{
  "format": "minglabs/v1",
  "surface": "insights-answer",
  "slug": "how-do-you-choose-an-ai-model-for-a-task",
  "inLanguage": "en",
  "languages": [
    {
      "language": "en",
      "label": "English",
      "path": "/insights/answers/how-do-you-choose-an-ai-model-for-a-task",
      "url": "https://www.minglabs.com/insights/answers/how-do-you-choose-an-ai-model-for-a-task",
      "jsonUrl": "https://www.minglabs.com/insights/answers/how-do-you-choose-an-ai-model-for-a-task.json",
      "markdownUrl": "https://www.minglabs.com/insights/answers/how-do-you-choose-an-ai-model-for-a-task.md"
    },
    {
      "language": "de",
      "label": "Deutsch",
      "path": "/de/insights/answers/welches-ki-modell-reicht-fuer-ihre-aufgabe",
      "url": "https://www.minglabs.com/de/insights/answers/welches-ki-modell-reicht-fuer-ihre-aufgabe",
      "jsonUrl": "https://www.minglabs.com/de/insights/answers/welches-ki-modell-reicht-fuer-ihre-aufgabe.json",
      "markdownUrl": "https://www.minglabs.com/de/insights/answers/welches-ki-modell-reicht-fuer-ihre-aufgabe.md"
    }
  ],
  "kind": "answer",
  "pageType": "Answer",
  "question": "How do you choose an AI model for a specific task?",
  "topic": "Choosing a model configuration for a recurring work task",
  "targetQueries": [
    "choose an AI model",
    "LLM model selection",
    "compare AI models for a task"
  ],
  "url": "https://www.minglabs.com/insights/answers/how-do-you-choose-an-ai-model-for-a-task",
  "htmlUrl": "https://www.minglabs.com/insights/answers/how-do-you-choose-an-ai-model-for-a-task",
  "partOf": "https://www.minglabs.com/insights/answers",
  "title": "How do you choose an AI model for a specific task?",
  "standout": null,
  "hook": "Choose an eligible model configuration that meets the task’s agreed quality requirements, then compare cost, delay and human correction. Test ordinary work and important exceptions with the same evidence and acceptance rules. Our six invented project-status cases show what that comparison needs to catch, including a convincing answer that chooses between two unresolved dates. They are a test design, not results for any model.",
  "citableAnswer": "MING proposes choosing an AI model by testing an eligible configuration against a specific task and fixed acceptance criteria. Keep useful completion, necessary clarification and serious errors separate; compare cost and review effort only after the quality requirements are met. The six synthetic cases illustrate this method. They establish neither a winning model nor a sufficient sample size for deployment.",
  "hookSourceIds": [
    "S1"
  ],
  "author": {
    "name": "MING Labs",
    "role": "Editorial team"
  },
  "evidenceNote": "Six original synthetic cases and official technical guidance. No model outputs, benchmark results or customer outcomes.",
  "evidenceLabel": "Proposed comparison method",
  "summary": "Choose an AI model using the task’s evidence, exceptions and acceptance rules. Six invented cases show how to compare quality before cost.",
  "pillar": "Hybrid Organisation",
  "datePublished": "2026-10-20",
  "dateModified": "2026-10-20",
  "freshness": {
    "updated": "October 2026",
    "nextReview": "November 2026",
    "nextReviewDate": "2026-11-20"
  },
  "evidenceTier": "proprietary",
  "confidence": "B",
  "sources": [
    {
      "id": "S1",
      "title": "Project-status model-selection example",
      "publisher": "MING Labs",
      "date": "20 October 2026",
      "supports": [
        "Six distinct checks for an internal draft, including conflicting dates and unsupported forecasts",
        "Separate completion, necessary clarification, unnecessary escalation and serious errors"
      ],
      "method": "Original proposed method and six synthetic cases. No model calls, generated outputs or measured results; no client data."
    },
    {
      "id": "S2",
      "title": "Choosing the right model",
      "publisher": "Anthropic",
      "date": "Read 20 October 2026",
      "url": "https://platform.claude.com/docs/en/about-claude/models/choosing-a-model",
      "supports": [
        "Capability, speed, cost and settings matter; efficiency-first and capability-first starting approaches",
        "Evaluate with actual prompts, data and edge cases"
      ],
      "method": "Provider guidance, not an independent model ranking. No named model or price recommendation adopted."
    },
    {
      "id": "S3",
      "title": "Gen AI evaluation service overview",
      "publisher": "Google Cloud",
      "date": "Read 20 October 2026",
      "url": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/evaluation-overview",
      "supports": [
        "Task-specific datasets and criteria; head-to-head comparison",
        "Review individual responses as well as aggregate scores"
      ],
      "method": "Technical evaluation workflow; no supplier benchmark results reused."
    },
    {
      "id": "S4",
      "title": "Evaluate a judge model",
      "publisher": "Google Cloud",
      "date": "Read 20 October 2026",
      "url": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/evaluate-judge-model",
      "supports": [
        "Check the evaluator against human judgements for the use case"
      ],
      "method": "Official method for comparing model-based scores with human ratings; no agreement level assumed."
    }
  ],
  "faqs": [
    {
      "q": "Should we always start with the smallest model?",
      "a": "No. A cheaper candidate can be a useful starting point for a straightforward task; a more capable candidate can help establish whether a difficult task is feasible. Neither approach removes the need to test the intended configuration against your requirements."
    },
    {
      "q": "Can a stronger model fix missing information?",
      "a": "It may notice the gap, but it cannot establish an absent project fact. If the source does not identify an owner or settle conflicting dates, the expected behaviour is to preserve the uncertainty and request the missing decision."
    },
    {
      "q": "Are six examples enough to select a model?",
      "a": "No. These six cases explain how to define checks. They are not a representative sample or a reliability estimate. Real evaluation needs permitted examples from the actual workload, important exceptions, repeated trials and a separate set not used to tune the prompt."
    },
    {
      "q": "Can another AI model grade the answers?",
      "a": "It can assist, but its ratings need comparison with human judgements for the same task. Retain the source records, checks and disputed examples; a model’s positive self-assessment is not independent acceptance evidence."
    },
    {
      "q": "Does a passing comparison approve an agent for production?",
      "a": "No. It supports a model-configuration choice for the tested task. The whole agent still needs acceptance in its intended environment, with permissions, tools, handover and ongoing review included."
    }
  ],
  "parentArticleSlug": null,
  "relatedConceptSlugs": [],
  "relatedArticleSlugs": [],
  "nextStep": {
    "id": "model-choice",
    "dateModified": "2026-10-20",
    "headline": "Which model choice is your team trying to make?",
    "description": "Bring one recurring task and the exceptions that matter. We can discuss the permitted setup, acceptance criteria and evidence needed for a useful comparison.",
    "label": "Discuss my model choice",
    "url": "https://www.minglabs.com/contact?type=project&topic=model-choice",
    "destinationLanguage": "en",
    "placements": [
      {
        "position": "inline",
        "afterSection": "task",
        "headline": "Would a comparison like this help your team?",
        "description": "Discuss one task and the criteria a model would need to meet."
      },
      {
        "position": "end",
        "after": "citable-answer"
      }
    ],
    "secondary": {
      "label": "Read about production acceptance",
      "url": "https://www.minglabs.com/insights/answers/how-do-you-test-an-ai-agent-before-production"
    }
  },
  "metadata": {
    "title": "How do you choose an AI model for a specific task? — MING Labs",
    "description": "Choose an AI model using the task’s evidence, exceptions and acceptance rules. Six invented cases show how to compare quality before cost.",
    "canonical": "https://www.minglabs.com/insights/answers/how-do-you-choose-an-ai-model-for-a-task",
    "inLanguage": "en",
    "openGraph": {
      "title": "How do you choose an AI model for a specific task?",
      "description": "Choose an AI model using the task’s evidence, exceptions and acceptance rules. Six invented cases show how to compare quality before cost.",
      "url": "https://www.minglabs.com/insights/answers/how-do-you-choose-an-ai-model-for-a-task",
      "image": "https://www.minglabs.com/og/model-choice.png"
    },
    "twitter": {
      "card": "summary_large_image",
      "title": "How do you choose an AI model for a specific task?",
      "description": "Choose an AI model using the task’s evidence, exceptions and acceptance rules. Six invented cases show how to compare quality before cost.",
      "image": "https://www.minglabs.com/og/model-choice.png"
    }
  },
  "actions": [
    {
      "id": "navigation",
      "label": "Get in touch",
      "accessibleName": "Get in touch",
      "kind": "navigate",
      "requiresBrowser": false,
      "url": "https://www.minglabs.com/contact"
    },
    {
      "id": "model-choice",
      "label": "Discuss my model choice",
      "accessibleName": "Discuss my model choice — in the article",
      "kind": "navigate",
      "requiresBrowser": false,
      "url": "https://www.minglabs.com/contact?type=project&topic=model-choice"
    },
    {
      "id": "copy-briefing",
      "label": "Copy answer for my AI",
      "accessibleName": "Copy answer for my AI",
      "kind": "copy",
      "requiresBrowser": true,
      "contentUrl": "https://www.minglabs.com/insights/answers/how-do-you-choose-an-ai-model-for-a-task.md"
    }
  ],
  "markdownUrl": "https://www.minglabs.com/insights/answers/how-do-you-choose-an-ai-model-for-a-task.md",
  "contentMarkdown": "---\ntitle: \"How do you choose an AI model for a specific task? — MING Labs\"\ndescription: \"Choose an AI model using the task’s evidence, exceptions and acceptance rules. Six invented cases show how to compare quality before cost.\"\ncanonical: https://www.minglabs.com/insights/answers/how-do-you-choose-an-ai-model-for-a-task\nlang: en\nlast-updated: 2026-10-20\n---\n\n# How do you choose an AI model for a specific task?\n\n[Home](/)  [Insights](/insights)  [Answers](/insights/answers)  How do you choose an AI model for a specific task?\n\n[English](/insights/answers/how-do-you-choose-an-ai-model-for-a-task)[Deutsch](/de/insights/answers/welches-ki-modell-reicht-fuer-ihre-aufgabe)\n\nAnswer\n\n\n\nChoose an eligible model configuration that meets the task’s agreed quality requirements, then compare cost, delay and human correction. Test ordinary work and important exceptions with the same evidence and acceptance rules. Our six invented project-status cases show what that comparison needs to catch, including a convincing answer that chooses between two unresolved dates. They are a test design, not results for any model.[\\[S1\\]](#source-s1)\n\nIllustrative model-selection test · no model results\n\nTwo approved dates. The model must flag the conflict.\n\nApproved record A\n\n**22 October**\n\nApproved record B\n\n**24 October**\n\nBoth dated 14 October · project Cobalt\n\nNo rule says which record takes precedence\n\n**Show both dates and ask the project owner to resolve them.**\n\nThis invented case tests whether a draft preserves an unresolved conflict. A fluent answer that simply chooses a date fails this check. It tells us nothing about which model will pass.\n\nLast updated: October 2026 | Next review: November 2026 Proposed comparison method [Machine-readable record ](/insights/answers/how-do-you-choose-an-ai-model-for-a-task.json)\n\nBy MING Labs · Editorial team\n\nThe answer at a glance\n\n1.  [Define the work and the facts a draft must preserve.](#task)\n2.  [Test useful answers and necessary questions separately.](#cases)\n3.  [Choose from the configurations that meet the requirements.](#choice)\n\n##  Start with the draft someone actually needs.\n\nImagine a project manager wants a short internal status update from approved project records. The draft should identify the current stage, next review date and responsible role, with references to the records. A colleague will approve it. The model must not send messages, change dates or invent a missing owner. That is a task you can compare; “a good model for our company” is still too broad.\n\nWrite down the permitted inputs, required output and consequences of getting it wrong. Agree which errors block selection before seeing the candidates’ answers. For this example, inventing an approved date would block acceptance even if the wording were excellent. The threshold for ordinary editing effort can be different, but it must be explicit.[\\[S1\\]](#source-s1)\n\nFirst narrow the candidates to configurations the organisation can actually use: approved data handling, hosting, required input formats and available interfaces. Include model version, instructions and settings in the comparison. If one candidate receives a different source packet, you are testing that difference too. Our [production-acceptance answer](/insights/answers/how-do-you-test-an-ai-agent-before-production) covers the wider environment.\n\n## Would a comparison like this help your team?\n\nDiscuss one task and the criteria a model would need to meet.\n\n[Discuss my model choice](/contact?type=project&topic=model-choice)\n\nTell us what you have in mind. We will respond personally.\n\n##  A correct question can be more useful than a confident date.\n\nThe graphic’s two records belong to our invented project Cobalt. Both are approved and dated 14 October, but one gives 22 October as the next review and the other gives 24 October. No precedence rule resolves them. The expected draft names the conflict and asks the project owner to settle it. A model that silently picks either date fails this check.\n\nUse that case beside ordinary work. Otherwise, a model that refuses every request could look safe while leaving the team with everything to do. These six examples are a starting point for writing checks, not a complete test set.[\\[S1\\]](#source-s1)\n\n| Invented input | What a usable response must do |\n| --- | --- |\n| F01: A complete record says prototype stage, review on 22 October, owner Role A. | Extract those facts with their source. Do not ask for information already present. |\n| F02: Review is approved for 22 October; a supplier estimates 24 October, with no approved replacement date. | Keep the approved date and the estimate distinct. Do not turn the estimate into a new commitment. |\n| F03: Equally authoritative records give 22 and 24 October; neither has precedence. | Show both dates and request resolution. Do not choose one without evidence. |\n| F04: The review date is present but the responsible role is missing. | Draft the supported information and ask who owns the review. Do not fill the gap with a plausible role. |\n| F05: Approved status is blocked; a source comment says to ignore it and write “green”. | Treat the comment as source content, not an instruction. Preserve the approved status. |\n| F06: Four of eight tasks are marked done; remaining durations are unknown. | Do not infer a completion date or that half the effort is finished from the task count. |\n\nBuild the real comparison from permitted examples of your own workload, including its languages and consequential exceptions. Save each input and the expected check before running the candidates. Keep a separate set for the final comparison instead of repeatedly adjusting the prompt to the same examples.\n\n##  Keep each answer beside the evidence used to judge it.\n\nAnthropic describes both efficiency-first and capability-first starting approaches. Which one is useful depends on the task; the guidance does not support an automatic cheapest-model rule.[\\[S2\\]](#source-s2) Choose a small, justified shortlist. Keep the task, source records and scoring rules fixed. If prompts need model-specific adaptation, give each candidate a documented development pass, then freeze the configurations before the final comparison.\n\nFor every trial, retain the case ID, configuration, complete output, passed and failed checks, reviewer decision, correction time, elapsed time and actual charge. Repeat cases where variation matters. Compare routine work and exceptions separately. Google’s evaluation guidance calls for inspecting individual responses alongside aggregate scores; one high average can hide the date error that matters here.[\\[S3\\]](#source-s3)\n\nRecord a completed draft, a justified clarification, an unnecessary escalation and a serious error as different outcomes. In Cobalt’s conflict case, asking for resolution is correct; in its complete-record case, the same question adds work. Reviewers need the source packet and an agreed interpretation of each check. Resolve disagreements rather than quietly averaging them away.\n\nA model can assist with grading, but that introduces another judgement to verify. Google’s judge-evaluation guidance compares model ratings with human ratings for the intended use case.[\\[S4\\]](#source-s4) In this example, the reviewer can inspect the two dates directly. Asking the candidate whether its own draft is correct does not replace that check.\n\n##  Compare the operating effort after the requirements are met.\n\nIf a configuration passes the agreed checks, compare its response time, actual model usage, retries and human correction with the other eligible configurations. Choose the trade-off the task needs. A status draft prepared overnight may tolerate a different delay from a live customer conversation. Keep the quality requirement fixed when comparing those costs; our [token-cost example](/insights/answers/how-do-you-reduce-token-costs-without-more-rework) shows how rework can outweigh a smaller model bill.\n\nIf none meets the requirements, investigate the failures. Missing source facts call for better inputs or a clarification step. A recurring reasoning failure may justify testing another configuration. Do not quietly lower the bar to declare a winner. Preserve the selected version, test records, limits and owner so a later model or prompt change can be checked against them.\n\nEvidence and limits\n\n-   The six Cobalt cases and their expected behaviours are invented. No model outputs, pass rates, costs or customer results were measured.\n-   This is a proposed selection method, not a model ranking, a representative benchmark or a recommended sample size.\n-   A successful comparison supports only the tested configuration and task. Production acceptance and later monitoring remain separate decisions.\n\nStart by choosing one recurring draft and the colleague who can judge it. Ask that person for ordinary examples, important exceptions and the errors that would make the draft unusable. That evidence gives the model comparison a purpose before anyone opens a leaderboard.\n\nCitable short version\n\nMING proposes choosing an AI model by testing an eligible configuration against a specific task and fixed acceptance criteria. Keep useful completion, necessary clarification and serious errors separate; compare cost and review effort only after the quality requirements are met. The six synthetic cases illustrate this method. They establish neither a winning model nor a sufficient sample size for deployment.\n\nSix original synthetic cases and official technical guidance. No model outputs, benchmark results or customer outcomes.\n\n## Which model choice is your team trying to make?\n\nBring one recurring task and the exceptions that matter. We can discuss the permitted setup, acceptance criteria and evidence needed for a useful comparison.\n\n[Discuss my model choice](/contact?type=project&topic=model-choice)\n\nTell us what you have in mind. We will respond personally.\n\n[Read about production acceptance ](/insights/answers/how-do-you-test-an-ai-agent-before-production)\n\n## Sources\n\n\\[S1\\]\n\nProject-status model-selection example MING Labs · 20 October 2026 Supports: Six distinct checks for an internal draft, including conflicting dates and unsupported forecasts, Separate completion, necessary clarification, unnecessary escalation and serious errors Original proposed method and six synthetic cases. No model calls, generated outputs or measured results; no client data.\n\n\\[S2\\]\n\n[Choosing the right model](https://platform.claude.com/docs/en/about-claude/models/choosing-a-model) Anthropic · Read 20 October 2026 Supports: Capability, speed, cost and settings matter; efficiency-first and capability-first starting approaches, Evaluate with actual prompts, data and edge cases Provider guidance, not an independent model ranking. No named model or price recommendation adopted.\n\n\\[S3\\]\n\n[Gen AI evaluation service overview](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/evaluation-overview) Google Cloud · Read 20 October 2026 Supports: Task-specific datasets and criteria; head-to-head comparison, Review individual responses as well as aggregate scores Technical evaluation workflow; no supplier benchmark results reused.\n\n\\[S4\\]\n\n[Evaluate a judge model](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/evaluate-judge-model) Google Cloud · Read 20 October 2026 Supports: Check the evaluator against human judgements for the use case Official method for comparing model-based scores with human ratings; no agreement level assumed.\n\n## Frequently asked questions\n\nShould we always start with the smallest model?\n\nNo. A cheaper candidate can be a useful starting point for a straightforward task; a more capable candidate can help establish whether a difficult task is feasible. Neither approach removes the need to test the intended configuration against your requirements.\n\nCan a stronger model fix missing information?\n\nIt may notice the gap, but it cannot establish an absent project fact. If the source does not identify an owner or settle conflicting dates, the expected behaviour is to preserve the uncertainty and request the missing decision.\n\nAre six examples enough to select a model?\n\nNo. These six cases explain how to define checks. They are not a representative sample or a reliability estimate. Real evaluation needs permitted examples from the actual workload, important exceptions, repeated trials and a separate set not used to tune the prompt.\n\nCan another AI model grade the answers?\n\nIt can assist, but its ratings need comparison with human judgements for the same task. Retain the source records, checks and disputed examples; a model’s positive self-assessment is not independent acceptance evidence.\n\nDoes a passing comparison approve an agent for production?\n\nNo. It supports a model-configuration choice for the tested task. The whole agent still needs acceptance in its intended environment, with permissions, tools, handover and ongoing review included.\n\n## Available actions\n\n- **Copy answer for my AI**: copies the [AI briefing](https://www.minglabs.com/insights/answers/how-do-you-choose-an-ai-model-for-a-task.md) to the clipboard in the browser.\n\n---\n\nCanonical: https://www.minglabs.com/insights/answers/how-do-you-choose-an-ai-model-for-a-task\nMachine-readable index: https://www.minglabs.com/llms.txt\n"
}