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GitGot is an OpenAI-compatible endpoint, so the provider is a thin OpenAI subclass with a base URL, registered the same way DeepInfra and OVHcloud are. It serves open-weight models that have few other providers — moonshotai/Kimi-K2.7-Code and Kimi-K2.6, deepseek-ai/DeepSeek-V4-Pro and -Flash, alongside openai/gpt-oss-120b and Llama 3.3 70B. gpt-oss-120b and Llama 3.3 70B already exist as model packages, so those gain a GitGot dimension rather than a second entry. Kimi K2.7 Code and DeepSeek V4 Flash are new. Covered by the existing createOpenAISubclassTests suite, which passes. The docs page notes that reasoning_effort behaves differently per model on this host — graded on gpt-oss, an on/off switch on the DeepSeek models, and ignored on the Kimi models — because a caller who assumes one shape across all of them gets a control that silently does nothing.
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What
Adds GitGot as a model provider. It is an OpenAI-compatible endpoint, so
core/llm/llms/GitGot.tsis a thinOpenAIsubclass with a base URL — the same shape asDeepInfra.tsand the other hosted OpenAI-compatible providers — plus the usual registrations inautodetect.ts,openai-adapters,config-types, the VS Code config schema, and the Add-New-Model UI.Why
It serves open-weight models that have few other providers:
moonshotai/Kimi-K2.7-Codemoonshotai/Kimi-K2.6deepseek-ai/DeepSeek-V4-Prodeepseek-ai/DeepSeek-V4-Flashopenai/gpt-oss-120bmeta-llama/Llama-3.3-70B-InstructKimi K2.7 Code in particular is a code-tuned model with a 262k context, which is the case for it in this extension specifically.
Model packages
gptOss120Bandllama3370bChatalready exist, so those gain a GitGot dimension rather than a duplicate entry.kimiK27CodeanddeepseekV4Flashare new.Reasoning controls
The docs page is explicit that
reasoning_effortbehaves differently per model on this host, because the models do:openai/gpt-oss-120b— graded. Meanreasoning_contentover three runs: low 283 chars, medium 469, high 1289.reasoning_contentwas empty on every run; with it present at any value, reasoning ran, and the value made no monotonic difference.All of them return reasoning in
reasoning_content. I mention it because a caller who assumes one shape across all six gets a control that silently does nothing.Tests
Added to the existing
createOpenAISubclassTestssuite rather than a new file:The models list with live prices and context limits is at
https://inference.gitgot.ai/v1/models, no key needed, if it is useful for verifying any of the above.