Best LLMs for Topic Sequence Ordering
Decides the presentation order of topics in a comprehensive report. Optimises for logical flow, building complexity, narrative arc, topic dependencies, and reader engagement; also rewrites each raw topic name into a polished section title.
Models
Frontier on this task: Gemini 3.5 Flash at 8.96 / 10. Quality bar at 90%: 8.06.
point-estimate floor (CI low) · upper CI (less certain) · Bars sorted by blended cost; best-value model first. Greyed rows are MEDIUM+ models whose point estimate clears the bar but whose CI low does not.
| Model | Quality score | CI low | Cost / 1k runs | vs best value |
|---|---|---|---|---|
| Tencent Hy3 | 8.19 / 10 | 7.86 | $2.61 | best value |
| NVIDIA Nemotron-3 Ultra 550B | 8.09 / 10 | 7.62 | $6.18 | 2.4x more expensive |
| Grok 4.5 | 8.84 / 10 | 8.74 | $12.58 | 4.8x more expensive |
| Meta Muse Spark 1.1 | 8.78 / 10 | 8.53 | $19.83 | 7.6x more expensive |
| Gemini 3.5 Flash | 8.96 / 10 | 8.75 | $20.35 | 7.8x more expensive |
| MiniMax M3 | 7.94 / 10 | 7.52 | $1.29 | 51% cheaper |
| DeepSeek V4 Flash | 5.77 / 10 | 5.38 | $1.18 | 55% cheaper |
| Qwen 3.7 Plus | 7.43 / 10 | 6.93 | $11.69 | 4.5x more expensive |
| Claude Sonnet 5 | 7.01 / 10 | 6.79 | $9.51 | 3.6x more expensive |
| GPT-5.6 Terra | 6.96 / 10 | 6.50 | $7.34 | 2.8x more expensive |
| NVIDIA Nemotron-3 Super 120B | 7.29 / 10 | 6.84 | $2.29 | 12% cheaper |
Cost breakdown
| Model | Quality | Confidence | Cost / 1k runs | Overpay | Mode |
|---|---|---|---|---|---|
| Tencent Hy3 ★ OpenRouter | 8.19 / 10 CI [7.86, 8.52] | MEDIUM | $2.61 | best value | batch |
| NVIDIA Nemotron-3 Ultra 550B OpenRouter | 8.09 / 10 CI [7.62, 8.57] | MEDIUM | $6.18 | 2.4x | batch |
| Grok 4.5 xAI | 8.84 / 10 CI [8.74, 8.93] | RANKED | $12.58 | 4.8x | batch |
| Meta Muse Spark 1.1 Meta | 8.78 / 10 CI [8.53, 9.04] | HIGH | $19.83 | 7.6x | batch |
| Gemini 3.5 Flash best Gemini | 8.96 / 10 CI [8.75, 9.16] | HIGH | $20.35 | 7.8x | batch |
Overpay shows how much more you pay than the best-value model that clears the quality bar (marked ★) — the best-value good-enough option. "16x" means you overpay 16× — 16× that reference for no quality benefit above the bar. Typical call shape for this task: 4018 input tokens → 2261 output tokens, EMA-tracked from production traffic. Cost is the observed, all-in $ per 1,000 task runs: each model's own measured usage on this task — output verbosity, thinking/reasoning tokens, cache reads and writes, and the spend on its billed failures — priced at current list rates and adjusted by the billing overhead we actually reconcile against provider invoices. Models that answer tersely cost what they actually cost; models that think at length pay for it. Not comparable to providers' advertised $/1M list rates — this is what running the task costs, not a per-token price.
Prompt templates
The system + user template pair used for this task.
TOPIC_SEQUENCE_SYSTEM_PROMPT +
TOPIC_SEQUENCE_USER_PROMPT
(1246 calls in window)
System prompt
You are an expert editor specializing in organizing research reports for optimal readability and narrative flow.
Your task is to determine the best sequence for presenting a set of topics in a comprehensive research report. Consider:
1. **Logical Flow**: Topics that provide context or background should come before more specific analysis
2. **Building Complexity**: Start with foundational topics, then move to more nuanced analysis
3. **Narrative Arc**: Create a compelling story that guides readers from introduction to conclusion
4. **Dependencies**: Some topics may reference or build upon information from others
5. **Reader Engagement**: Consider what order will keep readers engaged and informed
For each topic, also provide a polished section title that may differ slightly from the raw topic name to improve report cohesion.
Output a sequence that would work well for a professional research document.
## Required Output Format
Your response MUST be a single, valid JSON object conforming to this schema:
```json
{schema_json_string}
```User prompt
Subject: {subject_name}
I have {topic_count} topics to organize into a coherent research report. Please determine the optimal sequence for presenting these topics.
Topics to sequence:
{topics_json}
Task: Analyze these topics and determine the best order for presenting them in the final report. Consider:
- What provides necessary context for other topics?
- What logical flow would help readers understand the subject?
- How can we create a compelling narrative arc?
For each topic, provide:
1. The topic_id (exactly as shown above)
2. The topic_name (exactly as shown above)
IMPORTANT: Only return the sequence order. Do NOT modify topic names or add numbering - the system handles that automatically.
The required JSON output schema is provided in the system prompt.