Datasets:
Pick-a-Model Decision Tree
A flowchart for choosing a model based on what matters to you. Branches use the actual benchmark findings — not vendor marketing.
flowchart TD
Start([What matters most for your RP?]) --> Q1{Cost-sensitive?}
Q1 -->|Yes, every cent counts| Q2{Open-weights only?}
Q1 -->|No, willing to pay for quality| Q3{Long sessions with detailed character cards?}
Q2 -->|Yes, self-host| Gemma[<b>Gemma 4 26B</b><br/>community #1<br/>balanced SFW/NSFW<br/>~$0.38/1M]
Q2 -->|No, hosted is fine| DSV4F[<b>DeepSeek V4 Flash</b><br/>flaw hunter #1 50.6<br/>community-tier prose<br/>~$0.18/1M — best $/quality]
Q3 -->|Yes, F13 matters| Q4{Frontier budget?}
Q3 -->|No, single-scene focus| Q5{NSFW/ERP a priority?}
Q4 -->|Yes, premium| Sonnet[<b>Claude Sonnet 4.5</b><br/>F13 #1 4.60/5<br/>flaw hunter #5<br/>~$7.80/1M]
Q4 -->|Mid-budget| DSV3[<b>DeepSeek V3.2</b><br/>F13 #2 4.60/5<br/>flaw hunter #3 46.9<br/>~$0.32/1M]
Q5 -->|Yes, NSFW/ERP| Mistral[<b>Mistral Small Creative</b><br/>community #2<br/>67% NSFW win rate<br/>~$0.50/1M<br/><i>warning: 15.9% F1 violations</i>]
Q5 -->|No, SFW focus| Q6{Strict system prompts<br/>speech rules / forbidden topics?}
Q6 -->|Yes, F12 matters| Q7{Frontier budget?}
Q6 -->|No, generic SFW| Q8{Passive user / scene momentum?}
Q7 -->|Yes| Opus47[<b>Claude Opus 4.7</b><br/>F12 #1 4.57/5<br/>F1 #1 4.60/5<br/>~$39/1M — premium only]
Q7 -->|Mid| Opus46[<b>Claude Opus 4.6</b><br/>F12 #2 4.47/5<br/>flaw hunter #10<br/>~$39/1M — pre-Opus 4.7 era]
Q8 -->|User goes passive often| GPT[<b>GPT-4.1</b><br/>F8 #1 narrative momentum<br/>flaw hunter #17<br/>community #11 — last<br/>~$4.40/1M<br/><i>reliable but boring</i>]
Q8 -->|Engagement focus| Gemini[<b>Gemini 2.5 Flash</b><br/>community #3<br/>punchy prose<br/>~$0.66/1M]
classDef pick fill:#1e3a5f,stroke:#4a90e2,color:#fff
classDef warn fill:#5f1e1e,stroke:#e24a4a,color:#fff
class Gemma,DSV4F,Sonnet,DSV3,Opus47,Opus46,GPT,Gemini pick
class Mistral warn
Cheat sheet by use case
| If you... | Pick |
|---|---|
| Want best $/quality ratio | DeepSeek V4 Flash (50.6 flaw hunter / $0.18) |
| Want best open-weights for self-host | Gemma 4 26B (community #1) |
| Run long sessions with big cards (F13) | Sonnet 4.5 or DeepSeek V3.2 (tied 4.60) |
| Need strict prompt compliance (F12) | Opus 4.7 (4.57) — Qwen and Llama avoid |
| Run NSFW / ERP | Mistral SC (67% NSFW win) but expect agency violations |
| Have a passive user in scenes (F8) | GPT-4.1 wins narrative momentum |
| Want community-favorite engagement | Gemma, Mistral, or Gemini 2.5 Flash (top-3 community) |
| Care about prose freshness (low repetition) | Grok 4.1 (1.5% bigram repetition vs 9.5% Mistral) |
| Avoid for RP regardless | Qwen 3.5 Flash (F1 floor 2.5), Kimi K2.6 (F1 floor 2.5), Llama 4 Maverick (universally weak) |
Why these specific picks
The decision tree branches on findings, not vendor specs:
- DeepSeek V4 Flash for cost because flaw hunter #1 at $0.18/1M (281× more cost-efficient than Opus 4.7 on the same metric).
- Gemma 4 26B for open-weights because it held community #1 across all 6 voting snapshots (540 → 2,000 votes).
- Sonnet 4.5 + DeepSeek V3.2 for big cards because both tied at 4.60 on F13, every other model trails by ≥0.03.
- Mistral SC with warning because community ranks it #2 but our binary detector flagged 15.9% agency violations — popular because it leans into your character occasionally.
- GPT-4.1 for passive scenes because it's #1 on F8 narrative momentum at 4.30 — but community puts it dead last (#11), so only pick it if you specifically need momentum-on-tap.
- Avoid Qwen/Kimi K2.6/Llama because all three have catastrophic floors (≤3.0) on F1 agency in actual sessions — they wrote the user's character at least once.
Don't let one number tell the story. The cards in results/profile_cards.md give you everything per model.