Model guide
Which model should you run?
GIDE is model-agnostic in a literal sense: the bundled runtime loads roughly 115 architectures, and the agent drives all of them through the same plan gate, write gate and audit log. What changes between models is how they talk to tools, and that is what this page is about.
Start here
Pick by the job, not the leaderboard
Four situations cover almost everything people actually do. The harness is identical in each, only the engine changes.
Everyday agentic coding
Qwen3.6 · Ornith 9B · Mistral 3, at 9B or above
Tier 1 families at or above the size floor: the pipeline speaks their own tool dialect, so the loop runs grammar-off and the model stays in the format it was trained on.
Hard planning and refactors
Claude · GPT · Gemini, on your key
Any frontier model works. Plan the difficult turn with one, then hand the mechanical loop back to a local model so the long tail of edits costs nothing.
Air-gapped or regulated work
Ornith 9B, bundled
Ships inside the app and runs on first launch with no network at all. Every edit still passes the write gate and lands in the local audit log.
The best local output you can fit
GLM-5.x · DeepSeek V4 · gpt-oss 20B
If the memory is there, the larger tier 1 families are the strongest local coders. Check the memory matrix before committing to a download.
Local · tier one
Models that speak tools natively
Their GGUF template carries a tool format the pipeline recognises, so the agent runs grammar-off and the model answers in the dialect it was trained on. This is the strongest local path.
Qwen
Qwen2.5 · Qwen3 · Qwen3.5 / 3.6 (+MoE) · Qwen3-Next · QwQ · Qwen-Coder
dialect: hermes
Mistral
Mistral Small / Nemo · Mistral 3 (incl. Medium 3.5) · Large 3 · Mixtral · Ministral
dialect: [TOOL_CALLS]
DeepSeek
V3 / V3.x · V4 (incl. V4-Pro) · R1 (full MoE) · R1-Distills
dialect: deepseek markers
Kimi
Kimi K2 · K2.x · Kimi-Linear
dialect: deepseek markers
gpt-oss
gpt-oss 20B · gpt-oss 120B
dialect: harmony
Seed-OSS
Seed-OSS · Seed-Coder · Functionary
dialect: hermes
Hermes fine-tunes
Nous-Hermes · OpenHermes · most modern agent fine-tunes
dialect: hermes
GLM & Command-R
GLM-4 / 4.5 / 4.6 / 5.x (+MoE) · Command-R / R7B / Command-A
dialect: native or clean fallback, depending on the quant's template
One caveat worth stating: “native” means the pipeline speaks their format. A 7–8B model can still drift out of its own format under pressure. That is what the fallback chain exists for, and why output quality tracks the model, not the harness.
Local · tier two
Models the grammar drives
No native tool format in the template, so tool calls go through an enforced-JSON grammar instead. They still run the agent. The dialect is simply ours rather than theirs.
Gemma
Gemma 2 / 3 / 3n / 4 (+MoE)
Phi
Phi 2 / 3 / 3.5 / 4 (+MoE)
Granite
Granite 3.x · 4.0 / 4.1, including the MoE and hybrid variants
OLMo
OLMo · OLMo-2 · OLMoE
Nemotron
Nemotron · Nemotron-H (+MoE)
EXAONE
EXAONE 3 / 4 (+MoE)
OpenELM
OpenELM
ERNIE
ERNIE 4.5
Hunyuan
Hunyuan
Arctic
Snowflake Arctic
DBRX
DBRX
Grok
Grok-1 · Grok-2
And the rest
SmolLM3 · StableLM · InternLM2 · MiniCPM · Falcon / Falcon-H1 · StarCoder 1 / 2 · CodeShell · Yi · Vicuna · TinyLlama · CodeLlama · ChatGLM · Baichuan · Jais · Orion · XVERSE · PLaMo 2 / 3 · Arcee · Apertus · dots.llm1 · Ling / Bailing MoE 2 · LFM2 / LFM2-MoE (Liquid) · MiniMax-M2 · BitNet
State-space & hybrid
Mamba · Mamba-2 · Jamba · RWKV-6 / 7
Diffusion language models
LLaDA · LLaDA-MoE · Dream
These load too. That is what “any architecture” is worth in practice.
Online · your own key
Any frontier model works
Connect a provider with your own key and GIDE never sees a copy of it. The key goes to your macOS Keychain, or a permission-restricted file on Windows and Linux, and prompts go straight to the provider you chose.
Anthropic Claude
Claude Opus 5 · Sonnet 5 · Haiku 4.5
Any Claude your key can reach, by model ID
A full Messages-API adapter with tool_use block streaming. Modern Claudes are heavily tool-trained, which makes this the strongest online tool-calling path.
OpenAI GPT
GPT-5.6 Sol · Terra · Luna
Any model ID your key can reach
A Chat Completions adapter that reassembles streamed tool_calls fragments. Works with any model that does function calling on /chat/completions. Models exposed only on the newer Responses API are out of scope.
Google Gemini
Gemini 3.1 Pro · 3.6 Flash · 3.5 Flash-Lite
Any model ID your key can reach
Rides the same OpenAI-shaped adapter through Google's compatibility endpoint, with handling for Gemini's whole-arguments delta quirk.
…and anything else that speaks OpenAI
Point the adapter at any OpenAI-compatible base URL and it works the same way. That is most of the hosted world, and your own servers too.
The honest part
What it will not do
A support list is only useful if it also says where it stops.
Embedding and encoder models
BERT, nomic-embed, T5-encoder, gemma-embedding. They are in the architecture enum but they are not chat models, so they are no use to an agent.
The vision and audio halves of multimodal models
Qwen-VL, CogVLM, wavtokenizer and friends: the text side may load, but there is no image or audio input. The multimodal weights are dead weight.
Architectures newer than the bundled runtime
The lists above are bounded by llama.cpp b10068, not by what has been released. A point release that reuses its family's architecture loads as soon as the weights exist (Qwen3.6 rides Qwen3.5's, GLM-5.x rides GLM-4's) while a genuinely new architecture has to land in llama.cpp first and reach us on the next node-llama-cpp bump. Anything newer than that is still reachable through a hosted OpenAI-compatible endpoint on your own key.
And one about hardware
Being in the architecture list is not the same as fitting in your memory. The 70B dense models and the very large mixtures (DeepSeek-V3, Grok, DBRX, Arctic) load in principle and are refused in practice by the hardware-floor check, which is the correct outcome. The memory and context matrix has the numbers per machine.
Bring the model. We'll bring the harness.
Plan gate, write gate, memory, tools and the audit log behave identically whichever of these you choose.