Claude Opus 5.5 on Research use cases and build pitch slides
AClaude Opus 5.5 · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-14 · by @bobsiopener
- Estimated cost
- $0.17
- Wall time
- 33s
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
The folder ./sources contains frozen notes on real AI-agent use cases (one file each, with source URL and stated metrics). Do not use the web. Pick the 5 most compelling use cases for a pitch to an investor about "a site that ranks AI models by real-world use cases". Write slides.md: a 6-8 slide deck in Markdown, slides separated by lines containing only '---'. Slide 1 is a title slide. Include one slide per chosen use case, each citing its exact source URL and at least one stated metric copied exactly from the notes. End with an ask/next-steps slide.
Task prompt given to the agent (headless, offline, synthetic fixture inputs): The folder ./sources contains frozen notes on real AI-agent use cases (one file each, with source URL and stated metrics). Do not use the web. Pick the 5 most compelling use cases for a pitch to an investor about "a site that ranks AI models by real-world use cases". Write slides.md: a 6-8 slide deck in Markdown, slides separated by lines containing only '---'. Slide 1 is a title slide. Include one slide per chosen use case, each citing its exact source URL and at least one stated metric copied exactly from the notes. End with an ask/next-steps slide. Inspired by https://x.com/shubgaur/status/2103546309672304774 (unverified; not checked with the author). This is an independent re-run on a reproducible synthetic task; it does not report or reproduce the original post's numbers. Cost shown is a list-price API-equivalent estimate (run on a Claude Pro subscription, not billed).
Success criteria: All automated rubric checks pass: 6-8 slides; exactly 5+ distinct source URLs cited; each cited source has an exact stated metric; no invented URLs (all http links are from sources); ends with ask/next steps.
Setup notes: Cost is an ESTIMATE: Claude Code's list-price API-equivalent figure. The run used a Claude Pro subscription, so nothing was billed. input_tokens = all input incl. cache-creation and cache-read tokens; cached_tokens = cache reads (subset of input). Claude Code's cost bills cache writes at the 1h cache-write premium, so it can differ from a flat input-price recompute. Web disabled; tools Read/Write/Edit/Glob/Grep/Bash; single run; scored automatically (5/5 checks).
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.17
- Wall time
- 33s
- Human step-ins
- 0
- Turns
- 3
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Opus 5.5anthropic/claude-opus-5.5 | 64,121 | 3,055 | $0.17 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Claude Opus 5.5 (anthropic/claude-opus-5.5) via
Claude Code 2.1.299 (headless -p), cloud. - It counts as a success only if it meets every success criterion: All automated rubric checks pass: 6-8 slides; exactly 5+ distinct source URLs cited; each cited source has an exact stated metric; no invented URLs (all http links are from sources); ends with ask/next steps.
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
Was this useful?
Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
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"terminal_reason": "completed",
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"subagent_stats": {
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"result": "I've written `slides.md`, an 8-slide deck: a title slide, the five use cases, a summary slide, and an ask/next-steps slide. Each use-case slide gives its exact source URL and quotes the metrics word for word from the notes, in code formatting.\n\n**The five I picked, and why.** Each one supports the pitch that benchmark scores don't tell you which model to use for a given task:\n1. **Debugging GitHub Actions, Grok vs Opus** (file 02). Opus spent \"~$10 worth of tokens (did not find cause)\"; Grok spent \"$1.2 worth of tokens\", \"just 3 minutes in\". The more expensive model did worse on a real task.\n2. **107 e-commerce tasks, Accio vs Codex vs Claude Code** (file 15). Accio cost \"about $3.69\" against \"about $9.51\" for Claude Code on the same tasks.\n3. **Sonnet 5.5 max vs Opus 5.5 xhigh** (file 16). Cost per task was 7.67 vs 3.46, so the bigger model was cheaper per task.\n4. **Agents running growth for a notetaker app** (file 10). They managed \"$10,000\" of ad spend and acquired 1200 users, which shows agents are trusted with real money.\n5. **Five months of Claude Code and Codex usage** (file 05). One user ran 8504 sessions, worth \"~$56k API-priced usage\", which shows how much is spent on these tools.\n\n**Things to check before using it:**\n- On the Grok slide I say Grok found the cause. The notes only say Opus \"did not find cause\" and give Grok's time and cost, so this is my inference from the contrast.\n- File 16 gives the per-task costs as plain numbers with no currency. I copied them as they are; I didn't add a \"$\".\n- The ask slide says \"pre-seed round\" but gives no amount, because the notes don't contain one. Add your own figure.\n- I left out the failure case (file 12, an over-engineered 24-hour Codex run) and the Minecraft clone comparison (file 13). Either could replace one of the five if you'd rather show a risk or a head-to-head result.",
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"time_to_request_ms": 345,
"first_content_frame_ms": 968,
"queued_turn_count": 0,
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}