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Any LLM

wavespeed-ai /

Any LLM is a versatile large language model for text generation, comprehension, and diverse NLP tasks such as chat and summarization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

llm
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$0.01cho mỗi lần chạy·~100 / $1

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README

Any LLM

Any LLM is a unified large language model gateway that provides access to multiple state-of-the-art AI models through a single interface. Chat, reason, and generate text using models from Google, OpenAI, Anthropic, and more — all in one place.

Why It Stands Out

  • Multi-model access: Choose from a variety of leading AI models including Gemini, GPT, Claude, and more.
  • Unified interface: One consistent API and playground for all supported models.
  • System prompt support: Customize model behavior with custom instructions.
  • Reasoning mode: Enable step-by-step reasoning for complex problem-solving tasks.
  • Priority control: Choose between latency-optimized or quality-optimized responses.
  • Flexible parameters: Fine-tune temperature, max tokens, and other settings.
  • Prompt Enhancer: Built-in AI-powered prompt optimization for better results.

Parameters

ParameterRequiredDescription
promptYesYour question or instruction to the model.
system_promptNoCustom instructions to guide model behavior.
reasoningNoInclude reasoning steps in the final answer.
priorityNoOptimize for latency or quality (default: latency).
temperatureNoControls randomness (lower = focused, higher = creative).
max_tokensNoMaximum length of the response.
modelNoSelect which LLM to use (e.g., google/gemini-2.5-flash).
enable_sync_modeNoWait for result before returning response (API only).

Supported Models

  • google/gemini-2.5-flash
  • anthropic/claude-3.5-sonnet
  • openai/gpt-5-chat
  • And more...

How to Use

  1. Write your prompt — enter your question or instruction. Use the Prompt Enhancer for AI-assisted optimization.
  2. Add a system prompt (optional) — provide custom instructions to guide the model's behavior.
  3. Enable reasoning (optional) — turn on for step-by-step explanations.
  4. Select priority — choose "latency" for faster responses or "quality" for better outputs.
  5. Adjust parameters (optional) — set temperature and max_tokens as needed.
  6. Select a model — choose from available LLMs.
  7. Click Run and receive your response.

Best Use Cases

  • General Q&A — Get answers to questions across any topic.
  • Writing Assistance — Draft emails, articles, reports, and creative content.
  • Code Generation — Write, debug, and explain code in multiple languages.
  • Research & Analysis — Summarize documents, analyze data, and extract insights.
  • Reasoning Tasks — Solve math problems, logic puzzles, and complex reasoning challenges.
  • Brainstorming — Generate ideas, outlines, and creative concepts.

Pro Tips for Best Quality

  • Use system prompts to define the model's role, tone, and output format.
  • Enable reasoning for math, logic, and multi-step problems.
  • Lower temperature (0.1–0.3) for factual, consistent answers.
  • Higher temperature (0.7–1.0) for creative, varied responses.
  • Choose "latency" priority for quick interactions, "quality" for important tasks.
  • Experiment with different models to find the best fit for your use case.

Notes

  • Processing time varies based on model selection and prompt complexity.
  • Please ensure your prompts comply with usage guidelines.
Lưu ý:Trang web này sử dụng các mô hình AI do bên thứ ba cung cấp.

Any Llm API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/any-llm with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Any Llm below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "reasoning": false,
    "priority": "latency",
    "model": "google/gemini-2.5-flash"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/any-llm" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/any-llm";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "reasoning": false,
        "priority": "latency",
        "model": "google/gemini-2.5-flash"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "reasoning": False,
    "priority": "latency",
    "model": "google/gemini-2.5-flash"
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/wavespeed-ai/any-llm", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    time.sleep(2)

Any Llm API — Frequently asked questions

What is the Any Llm API?

Any Llm is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Any LLM is a versatile large language model for text generation, comprehension, and diverse NLP tasks such as chat and summarization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Any Llm API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/any-llm.

How much does Any Llm cost per run?

Any Llm starts at $0.010 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Any Llm accept?

Key inputs: `prompt`, `enable_sync_mode`, `max_tokens`, `model`, `priority`, `reasoning`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/any-llm.

How long does Any Llm take to generate?

Median end-to-end generation time on WaveSpeedAI is around 4 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Any Llm outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

Any LLM | Fast LLM API | WaveSpeedAI