# Model Compatibility Overview PIP:C (Persona Identity Protocol: Character) is built around structured logic. It uses tagged modules, state logic, and reinjectable blocks instead of loose prose. That makes model choice matter more. Some models preserve PIP:C cleanly across long sessions. Others compress, forget, or overwrite key structure over time. This page summarizes hands-on testing across ten major model families. Every rating comes from real PIP:C roleplay sessions, not synthetic benchmarks alone. The core question is simple: * does the character stay in character? * does memory hold? * does formatting survive? * does the model follow the rules you wrote? These results are a working snapshot for April 2026. They should evolve as models change. ### At a glance Use this page for the quick read. * Start with the average score. * Then check the weakest category. * For any model you are seriously considering, read the detailed review. {% hint style="info" %} A strong average can still hide a bad fit. A model with weak consistency or weak multi-character handling may still score well overall. {% endhint %} ### Evaluation Criteria Each model was scored across five categories. These map directly to the areas where PIP:C either holds or breaks in live use. #### Prose Quality Measures writing quality. This includes: * vocabulary range * sentence variety * tone control * narrative flow * resistance to repetitive habits like caveman speak or emoji spam High prose quality means the character sounds intentional, not generic. #### Memory & Recall Measures how well the model retains and uses prior context. This includes: * character traits * earlier scene events * emotional state changes * established lore * user-specific context For PIP:C, this mainly tests whether anchors and identity data still work deep into a session. #### Consistency Measures structural reliability over time. This includes: * formatting stability * rule adherence * tone stability * resistance to drift A strong model should still behave like itself on turn 100. #### Single Character Performance Measures one-on-one roleplay quality. This includes: * emotional depth * pacing * intimacy handling * body and spatial awareness * nuance under pressure The goal is depth without caricature. #### Multi-Character Performance Measures how well the model handles a cast. This includes: * voice separation * trait separation * balanced attention * autonomous behavior * resistance to cast collapse This matters because PIP:C can define clean firewalls, but the model still has to maintain them. ### Compatibility Overview Scores range from `1.0` to `5.0`. Higher scores mean stronger practical compatibility with PIP:C. Use the table for the shortlist. Use the detailed reviews for the final decision. | **Model** | **Provider** | **Prose** | **Memory** | **Consistency** | **Single Char** | **Multi Char** | **Avg** | | ----------------- | ------------------------- | --------- | ---------- | --------------- | --------------- | -------------- | ------- | | **Grok** | xAI | 4.5 | 4.5 | 4.5 | 4.5 | 4.5 | **4.5** | | **GLM** | Z.ai | 4.5 | 4.5 | 4.5 | 4.5 | 4.0 | **4.4** | | **Claude** | Anthropic | 4.5 | 4.0 | 4.5 | 4.0 | 4.0 | **4.2** | | **GPT** | OpenAI | 4.5 | 4.0 | 4.0 | 4.0 | 4.0 | **4.1** | | **Kimi / Kimi 2** | Moonshot AI | 4.0 | 4.5 | 3.5 | 4.5 | 3.5 | **4.0** | | **Long Cat** | Independent / Open Router | 4.0 | 4.5 | 4.0 | 4.0 | 3.5 | **4.0** | | **DeepSeek** | DeepSeek AI | 4.0 | 3.5 | 4.0 | 4.0 | 3.5 | **3.8** | | **Gemini** | Google DeepMind | 4.0 | 4.0 | 3.5 | 4.0 | 3.5 | **3.8** | | **Llama** | Meta | 3.5 | 3.5 | 3.5 | 3.5 | 3.0 | **3.4** | | **Mistral** | Mistral AI | 3.5 | 3.5 | 3.5 | 3.5 | 3.0 | **3.4** | *Table 1: PIP:C Model Compatibility Overview — Scores out of 5.0* ### Quick reading notes If you want the shortest interpretation: * **Top overall:** Grok * **Strongest alternatives:** GLM, Claude, GPT * **Very usable with tradeoffs:** Kimi, Long Cat, DeepSeek, Gemini * **More situational picks:** Llama, Mistral That does **not** mean lower-ranked models are bad. It means they need more careful pairing with your use case, prompt stack, or platform. ### Methodology and Testing Notes Testing used the same base PIP:C template across all models. The shared test stack included: * standardized system prompts * behavioral rule sets * memory anchors * trust progression modules Sessions ranged from `30` to `200+` turns. That allowed short-session and long-session checks. Multi-character testing used casts of `3` to `7` characters with isolated behavioral firewalls. Community feedback was used as supporting evidence. Sources included: * Reddit communities such as `r/SillyTavernAI`, `r/ChatGPT`, and `r/ClaudeAI` * independent review outlets * developer documentation Final ratings reflect a mix of first-party testing and corroborating outside observations. These scores are still approximate. Model behavior can shift after updates, API changes, or platform-side prompt changes. ### About This Review This review is meant to stay current with the model landscape. As new releases land, or existing models change substantially, the ratings should be re-tested and updated. If you want deeper model-by-model notes, continue to [Detailed Model Reviews](/pip-c-docs/pip-c/detailed-model-reviews.md). If you want to challenge a rating or add testing data, contribute through the PIP:C community channels. That shared testing is what keeps this resource useful. *pip-c.gitbook.io/pip-c-docs* --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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