35B-A3B. The card reports 12 of 25 on SWE-bench-Live, level with Opus 4.6 medium and ahead of KAT-Coder and Nail. It is a community refinement, not a lab launch.

Compare KAT-Coder V2.5 if you wanted another agentic coding MoE, Qwen3.8-27B if you wanted the coding lineage, and GLM-5.3 if you wanted a hosted coding plan.

Key Features

MoE efficiency: 35B total, ~3B active per token, so it runs fast on one modest GPU while keeping coding quality.
22GB at 4-bit: fits in a single 24GB GPU (or a Mac with 36GB+ unified memory) via GGUF; MLX variants are also published.
Agentic coding template: a terse chat template tuned for tool loops and repo-level fixes, with a "think first, answer directly" system prompt.
Memory-efficient quant: imatrix + dynamic quantization (unsloth-dynamic) to cut size without a big quality drop.
Multi-format: GGUF for llama.cpp, plus MLX and MTP (multi-token prediction) variants.

Limitation: it is a single community refinement, not an Anthropic or Alibaba release. The "level with Opus 4.6" claim is a self-reported 12/25 on SWE-bench-Live, not an independent benchmark.

Model Specs

Spec	Value
Base model	Ornith-1.5-35B-A3B (Qwen3.6-35B-A3B lineage)
Parameters	~35B total / ~3B active (MoE)
Context	As the base; check the card for the exact window
4-bit size	~22 GB
License	MIT
Published	2026-08-19
Formats	GGUF, MLX, MTP

Use Cases

Local agentic coding on a single 24GB GPU with llama.cpp or the terse template.
Repo-level bug fixes where a fast, tool-calling model beats a big general model.
People who want open weights they can run offline and pin to a local harness.

Pricing

Free to download (MIT).

Path	Cost
Weights (GGUF/MLX)	$0, MIT
Local inference	Your GPU or Mac hardware

Getting Started

Pull the GGUF from huggingface.co/peculiar-ragdoll/Tiel-Coder-35B-A3B-GGUF.
Run it with llama.cpp or an Ollama-compatible loader.
Use the model's terse, tool-calling system prompt to keep agent loops lean.
Watch VRAM: ~22GB at 4-bit means a 24GB GPU or 36GB+ Mac.

First-party start: the GGUF model card.

Frequently Asked Questions

Is this an official lab model?

No. It is a community re-quant/refinement of Ornith-1.5-35B-A3B by peculiar-ragdoll.

Does it fit on my GPU?

22GB at 4-bit fits a 24GB card or a Mac with 36GB+ unified memory, at the cost of some speed.

Mind to run in a coding agent?

Yes. Its terse, tool-calling template is tuned for agent loops, so keep the system prompt.

Alternatives

KAT-Coder V2.5: 35B MoE agentic coding model, Apache 2.0.
GLM-5.3: hosted coding plan, not open weights.
Qwen3.8-27B: the coding lineage, larger open context.

Tips

Quote 22GB and the self-reported 12/25, not an audited benchmark.
For an actual repo fix, keep the terse template enabled.
Test on your exact GPU before committing local agent workflows.

Conclusion

Tiel-Coder is a ~22GB, MIT agentic coding MoE built on Ornith-1.5-35B-A3B, not a lab release. Start at the GGUF card, run it locally, then decide whether KAT-Coder V2.5 already covers the coding model you want.