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Mibyan is designed to make strong Arabic professional assistance practical on a deliberately lean serving footprint. That is an engineering goal, not a claim that a foundation model is magically small or that every workload belongs on the same machine. The important distinction is between model capability and system efficiency. Users experience the latter: how quickly work starts, how consistently it streams, whether it recovers cleanly, and how much infrastructure is required to deliver an accepted result.

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What “lean” means here

We optimize the serving system as a whole rather than making an unqualified claim about a single hardware number.
The public API does not publish a fixed GPU, RAM, quantization, or provider configuration. Hardware is a deployment variable; the public promise is the measured service behavior and the reproducible protocol below.

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Why this matters

The largest deployment is not automatically the best deployment. For a focused Arabic workflow, the useful measure is quality per accepted outcome: the quality that survives review, divided by the time, tokens, and operational cost required to create it. This framing avoids two common errors:
  • Comparing parameter counts while ignoring latency, retries, output waste, and human editing.
  • Announcing a tiny hardware footprint without saying what concurrency, context length, quality setting, region, or failure rate it supports.

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Deployment profile: what we measure

Before calling a deployment profile “low-spec,” record the following alongside the result. This makes the claim auditable across environments.

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A responsible comparison

Mibyan should be compared with a frontier model on the same held-out Arabic workload, not by implying that either model is universally better. A frontier model can lead on breadth, multimodality, or difficult open-ended reasoning. Mibyan may be the better operational choice when Arabic terminology, structured business work, stable integration, and a lean controlled serving path are decisive. The comparison must hold constant the prompt, context, output cap, temperature, tools, retry policy, region, and scoring rubric. See the benchmark protocol for the full reporting contract.

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Operator checklist

  1. Start with a representative held-out Arabic workload, not demo prompts.
  2. Run a baseline with the intended concurrency and context limits.
  3. Capture latency, reliability, and quality together; do not optimize one in isolation.
  4. Classify every failure: timeout, missed constraint, bad Arabic, unsupported task, tool error, or factual error.
  5. Compare accepted outputs—not just raw tokens or a single leaderboard score.
  6. Publish a dated deployment manifest before turning an internal efficiency result into a public numerical claim.
A lean serving design does not remove the need for human review, retrieval from authoritative sources, application-level validation, or rate and budget controls.