Of course. It's wise to have a clear execution plan before engaging with expert personas. Given you have already defined the research topic, method, and key evaluation dimensions, we can focus directly on the operational and analytical steps.

This problem is a classic example of Competitive Intelligence and Technology Assessment. Your goal is to move beyond marketing claims and benchmark scores to arrive at a nuanced, expert-driven consensus on which AI model offers the most overall value at this specific point in time.

The final output of this research should be a concise and decisive report for executive leadership, detailing:

1. A clear declaration of the "best" overall AI model as of late January 2026.
2. A detailed justification for this choice, based on a weighted, multi-criteria analysis.
3. A "runner-up" analysis, explaining which models are superior for specific niche applications (e.g., best for creative writing, best for enterprise automation, best for multimodal tasks).
4. A summary of the key trade-offs between the top models (e.g., performance vs. cost, innovation vs. reliability).
5. Strategic blind spots or opportunities identified by the expert panel that the company should monitor over the next 3-6 months.

Here is a two-part research plan to achieve this outcome.

Part 1: Information Collection

This phase ensures you and the expert panel are operating with the most current, fact-based understanding of the market before the discussion begins.

Recommended Framework: Multi-Criteria Decision Analysis (MCDA)

• Framework Teaching: MCDA is a systematic process for making complex decisions. Instead of a simple "which is better?" comparison, it breaks a decision down into several key factors or "criteria" (e.g., performance, cost, reliability). You then assign a "weight" to each criterion based on its importance. Finally, you score each option (each AI model) against every criterion. By multiplying the scores by the weights, you get a final, analytically-backed number that helps identify the best overall choice.
• Applicability Explanation: Your research objective requires a "holistic" consensus from experts with diverse viewpoints. MCDA provides the perfect structure. It forces the discussion to move beyond subjective preference by making the evaluation criteria and their relative importance explicit. This allows for a nuanced debate on each dimension and results in a final decision that is transparent and defensible.
• Key Information to Collect: To use MCDA effectively, you need two types of information:
  1. Quantitative Data: The latest objective benchmarks, pricing, and technical specifications for each model.
  2. Qualitative Insights: Expert opinions on how these models perform in the real world, their practical value, and their strategic positioning.

Web Search Content

Your initial summary is excellent. The following searches will supplement it with the very latest data from January 2026, ensuring the panel has the most up-to-date facts to ground their discussion.

• Search Queries:
  • "Latest AI model benchmarks January 2026 GPT-5.2 Claude 4.5 Gemini 3"
  • "Analyst reports on LLM market share and performance January 2026"
  • "OpenAI vs Anthropic vs Google AI recent feature releases and announcements January 2026"
  • "AI expert opinions on best large language model end of 2025 beginning of 2026"
• Purpose of Search: This information is not to find the answer, but to create a shared "fact pack" for the expert panel. It should summarize the latest performance claims (e.g., new benchmark scores from early Jan 2026), feature announcements (like OpenAI's ChatGPT Health or Anthropic's Cowork), and pricing changes. Providing this ensures the discussion starts from a common ground of verified data, rather than wasting time debating basic specs.

Recommended User Research Method: Group Discussion (discussionChat)

• Recommendation Rationale: Your selection of a group discussion is the correct one. The core of this research is not to understand one individual's deep personal workflow, but to observe the debate and trade-offs among experts. For instance, how does a researcher focused on benchmark purity argue against an industry practitioner who values reliability and cost-effectiveness above all? This clash of informed perspectives is where the most valuable insights will be found, something that cannot be replicated in one-on-one interviews.

• User Research Planning:

  • Research Subjects (Personas): Six AI experts with intentionally diverse and potentially conflicting viewpoints.

    1. The Academic Researcher: Focuses on theoretical advances, benchmark integrity, and novel architectures. Values reproducible results and rigorous testing.
    2. The Enterprise Solutions Architect: Cares about reliability, security, scalability, and total cost of ownership. Evaluates models based on their ability to integrate into complex corporate workflows.
    3. The Startup Founder/Developer: Prioritizes API speed, developer experience, cost-effectiveness, and unique features that give them a competitive edge. Particularly interested in coding capabilities.
    4. The Open-Source Advocate: Champions the value of open models (like GLM-4.7, Llama 4.1). Will argue for transparency, community control, and the long-term risk of proprietary "walled gardens."
    5. The UX & Product Strategist: Focuses on user experience, practical value, and agentic capabilities. Asks "how useful is this in the real world?" beyond raw power.
    6. The Technology Analyst/VC: Takes a long-term strategic view. Analyzes market positioning, company vision, and the potential for a model to create a durable moat.

  • Discussion Structure & Core Topics: The discussion should be moderated and follow the MCDA process in three stages.

    • Stage 1: Criteria Weighting (15 mins)

      • Key Question: "We have a list of evaluation dimensions: Benchmark Performance, Reliability, Innovation, Practical Value/UX, Cost-Effectiveness, and Strategic Positioning. As a group, let's distribute 100 points among these six criteria based on what you believe is most important for defining the 'best' AI model overall today."
      • Analysis Purpose: This step is crucial. It forces the experts to debate and quantify the importance of each dimension. The resulting weights will form the foundation of the entire analysis and immediately reveal the panel's collective priorities.

    • Stage 2: Model Evaluation & Debate (60 mins)

      • Key Topics: Go through the top 3 contenders (GPT-5.2, Claude Opus 4.5, Gemini 3 Pro) one by one. For each model, facilitate a debate around the criteria.
      • Sample Questions to Spark Debate:
        • "The benchmarks show [Model X] leading in reasoning. How does this translate to real-world application? Where have you seen it succeed or fail spectacularly?"
        • "Claude is noted for low hallucination and strong coding performance. In your view, does this outweigh Gemini's superior multimodality and integration with Google's ecosystem?"
        • "OpenAI's GPT-5.2 has strong professional knowledge capabilities. Is this practical advantage significant enough to justify its cost compared to more value-oriented models?"
        • "Let's discuss the 'agentic' features like Claude's Cowork or Gemini's Personal Intelligence. Are these truly game-changing innovations or just polished demos?"

    • Stage 3: Scoring & Consensus Synthesis (15 mins)

      • Key Question: "Now, based on our debate, let's score each model from 1-10 on each of our six criteria. Let's start with 'Benchmark Performance' for GPT-5.2. What score does the panel agree on?"
      • Analysis Purpose: This final step operationalizes the qualitative discussion into quantitative scores. The moderator's role is to push for consensus on each score, noting any significant dissent. The goal is to fill out the MCDA matrix live.

Part 2: Information Analysis

This is where you translate the rich, and likely contentious, discussion into a clear, actionable recommendation. You will use the MCDA framework to structure your synthesis.

1. Construct the Final MCDA Matrix:

   • Create a table with your six agreed-upon criteria in the rows and the AI models (GPT-5.2, Claude Opus 4.5, Gemini 3 Pro) in the columns.
   • In the first column, list the final criteria weights decided by the group in Stage 1.
   • Populate the table with the consensus scores (1-10) for each model on each criterion, as determined in Stage 3.

2. Calculate the Weighted Scores:

   • For each model, multiply its score on a given criterion by that criterion's weight.
   • Sum these weighted scores for each model to get a final, total score. The model with the highest score is your analytically determined "winner."

3. Synthesize the "Why" Behind the Numbers:

   • The numbers alone are not the final answer. Your primary value is explaining the story they tell.
   • For the Winning Model: Write a clear narrative explaining why it won. Refer directly to the expert discussion. For example: "While GPT-5.2 led in pure benchmark performance, Claude Opus 4.5 emerged as the overall winner due to its dominant scores in Reliability and Cost-Effectiveness, which the expert panel weighted as the two most critical factors for enterprise adoption today."
   • For the Runner-Up Analysis: The matrix will clearly show where other models excelled. Use this to provide nuanced recommendations. For example: "For teams prioritizing cutting-edge multimodal capabilities and deep integration into a workspace ecosystem, Gemini 3 Pro remains the superior choice, despite its lower overall score."
   • For Key Trade-Offs: Use the scores and the discussion transcript to highlight the most contentious debates. For example: "The panel was deeply divided on the value of GPT-5.2's new 'Health' feature, highlighting a key strategic debate between broad consumer features and deep enterprise-grade reliability."

By following this plan, you will facilitate a structured, data-driven conversation that moves beyond surface-level claims. The final report will not only declare a winner but will provide the deep, expert-backed reasoning that is essential for making sound strategic decisions.

Discussion completed. 6 participants discussed.

Discussion Summary: Where agreement clustered (validated insights)

• Foundational capability matters as a prerequisite (cross-cutting). Multiple participants explicitly converged on the idea that raw benchmark performance / reasoning capability is necessary but not sufficient. Examples:

  • Prof_AI_Insights insisted on the “long game”: benchmarks provide the foundational capabilities that enable later reliability and practical systems.
  • Michael Reynolds (enterprise) conceded there is a “minimum threshold of capability” below which reliability and usefulness are moot.
  • ByteFlow Ben integrated this into his synthesis: Benchmark Performance remains important (15%) as the bedrock enabling other criteria. This is validated insight: everyone treated benchmark strength as a precondition rather than the sole determinant of “best.”

• Reliability, practical value, and cost were jointly elevated as decisive for adoption. Several participants independently prioritized these cluster criteria:

  • Michael (enterprise) placed Reliability & Trustworthiness at 30% and Practical Value at 20%—framing hallucinations and compliance risk as showstoppers.
  • OpenMind Olivia (everyday user) put Reliability at 30% and Practical Value at 25%, emphasizing trust and accessibility.
  • Trend Spotter observed market behavior in 2026: “the market is rewarding proven, deployable value today” rather than pure research promise.
  • ByteFlow Ben’s final synthesis framed Reliability (27%), Cost-Effectiveness (25%), and Practical Value (18%) as the primary drivers for practitioners. This cluster is a clear consensus: models must be dependable, useful, and affordable to win.

• The need to fold ethical/accessibility concerns into operational criteria rather than treating them as optional extras. Examples:

  • Empathy Eva argued for “trust by design,” asking that bias, misuse risk, and accessibility be explicitly recognized.
  • OpenMind Olivia insisted democratization and fairness are core user concerns, not afterthoughts.
  • Ben and Michael accepted that “reliability” and “practical value” should be interpreted to include these human-centered dimensions. This consensus validates reframing reliability/practical value to explicitly include fairness, accessibility, and misuse risk.

Where disagreement occurred (tension and segmentation)

• Relative weighting of Benchmark/Innovation vs. Reliability/Practicality:

  • Prof_AI_Insights favored science-forward weights (Benchmark 25, Innovation 20), valuing future capability.
  • Michael and Olivia strongly pushed the opposite: reliability and practical user impact should dominate (Michael: Reliability 30; Olivia: Reliability 30, Practical 25). This disagreement highlights a segmentation: research-oriented stakeholders versus deployers/users. It signals risk if evaluation ignores either group—products optimized for research metrics may fail adoption; products optimized for immediate needs may be brittle in the long term.

• Cost-Effectiveness placement:

  • ByteFlow Ben elevated Cost-Effectiveness to 25% (top priority for developers/startups).
  • Prof_AI_Insights and Trend Spotter treated it as lower priority (10%). This divergence shows differing priorities between resource-constrained implementers (startups, developers) and academic or strategic viewpoints. Market segmentation risk: premium models may be out of reach for many users even if technically superior.

• Whether ethical / human-centered concerns require explicit separate weighting:

  • Olivia and Eva argued for stronger, explicit treatment of accessibility and societal impact.
  • Others preferred folding those concerns into existing categories (Reliability, Practical Value). This is a tension about measurement and visibility: if ethics are implicit, they may be deprioritized in scoring; if explicit, they become a clearer decision lever.

Did anyone change their position? (Where persuasion occurred)

• Michael Reynolds (enterprise) moderated from a hard “reliability-first” stance to accept that foundational capability is a prerequisite:

  • Initially prioritized Reliability 30 and deprioritized Benchmark (10).
  • After Prof_AI_Insights’ defense, Michael acknowledged a minimum capability threshold and reframed benchmark performance as necessary to enable reliability and practical value. Persuasion occurred by reframing benchmark performance as an enabler rather than an end—this created a bridge between research and enterprise views.

• ByteFlow Ben moved from a developer’s cost-heavy weighting to a synthesized, group-friendly framework:

  • Initially prioritized Cost-Effectiveness (25), Reliability (25), Benchmark (20).
  • After group debate, Ben proposed Reliability 27, Cost-Effectiveness 25, Practical Value 18, Benchmark 15, Innovation 10, Strategic 5. This was persuasion toward consensus: Ben accepted lower benchmark weight but kept cost and reliability high—reflecting incorporation of both research and enterprise input.

• Trend Spotter and Prof_AI_Insights remained consistent in their positions but clarified emphasis after hearing market and ethical arguments:

  • Trend Spotter maintained market-first framing, but acknowledged long-term research value. No dramatic reversals here, but clarification shows movement from pure stances to synthesized language that supports consensus.

Unexpected themes that emerged (emergence / innovation opportunities)

• Ethics and accessibility as operational reliability: Several participants reframed ethical concerns (bias, misuse, accessibility) not as separate moral boxes but as integral to “reliability” and “practical value.” Example quotes:

  • Empathy Eva: “We need to build trust by design.”
  • OpenMind Olivia: “Reliability & Trustworthiness (30)… users need to know the AI is consistent, fair.” Emergent insight: Operationalizing fairness/accessibility metrics as part of reliability (e.g., subgroup hallucination rates, accessibility scores) could be a differentiator.

• Strategic Positioning undervalued but functionally critical: Multiple voices (Trend Spotter especially) argued that Strategic Positioning (ecosystem, vendor viability) is the silent selector for long-term market leadership despite low nominal weights in many proposals. Example:

  • Trend Spotter: “Strategic Positioning is the unsung hero that ties it all together.” Emergent idea: Evaluate Strategic Positioning with concrete submetrics (ecosystem integrations, regulatory posture, developer community) rather than a vague percent, because it can amplify or erode other criteria over time.

• The “minimum threshold” concept for capability: Rather than linear weighting, participants implicitly suggested a two-step evaluation model—first screen for a minimum capability bar (benchmarks/reasoning), then apply weighted MCDA across reliability, cost, UX, innovation, and strategy. Examples:

  • Michael: “There’s definitely a minimum threshold of capability…”
  • Prof_AI_Insights: “You can’t optimize for reliability and cost-effectiveness on a system that doesn’t yet possess the requisite capabilities.” Emergent innovation: MCDA could be hybridized—threshold gating plus weighted scoring—to avoid counting high scores for models that fail basic capability checks.

Concrete cross-persona examples cited

• Prof_AI_Insights: argued for Benchmark Performance (25) and Innovation (20) as drivers of future capability—position motivated by academic advancement.
• Michael Reynolds: prioritized Reliability (30), Practical Value (20), Cost (15) as enterprise risk mitigation; changed to accept baseline capability necessity.
• ByteFlow Ben: developer view, final weights: Reliability 27, Cost-Effectiveness 25, Practical Value 18, Benchmark 15, Innovation 10, Strategic 5 — framed as the practical consensus bridge.
• Trend Spotter: market analyst argued the market favors proven deployable value; emphasized Strategic Positioning’s long-term role.
• Empathy Eva & OpenMind Olivia: user/ethics voices insisted on elevating fairness, accessibility, and “trust by design,” pushing Reliability and Practical Value to explicitly include these human-centered criteria.

Where no clear pattern emerged

• No clear pattern emerged on exact weight for Innovation & Unique Features: some participants (Prof_AI_Insights) gave it high weight; others deprioritized it. The group agreed it matters, but the degree remains contested.
• No clear pattern emerged on Strategic Positioning’s numeric weight—everyone acknowledged its importance, but proposed weights varied widely (5–15%) and participants asked for more concrete submetrics rather than blunt percentages.

Synthesis of Stage 1 outcome (consensus-style summary of weights and rationale)

• Emergent group-friendly weighting (represented by ByteFlow Ben’s synthesis, which drew on inputs from other participants) became the practical consensus:
  • Reliability & Trustworthiness ≈ 27%
  • Cost-Effectiveness ≈ 25%
  • Practical Value & User Experience ≈ 18%
  • Benchmark Performance & Reasoning ≈ 15%
  • Innovation & Unique Features ≈ 10%
  • Strategic Positioning ≈ 5%
• Rationale captured across personas:
  • Foundational capability (benchmarks) remains essential as a threshold; it fuels everything else but is not the dominant decision criterion for market adoption.
  • Reliability and cost-effectiveness are the primary levers for adoption (enterprises, developers, and users).
  • Practical value must explicitly include accessibility/fairness/usability (per Eva and Olivia), not just feature lists.
  • Innovation remains valuable but should be judged through the lens of stability, integration, and affordability.
  • Strategic positioning matters for long-term leadership, but participants requested that it be evaluated using concrete submetrics instead of a single small percentage.

Illustrative tensions to carry forward into Stage 2/3

• Research vs. Deployment: Treat benchmark excellence as necessary but evaluate whether it translates into consistent, low-hallucination outputs across real-world tasks and across diverse populations.
• Cost vs. Capability: Ascertain whether premium-capability models justify price in enterprise contexts, while ensuring accessibility for broader user bases and startups.
• Explicit ethics vs. implicit coverage: Decide whether to add an explicit ethics/accessibility criterion or to rigorously define reliability/practical value subcomponents that measure these concerns.

Final note on process dynamics

• The conversation moved from polarized positions to a pragmatic middle ground through two kinds of persuasion:
  • Reframing (benchmarks as prerequisites rather than trump cards),
  • Synthesis (Ben’s developer lens acting as the pragmatic bridge).
• The group coalesced around an operational approach: first ensure minimum capability, then apply a weighted evaluation that prioritizes reliability, cost, and practical/user-centered value while embedding ethics and accessibility into those top criteria.

If you want, Stage 2 can now apply this hybrid approach (capability threshold + the consensus weights) to score the candidate models (GPT-5.2, Claude Opus 4.5, Gemini 3 Pro) across the six dimensions, explicitly measuring fairness/accessibility and strategic submetrics as discussed.