News30 Jun 2025• 12 min read

OpenAI o1 Preview: Strategy Team Field Guide

What the o1-preview and o1-mini reasoning models mean for startup strategy teams, with pricing, latency, and governance checklists.

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Max Beech
Head of Content

TL;DR

  • OpenAI o1-preview and o1-mini (announced September 2024) focus on deliberate reasoning, trading speed for deeper chain-of-thought.
  • Pricing lands at $15 / $60 per million input/output tokens for o1-preview, $3 / $12 for o1-mini (OpenAI, 2024); expect higher context costs if you stream detailed briefs.
  • Strategy teams should pair o1 with governance guardrails -algorithmic transparency records, escalation triggers, and evidence vaults -before rolling it into planning workflows.

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OpenAI o1 Preview: Strategy Team Field Guide

OpenAI’s o1 preview drop reframed how we think about reasoning models. Unlike GPT-4o’s real-time flair, o1 slows down to plan, simulate, and explain. For strategy teams juggling research, scenario planning, and approvals, the model unlocks deeper analysis -if you respect its cost profile and governance demands.

Model Snapshot o1-preview Reasoning focus High accuracy o1-mini Faster, cheaper Short briefs GPT-4o Realtime multimodal Lower reasoning
Featured illustration: o1-preview emphasises deliberate reasoning compared with o1-mini and GPT-4o.

Key takeaways

  • o1-preview excels at multi-step planning; keep it for high-stakes briefs, use o1-mini or GPT-4o for speed.
  • Costs stack quickly -reasoning traces add tokens. Cache outputs and store validated reports in Supabase to share across teams.
  • Document use in your AI experiment council and AI escalation desk before scaling.

What did OpenAI launch with o1 preview?

  • Models: o1-preview (higher accuracy, slower) and o1-mini (faster, cheaper).
  • Inference style: Models reason internally before responding, improving factuality for complex problems.
  • Availability: API + ChatGPT for enterprise and pro tiers (OpenAI, 2024).

The UK’s Frontier AI Safety commitments call for transparent reporting on advanced model capabilities (GOV.UK, 2024); o1’s reasoning traces help organisations meet that bar.

How does o1 perform versus GPT-4o?

OpenAI reported o1 outperforming GPT-4o across benchmark reasoning tasks like GSM8K and MATH (OpenAI, 2024). Expect noticeably longer latency: 8–12 seconds for complex prompts, compared with GPT-4o’s near real-time responses.

ModelInput / Output cost (USD per 1M)Avg latency (complex brief)Best use
o1-preview$15 / $608–12 sStrategic planning, simulations
o1-mini$3 / $124–6 sShort analyses, idea vetting
GPT-4o$5 / $152–3 sRealtime interactions
Cost and latency: o1 models are pricier and slower but deliver stronger reasoning.

How do you keep costs under control?

  • Trim prompt length; provide structured data via the organic growth data layer rather than narrative.
  • Cache outputs and reuse validated analyses.
  • Monitor usage in Supabase; set alerts when spend breaches thresholds.

Where should strategy teams deploy o1 first?

  1. Scenario planning: Stress-test launch plans or pricing changes; let o1 outline risks and mitigations.
  2. Research synthesis: Feed transcripts from founder community roadshow sessions; o1 can cluster themes and contradictions.
  3. Board prep: Generate draft investment memos with multi-step reasoning.

Mini case: o1 in go-to-market planning

An early-stage fintech swapped GPT-4 for o1-preview to redesign its go-to-market plan. The model generated a three-layer dependency map -community momentum, compliance approvals, and partner enablement. Humans spent 30% less time synthesising research and spotted a regulatory risk two months earlier. They still routed every recommendation through the pilot-to-paid playbook to capture evidence before executives signed off.

What guardrails do you need in place?

  • Transparency records: Log prompts, reasoning summaries, and decisions in line with the Algorithmic Transparency Record Standard (GOV.UK, 2024).
  • Escalation triggers: Tie o1 usage to the AI escalation desk -flag outputs with low confidence or sensitive claims.
  • Risk review: NIST’s AI Risk Management Framework recommends continuous monitoring of high-capability models (NIST, 2024). Schedule fortnightly reviews.
o1 Governance Checklist Transparency record filed Escalation triggers set Evidence logged in vault
Governance flow: transparency record, escalation triggers, and evidence logging keep o1 deployments accountable.

Expert review pending: [PLACEHOLDER for AI Safety Lead sign-off]

How do you brief executives?

  • Distinguish o1 from GPT-4o -highlight accuracy gains and cost implications.
  • Share risk mitigations (transparency record, escalation desk, evidence vault).
  • Present a 30-day pilot plan with KPIs (quality lift, hours saved, governance score).

Summary & next steps

  • Run a two-week pilot of o1-mini on research synthesis to gauge cost and quality.
  • Document usage in your experiment council, plugging telemetry into Supabase.
  • Expand to o1-preview for board-level documents once governance guardrails operate smoothly.

Next step CTA: Launch the o1 reasoning template within Athenic to spin up transparency records, escalation rules, and evidence logging in under an hour.

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