# Sampling Inkling and the Alias Pattern > Source: > Published: 2026-08-02 00:00:00+00:00 In July I wrote [The Model Sheet](/blog/choosing-an-llm) — how a filename like `Qwen2.5-Coder-32B-Instruct-Q5_K_M.gguf` breaks into family, domain, params, training stage, quantization, and format. Those fields predict behavior before you download. What comes after the spec sheet is the trial: load the weights, run a real prompt, see if the model behaves as advertised. Lately that trial has been Thinking Machines' Inkling: 975B total / 41B active MoE, Apache 2.0, 1M context. Most modern open-weight releases now ship with the OpenAI messages format (`system` / `user` / `assistant` roles with `content` arrays). That is the only reason the `claude-inkling` wrapper works. No translation layer needed — just point `ANTHROPIC_BASE_URL` at the endpoint. The Baseten route (and the rate limit that killed it) Baseten's direct endpoint (`https://inference.baseten.co` ) has a native `/v1/messages` route. For a verified workspace, it skips the Basic-tier rate cap (100k tokens/min) — a single Claude Code turn exhausts that instantly. The key lives at `~/.config/baseten/key` . The wrapper (`~/.local/bin/claude-inkling` ) reads it, exports the endpoint and model identifier (`thinkingmachines/inkling` or `inkling-small` ), then executes `claude` . In early trials we hit the cap immediately. Reading files with Inkling Large consumed tokens fast enough that the rate limit interrupted work before anything finished. The fix was temporary: switch back to the [Vercel AI Gateway](/blog/we-dont-want-ai-pets-fix-your-connectors) (`claude-gateway` ) to complete the task. The Gateway pools through a single bill with provider failover, so the interruption was minimal. Once the workspace is verified or a dedicated deployment is provisioned, Baseten direct is the stable path. For now the wrapper supports both: `INKLING_VIA=baseten` for direct, default for Gateway. The alias: `claude-inkling` The wrapper is a thin bash script. It exports Anthropic endpoint variables, disables telemetry and non-essential model calls (so local decode isn't wasted on background tasks), sets a generous output budget (32k by default), and executes `claude` . Same pattern as `claude-laguna` (local `llama-server` ) and `claude-deepseek` (local DeepSeek V4-Flash via `ds4` ). One alias per model, one endpoint per alias, zero proxy overhead. Because Inkling — like most new releases — speaks the OpenAI messages format, the client sends `{"model":"thinkingmachines/inkling","messages":[...]}` and gets back `{"type":"message","content":"..."}` . The alias just bridges Anthropic's CLI naming convention and the provider's model registry. What the trial looks like Sampling is not a benchmark run. It is a short, representative task from real work — a coding snippet, a reasoning chain, a format conversion — scored pass or fail against a known answer. The model either produces working output or it doesn't. Binary. I keep the prompt set in a plain file under version control. The next release has to earn the swap. Inkling's behavior so far aligns with its MoE spec: 41B active out of 975B total. Fast prefill, slower decode (bandwidth-bound, like any large model), and a generous output budget needed for reasoning spans before visible answers appear. That matches other reasoning-model releases — if `max_tokens` is too small, the budget goes to hidden thinking and the content field returns empty. Fix: large token budget, or a non-reasoning variant for short replies. This connects back to [The Benchmark](/blog/the-benchmark): a leaderboard rank collapses too many dimensions into one number. The fields from [The Model Sheet](/blog/choosing-an-llm) — dense vs MoE, active params, quantization level, training stage — are the actual variables. The alias wrapper just makes them accessible to the CLI I already use. Where this fits: the local lab keeps Llama-3.3-70B (storyteller) and Qwen2.5-Coder-32B (architect) hot on Metal via `llama-server` (`llama70` :8080, `coder32` :8081). The `claude-inkling` alias is the hosted complement: frontier reasoning or very long context goes through the Gateway; once verified, Baseten direct takes over. Next: finish the Baseten workspace verification so direct routing replaces the Gateway fallback, and update the prompt set file with the Inkling trial results. See the running notes in [ ~/llm-lab/session-log.md](/llm-lab/session-log.md) for the live measurements.