Best LLM for Roleplay 2025: What to Look For

What makes a model good at roleplay

Ask which is the best llm for roleplay 2025 and you will get a different answer from every forum thread, because people value different things. Rather than naming a winner I cannot verify, it is more useful to list the qualities that decide whether a roleplay session works, and then check any model against them.

• Willingness to stay in the fiction. A model that breaks character to lecture you, or refuses a scene a novelist could write, ruins the flow. This is the most common complaint about general assistants used for storytelling.
• Memory of the scene. Names, places, promises and injuries need to survive dozens of turns. This depends on context length and on how you manage the history.
• Consistent voice. The character should sound the same on turn 80 as on turn 5.
• Cost and speed per turn. Roleplay means many short exchanges. A model that is cheap per token lets you keep long histories.
• Control. Temperature, penalties and stop sequences let you tune creativity and stop the model from writing your character's lines.

No single model wins all five. Larger proprietary models often write more polished prose; check their current pricing and content rules and compare with your own test scene. A hosted open-weight model tuned to avoid refusals trades some polish for freedom and a low price.

What this API offers for roleplay

The service exposes one model through an OpenAI-compatible API, run by an independent provider. It is an open-weight model tuned to answer without content refusals for lawful adult use. It is not the official API of any big vendor and it is not a router that picks between models, so what you test is what you get.

The facts that matter for roleplay:

• Model ID uncensored at https://api.llmapisource.com/v1, using POST /v1/chat/completions.
• A 100,000-token context window shared by prompt and completion. That is long enough to keep a very long scene in the prompt.
• Streaming over SSE, so replies appear as they are written.
• Temperature, top_p, stop, seed and the frequency and presence penalties are passed through.
• Prompts are not used for training.

The limits are equally plain. It is text only: no image generation, no voice, no video. There is no fine-tuning, so you shape the character with the prompt. And one rule is fixed: no sexual content involving minors, in any framing. The service is meant for fiction, roleplay and companion apps for adults.

A working setup

Roleplay quality depends more on your system prompt than on any switch. A good one is short, concrete and states the format. Here is a pattern for a noir mystery, with the rules written as plain instructions:

Copyfrom openai import OpenAI

client = OpenAI(
    base_url="https://api.llmapisource.com/v1",
    api_key="YOUR_KEY",
)

SYSTEM = (
    "You are the narrator and all non-player characters in a 1940s detective story. "
    "Write in second person for the player, present tense, 2 to 4 short paragraphs. "
    "Never write the player's actions or dialogue. "
    "Keep track of clues, names and injuries. "
    "End each turn with a situation the player can respond to. "
    "Stay in the fiction; do not add disclaimers."
)

history = [{"role": "system", "content": SYSTEM}]

def turn(player_text):
    history.append({"role": "user", "content": player_text})
    reply = client.chat.completions.create(
        model="uncensored",
        messages=history,
        temperature=0.9,
        top_p=0.95,
        frequency_penalty=0.3,
        presence_penalty=0.3,
        stop=["Player:"],
    )
    text = reply.choices[0].message.content
    history.append({"role": "assistant", "content": text})
    return text

print(turn("I step into the office and close the door behind me."))

The stop sequence is a cheap guard against the model writing the player's next line. The penalties reduce repeated phrases, which is the usual way long sessions go stale. If you want to replay a scene exactly while you edit the prompt, pass a fixed seed.

Settings that matter

Setting	Starting value	What it changes
temperature	0.8 to 1.0	Higher gives more surprising prose and more drift from the facts of the scene
top_p	0.9 to 0.95	Trims unlikely words; lower it if replies turn odd
frequency_penalty	0.2 to 0.5	Cuts repeated words and sentence openings
presence_penalty	0.2 to 0.5	Nudges toward new topics and details
stop	a speaker label	Prevents the model from speaking for you
max_tokens	optional	Caps the length of a single reply

These are starting points, not tested optimums. Change one at a time and replay the same scene with a fixed seed so you can see the effect. If you use a front end such as SillyTavern, which lets you add an OpenAI-compatible endpoint, enter the base URL, key and model ID uncensored, then paste the same sampler values into its settings. The quickstart has curl and Node versions of the call above.

Memory and the 100k context window

A hundred thousand tokens is a lot of story, roughly the length of a short novel, but it is not infinite and it is not free. Every turn resends the whole history, so cost and latency grow as the session grows. Three habits keep that under control.

1. Keep a pinned summary. Every 20 to 30 turns, ask the model to write a short summary of facts, names and open threads, then replace the oldest turns with it.
2. Keep a character sheet in the system prompt. Appearance, goals, speech habits and secrets belong there, not buried 60 turns back.
3. Trim chatter. Out-of-character comments and rerolled replies do not need to stay in the history.

The model can still lose details deep in a very long prompt, so do not assume a full window means perfect recall. If a fact matters, restate it in the summary.

A request is rejected when the prompt plus the completion would exceed 100,000 tokens, so if you let a session grow without trimming, you will eventually have to cut history anyway.

Testing a model for your own style

Reviews cannot tell you whether a model suits your taste, so build a small test you can repeat. Pick three scenes that stress different skills: a tense dialogue with a secret, a long action beat, and a quiet scene where the character has to remember something said earlier. Run each one with a fixed seed and the same system prompt, then read the output for the failures you care about: broken character, lines written for you, repeated phrasing, forgotten details.

Keep notes on what you see, and run the same three scenes on any other model you are considering. That takes under an hour and tells you more than a ranking written for somebody else's tastes. Because this API is OpenAI-compatible, swapping the base URL and model ID in the script above is all the change a comparison needs.

What a session costs

Pricing is $0.15 per 1M input tokens and $0.50 per 1M output tokens, from prepaid credit. Because input is resent every turn, input dominates the bill in a long session. Here is a realistic example.

A session of 200 turns, with an average prompt of 6,000 tokens and an average reply of 300 tokens:

• Input: 200 x 6,000 = 1,200,000 tokens, which is 1.2 x $0.15 = $0.18
• Output: 200 x 300 = 60,000 tokens, which is 0.06 x $0.50 = $0.03
• Total: $0.21

Now the extreme case. A turn with 90,000 tokens of input and 400 of output costs 90,000 x $0.15 / 1,000,000 = $0.0135 for input and 400 x $0.50 / 1,000,000 = $0.0002 for output, so $0.0137. Running 100 turns at that size costs $1.37. Summarizing to keep prompts short is the main way to bring that down.

New users can claim $0.50 of trial credit with one click after sign-up, which needs no card and lasts 7 days, so you can run a few full scenes before paying. Top-ups start at $20, with bonuses of 5% from $50, 10% from $100 and 15% from $200, and credit never expires. See the pricing page for payment options, including card and crypto, or get a key to try it.

Where another option fits better

Be realistic about the trade-offs. If you want images of your characters, voices or a polished mobile app, a text-only API is only the engine and you build the rest. If you need the finest literary prose and do not mind content filters, a large proprietary model may read better for some scenes. If you want to run everything privately on your own computer, a local model is the right call, as long as your hardware can handle it.

A hosted uncensored endpoint suits a narrower case: you want fiction and companion-style chat for adults without refusals, you want an OpenAI-compatible API your existing tools already speak, and you want a per-token price that makes long sessions affordable. If that matches, the quickest test is to run your own hardest scene through it with the trial credit.

More guides

• Are There Any LLM With a Free API? What Free Costs
• AnythingLLM API Key: Connect a Custom OpenAI Endpoint
• LiteLLM vs Direct Uncensored LLM API