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AIWG Supported Models

Canonical reference for models tested with AIWG. Cloud models are built-in; local models are validated by the AIWG team or community contributors.

Cloud Providers (Built-in)

These models are fully supported and configured automatically when using AIWG with cloud providers.

Anthropic

Model AIWG Tier Context Tool Use Status
claude-opus-4-6 opus 200K Yes Fully supported
claude-sonnet-4-6 sonnet 200K Yes Fully supported
claude-haiku-4-5-20251001 haiku 200K Yes Fully supported

OpenAI

Model AIWG Tier Context Tool Use Status
gpt-5.4 opus 256K Yes Fully supported
gpt-5.3-codex sonnet 256K Yes Fully supported
gpt-5.1-codex-mini haiku 192K Yes Fully supported

Google

Model AIWG Tier Context Tool Use Status
gemini-2.5-pro opus 1M Yes Fully supported
gemini-2.5-flash sonnet 1M Yes Fully supported

Local Models — Validated

Models tested with AIWG by the team or community, with confirmed compatibility results.

Model Size Backend AIWG Tier Tool Use Coding Reasoning SDLC Workflows Tested By Notes
qwen3.5:9b 9B Ollama sonnet Yes Good Good Partial Community Strong coding, vision support (256K context), reasoning degrades in long chains
hermes-3-llama-3.1:8b 8B Ollama haiku Yes Good Moderate Limited Community Good instruction following, limited multi-step planning
codellama:34b 34B Ollama sonnet No Excellent Moderate Limited AIWG team Strong code gen, no native tool use support
qwen2.5-coder:14b 14B Ollama sonnet Yes Excellent Moderate Partial AIWG team Best coding quality per VRAM of validated local models
llama3.3:70b 70B Ollama/vLLM opus Partial Good Good Partial AIWG team Requires 48GB+ VRAM; closest to cloud quality
llama3.2:3b 3B Ollama haiku No Basic Basic No AIWG team Fast summaries and formatting only
llama3.1:8b 8B Ollama haiku No Moderate Moderate No AIWG team General purpose, budget tier
codellama:13b 13B Ollama haiku No Good Basic No AIWG team Budget code generation

Capability Legend

  • Yes — Works reliably in testing
  • Partial — Works with limitations (see Notes)
  • No — Not supported or not functional
  • Good/Moderate/Basic/Excellent — Relative quality rating

Status Definitions

Status Meaning
Fully supported Built into AIWG, tested with every release
AIWG team tested Validated by maintainers, not regression-tested
Community tested Reported working by community contributor (see notes for details)
Expected compatible Should work based on architecture, not formally tested

Local Models — Expected Compatible (Untested)

Models that should work with AIWG based on architecture and capabilities, but haven't been formally validated.

Model Size Expected Tier Notes
deepseek-coder-v2:16b 16B sonnet MoE architecture, strong coding benchmarks
mistral:7b 7B haiku Good instruction following
mixtral:8x7b 47B (MoE) sonnet MoE architecture, good general capability
phi-4:14b 14B sonnet Microsoft, strong reasoning for size
gemma2:27b 27B sonnet Google, good structured output
command-r:35b 35B sonnet Cohere, tool use support

Tier Mapping Guide

When configuring local models, map them to AIWG tiers based on their demonstrated capabilities:

AIWG Tier Expected Capability Typical Model Size
opus Complex reasoning, architecture review, multi-step planning 70B+
sonnet Code generation, debugging, structured output, moderate reasoning 13B–34B
haiku Summaries, formatting, simple transformations, fast tasks 3B–8B

Example models.json for Local Setup

{
  "openai": {
    "reasoning": {
      "model": "qwen3.5:9b",
      "description": "Local Qwen 3.5 for coding and reasoning"
    },
    "coding": {
      "model": "qwen2.5-coder:14b",
      "description": "Local Qwen 2.5 Coder for implementation"
    },
    "efficiency": {
      "model": "llama3.2:3b",
      "description": "Local 3B for quick tasks"
    }
  },
  "shorthand": {
    "opus": "qwen3.5:9b",
    "sonnet": "qwen2.5-coder:14b",
    "haiku": "llama3.2:3b"
  }
}

Contributing Model Test Results

See Community Model Testing Guide for how to submit your own model test results.

See Also