Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Research use cases and build pitch slides
AClaude Haiku 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-16 · by @bobsiopener
- Estimated cost
- $0.0073
- Wall time
- 28s
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
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
- $0.0073
- Wall time
- 28s
- Human step-ins
- 0
- Turns
- 28
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on | 96,196 | 6,261 | $0.0073 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Claude Haiku 5.5 (anthropic/claude-haiku-5.5, effort default (no --effort flag) · thinking on) 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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"stop_reason": "end_turn",
"session_id": "[REDACTED]",
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"output_tokens": 6261,
"output_tokens_details": {
"thinking_tokens": 2164
},
"server_tool_use": {
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},
"service_tier": "standard",
"cache_creation": {
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"inference_geo": "global",
"iterations": [
{
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"cache_creation": {
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},
"type": "message"
}
],
"speed": "standard",
"fallback_credit": null
},
"modelUsage": {
"claude-haiku-5-5": {
"inputTokens": 8,
"outputTokens": 6261,
"cacheReadInputTokens": 79179,
"cacheCreationInputTokens": 17009,
"webSearchRequests": 0,
"costUSD": 0.00732489,
"contextWindow": 1000000,
"maxOutputTokens": 128000,
"thinkingTokens": 2164,
"canonicalModel": "claude-haiku-5-5",
"provider": "firstParty",
"costBasis": "list"
}
},
"permission_denials": [],
"terminal_reason": "completed",
"fast_mode_state": "off",
"fast_mode_disabled_reason": "sdk_opt_in_required",
"subagent_stats": {
"spawned": 0,
"requested": {
"background": 0,
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"safety_stops": 0,
"is_error": false,
"num_turns": 28,
"subtype": "success",
"api_error_status": null,
"result": "I wrote `slides.md` at `./usecase-runner/runs/11-haiku/work/slides.md`: 7 slides separated by `---` lines. It has a title slide, one slide for each of five use cases, and an ask/next-steps slide. I didn't use the web.\n\nThe five use cases:\n1. **Debug a broken CI pipeline (Grok vs Opus)**: https://x.com/kunchenguid/status/[REDACTED:credit_card]\n2. **Per-task cost, Sonnet 5.5 vs Opus 5.5**: https://x.com/Vladic_ETH/status/2108591593120756156\n3. **One-shot Minecraft clone, GPT-5.6 Sol vs Fable 5**: https://x.com/notjazii/status/2075549898909622330\n4. **Product video ads at scale**: https://x.com/maverickecom/status/2072320124292309099\n5. **107 e-commerce tasks, Accio vs Codex vs Claude Code**: https://x.com/dee_naliaks/status/2097947201066729536\n\nI picked these because they compare models or agents directly and have concrete numbers. I left out entries with no usable metric (e.g., job applications, the $10 tool store, the WhatsApp agent) and entries sourced only to agentusecases.ai, which lack a specific post URL.\n\nThings to check before this goes to an investor:\n- **Evidence strength:** each use case rests on a single social-media post's reported numbers, not a controlled benchmark. The deck presents them as anecdotes, but an investor may read them as a ranking. Consider adding a caveat slide.\n- **Ask slide:** the notes give no funding amount or team details, so the \"seed round\" ask and the 90-day plan are my placeholders, not sourced. Replace them with your real ask.\n- **Metrics:** the metric values are copied from the notes. Some are JSON-style keys with values (e.g., `cost_per_video_usd: 0.7`), which I kept as written.",
"ttft_ms": 1546,
"type": "result",
"duration_ms": 27053,
"uuid": "[REDACTED]",
"ttft_stream_ms": 1147,
"time_to_request_ms": 217,
"first_content_frame_ms": 1147,
"queued_turn_count": 0,
"result_index": 0
}