gpt-oss-200b-goblin is an agentic coding model derived from GPT-OSS 120B.
Goblin expands the GPT-OSS 120B base with additional specialist MoE capacity for coding-agent workflows, repository work, SWE-style tasks, tool-using automation, and math-assisted reasoning.
This release continues the Goomba line with more SWE and sequential-agentic specialist capacity. It was trained on just two GPUs.
Overview
Base model: openai/gpt-oss-120b
Approx total parameters: 201B
Approx active parameters: 16.5B per token at top-k=16
Total expert rows: 224
Added specialist experts: 96
Format: MXFP4
Out-of-box active experts: top-k=16
Intended use: agentic coding, SWE-style workflows, repository exploration, tool-using automation, raw SWE coding, math-assisted coding
Status: research preview
Recommended vLLM
This model was primarily tested with vLLM using the GPT-OSS reasoning parser and OpenAI tool-call parser.
num_experts_per_tok=16 is already set in config.json
tensor-parallel-size=2
max-model-len=60000
gpu-memory-utilization=0.88
reasoning-parser=openai_gptoss
tool-call-parser=openai
enable-auto-tool-choice
The config ships with both num_experts_per_tok=16 and experts_per_token=16, so runtimes that respect the model config should use top-k 16 automatically. If your runtime overrides or ignores those fields, pass this explicitly:
--hf-overrides '{"num_experts_per_tok": 16}'
Tool Calling
Goblin was primarily tested as an agentic coding model. Basic OpenAI-compatible tool calling is expected to work best with the vLLM GPT-OSS reasoning parser and OpenAI tool-call parser enabled.
Suggested temperatures:
0.3 for steady coding-agent work
0.5 for broader agentic exploration
Recommended range: 0.3-0.5.
For repository exploration tasks, use an agent prompt that asks the model to inspect subdirectories, identify entry points, and summarize the project structure rather than stopping after a single directory listing.
License
Replace the placeholder license: other metadata with the actual license you want to publish under after confirming compatibility with the base model and your added weights.
