AI Song Lyrics Generator: API Pricing and Code (2026)

An AI song lyrics generator takes a short description of a song, a theme, a title or a mood, and returns text arranged into singable sections: verse, pre-chorus, chorus, bridge. The good ones are not just chat models with a songwriting prompt. They are tuned on song structure, so the line lengths scan, the chorus repeats, and the section tags are in a format a music model can read when it comes time to sing the words.

This post covers what the model actually produces, what it costs in credits, the exact HTTP calls to run one, and how to carry the lyrics forward into a finished track with synced timings. Code examples use Unicorn API, where one key and one bill cover the audio, image, video and chat models, so you can run a lyrics call and a music call without two accounts.

What an AI song lyrics generator produces

Ask a general chat model for a song and you usually get a poem: long lines, no repetition, nothing that fits a four-bar phrase. A dedicated AI song lyrics generator gives you something closer to a working lyric sheet, with explicit structure:

[Verse 1]
Six a.m. on the Hammersmith line
Coffee going cold, headlines going blind
...

[Chorus]
Hold the door, hold the door
...

Those bracketed tags matter more than they look. Music models read them as instructions about arrangement: where the energy lifts, where the hook lands, where to drop the instruments out. If you write lyrics by hand and skip the tags, the model guesses, and the guess is often a flat two-minute verse with no chorus.

Two pieces of jargon before the code. A prompt is the text you send describing what you want. A job is one unit of work on the server: media models are asynchronous, so you create a job, the server works on it, and you poll a status endpoint until it finishes. Nothing about an AI song lyrics generator is slow enough to need a webhook, but the shape is the same as video generation, which is why the API uses one pattern for everything.

On Unicorn API the lyrics endpoint is Ai Music API Generate Lyrics, a Suno audio model that takes a single prompt field. One field is the whole interface, which means prompt quality is the only lever you have. There is a section on that further down.

How much does an AI song lyrics generator cost?

Credits are the unit of billing. On a monthly plan a credit is $0.01: Starter is $25, Creator $50, Studio $100 and Pro $200 per month, each giving you credits at that rate. Top-up credits cost $0.015 each and never expire, so they are the right choice for bursty work rather than steady volume. Full details are on the pricing page.

Here is what the Suno audio endpoints cost. Prices are per output and round up to whole credits, so a 0.4-credit call bills as 1.

Model	What you use it for	Billed credits	At $0.01/credit
Ai Music API Generate Lyrics	Write lyrics from a prompt	1 per output	$0.01
Ai Music API Boost Music Style	Expand a short style note	1 per output	$0.01
Ai Music API Get TimeStamped Lyrics	Line-by-line timings for a track	1 per output	$0.01
Ai Music API Sounds	Short sounds, loops and stingers	3 per output	$0.03
Ai Music API Extend Music	Continue an existing track	12 per output	$0.12
Ai Music API Mashup	Blend uploaded audio into a new track	12 per output	$0.12

The practical takeaway: running an AI song lyrics generator is close to free compared with rendering audio. A hundred lyric drafts cost about a dollar. A hundred full-length track generations cost around twelve. So generate lyrics generously, pick the best one, and only then spend credits on audio. That ordering is the single biggest cost saving available in a music pipeline.

You never have to guess a price. POST /v1/models/price returns the exact cost of a request body before you run it, which is the right thing to call if you are showing users a price or enforcing a per-account budget:

curl -s https://api.unicornapi.net/v1/models/price \
  -H "Authorization: Bearer $UNICORN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ai-music-api-generate-lyrics",
    "input": { "prompt": "a slow country ballad about leaving a small town" }
  }'

Running an AI song lyrics generator with the API

Create a key at /keys and export it as UNICORN_API_KEY. Every media model follows the same two steps: POST /v1/jobs to start, then GET /v1/jobs/{id} until status is "succeeded", and read the result from outputs[0].url.

Step one: create the job

curl -s https://api.unicornapi.net/v1/jobs \
  -H "Authorization: Bearer $UNICORN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ai-music-api-generate-lyrics",
    "input": {
      "prompt": "An indie folk song about moving out of your first flat. Warm, specific, present tense. [Verse] [Chorus] [Verse] [Bridge] [Chorus]. Chorus hook: hold the door."
    }
  }'

The response contains an id. That is the job you poll.

Step two: poll until it is done

curl -s https://api.unicornapi.net/v1/jobs/JOB_ID \
  -H "Authorization: Bearer $UNICORN_API_KEY"

The whole thing in Python

import os, time, requests

BASE = "https://api.unicornapi.net"
HEADERS = {
    "Authorization": f"Bearer {os.environ['UNICORN_API_KEY']}",
    "Content-Type": "application/json",
}

def run(model, payload, timeout=300):
    """Create a job, wait for it, return the finished job object."""
    r = requests.post(f"{BASE}/v1/jobs",
                      headers=HEADERS,
                      json={"model": model, "input": payload})
    r.raise_for_status()
    job_id = r.json()["id"]

    deadline = time.time() + timeout
    while time.time() < deadline:
        job = requests.get(f"{BASE}/v1/jobs/{job_id}", headers=HEADERS).json()
        status = job["status"]
        if status == "succeeded":
            return job
        if status in ("failed", "canceled"):
            raise RuntimeError(f"{model} {status}: {job.get('error')}")
        time.sleep(2)
    raise TimeoutError(f"{model} job {job_id} did not finish in {timeout}s")

brief = ("An indie folk song about moving out of your first flat. "
         "Warm, specific, present tense. Four-line verses, two-line hook. "
         "[Verse] [Chorus] [Verse] [Bridge] [Chorus]. Chorus hook: hold the door.")

# Generate three candidates. Three credits, about three cents.
for i in range(3):
    job = run("ai-music-api-generate-lyrics", {"prompt": brief})
    url = job["outputs"][0]["url"]
    lyrics = requests.get(url).text
    print(f"--- draft {i + 1} ---\n{lyrics}\n")

Two details worth copying into your own code. First, the loop has a deadline, because a poll loop with no timeout is how a worker process hangs forever. Second, the result arrives as a URL, so fetch it and cache the text yourself rather than relying on that URL living forever.

If you would rather not write any code yet, the same models run in the browser at the audio playground, and the MCP integration exposes them to Cursor and Claude Code so you can ask an agent to draft lyrics and save the output into your project.

Turning the lyrics into an actual song

Lyrics on their own are half a deliverable. The same key covers the rest of the chain, and the pricing table above tells you where the money goes.

• Style text. Ai Music API Boost Music Style takes one field, content, and returns a boosted version of your style note. Send “indie folk, acoustic, melancholy” and use the fuller description it returns as the style value on a generation call. One credit.
• Longer tracks. Ai Music API Extend Music continues a track you already have. Its inputs include lyrics, style, title, continue_at, vocal_gender, instrumental, style_weight, negative_tags and weirdness_constraint, plus an audio_id identifying the existing track. That is how you get a second verse in the same voice instead of a new song that merely sounds similar.
• Blends. Ai Music API Mashup takes an upload_url_list of audio you supply, along with style, title, lyrics and duration, and produces a combined track. This is the honest version of what people search for as an AI cover song generator: your own stems, recombined, not someone else’s record impersonated.
• Short audio. Ai Music API Sounds handles loops and stingers, with sound_key, sound_tempo, sound_loop and grab_lyrics. Three credits per output, useful for intros and UI sounds rather than full songs.

Here is the chain from style note to extended track, with every intermediate value coming from the previous call:

# 1. Expand a terse style note into something the music model can use.
style_job = run("ai-music-api-boost-music-style",
                {"content": "indie folk, acoustic guitar, brushed drums, melancholy"})
style = requests.get(style_job["outputs"][0]["url"]).text.strip()

# 2. Continue an existing track with your chosen lyrics. 12 credits.
extend_job = run("ai-music-api-extend", {
    "model": "V4",
    "style": style,
    "title": "Hold the Door",
    "lyrics": lyrics,              # the draft you picked above
    "prompt": "Second verse and final chorus, slightly fuller arrangement",
    "audio_id": EXISTING_AUDIO_ID, # from a track already in your job history
    "continue_at": 96,
    "vocal_gender": "f",
})

print(extend_job["outputs"][0]["url"])

Note the dependency: extend needs an audio_id for a track that already exists, so it is the second step in a pipeline and never the first. Browse the catalog for the generation endpoints that produce those IDs, and see the Suno pricing and API walkthrough for the full music chain, or the music model comparison if you are still choosing a model.

Timestamped lyrics for lyric videos and karaoke

Several of the tools ranking for AI song lyrics generator are really lyric video makers: upload a song, get captions synced to the vocal. The work splits into two parts, transcription timing and rendering, and only the first part needs a model.

Ai Music API Get TimeStamped Lyrics handles it. It takes task_id and audio_id for a track generated through the music API and returns the lyrics with timings, for 1 credit:

curl -s https://api.unicornapi.net/v1/jobs \
  -H "Authorization: Bearer $UNICORN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ai-music-api-timestamped-lyrics",
    "input": { "task_id": "YOUR_TASK_ID", "audio_id": "YOUR_AUDIO_ID" }
  }'

From there it is plain data work. Convert each line and its start time into LRC for music players, or WebVTT for HTML video, and you have captions without touching a timeline editor. Pair it with text-to-speech or voice work and you can build a full pipeline; the text-to-speech API post covers that side.

Two practical notes. Timings are per line, not per syllable, so word-by-word karaoke highlighting needs interpolation across each line’s duration. And because the endpoint works from IDs rather than an uploaded file, it belongs after generation in your pipeline, not as a standalone transcription service for arbitrary MP3s.

Prompts that get usable lyrics out of the model

With a single prompt field, prompt craft is the entire product. The difference between unusable and usable output from an AI song lyrics generator is almost always specificity, and these are the five things worth stating every time:

1. Genre and era. “Early-2000s pop punk” constrains line length and rhyme scheme far more than “upbeat”.
2. Structure. Spell out the sections you want: [Verse] [Chorus] [Verse] [Bridge] [Chorus]. The model follows an explicit map.
3. Point of view and tense. First person present is the default register of most hit lyrics. Saying so prevents vague third-person narration.
4. The hook. If you have a title or a repeating line, give it. “Chorus hook: hold the door” produces a chorus that actually repeats something.
5. Concrete detail. Ask for named objects and places. “Hammersmith line, cold coffee” beats “the city, my feelings” every time, and it is the fastest way to kill the generic AI register.

A weak prompt: “write a sad song about love”. A prompt that works:

A mid-tempo 90s R&B breakup song, first person, present tense. Four-line verses, eight-line chorus. [Verse] [Pre-Chorus] [Chorus] [Verse] [Chorus]. Hook: “you kept the keys”. Reference specific small objects rather than abstract feelings. No rhyming on “heart” or “apart”.

For rap, add bar counts and rhyme density: “16 bars, multisyllabic end rhymes, internal rhyme on beats two and four, boom-bap tempo feel”. Negative instructions work too. Listing clichés to avoid removes a surprising amount of the sameness people complain about in every AI song lyrics generator.

Because each run bills 1 credit, the right workflow is volume then selection: generate five to ten drafts from the same brief, keep the strongest couplet or image from each, and assemble. Professional writers work the same way with their own drafts. The model just makes the drafting step cost a cent.

Mistakes people make with an AI song lyrics generator

Generating audio before the words are settled. Audio costs 12 credits a track, lyrics cost 1. Iterating on lyrics in the cheap endpoint and rendering once at the end costs a tenth of what shuffling full renders costs.

Dropping the section tags. If you hand-edit output and strip [Chorus] markers, the downstream music model loses its arrangement cues and tends to flatten the song into one long verse.

Treating one output as the answer. First drafts cluster around the obvious reading of your prompt. The interesting lines show up in draft four or five.

Polling too aggressively. A tight loop with no sleep wastes requests and can get throttled. Two seconds between polls, with a hard deadline, is plenty for text-scale jobs.

Not storing results. Outputs arrive as URLs. Download the text and keep it with the prompt that produced it. Without that pairing you cannot reproduce a result you liked, and losing a good draft is more annoying than losing a credit.

Imitating a specific artist’s catalogue. Asking for “lyrics exactly like” a named living songwriter produces output that is both derivative and legally awkward. Describe the sound and the subject instead, and keep your own edits on top. Copyright in purely machine-generated text is unsettled in several jurisdictions, so the lines you rewrite yourself are the lines you can most confidently claim.

Free options, no sign up, and the trade-off

Search results for this keyword are dominated by free web tools, and they are genuinely useful for a single idea at 1am. Type a theme, get a verse, close the tab. No key, no billing, no code. If that is the whole job, use one.

The trade-offs show up the moment you need the output somewhere other than your clipboard:

• No automation. A web form cannot be called from your app, your script or your agent.
• Caps and queues. Free tiers throttle, truncate, or ask for an account on the third generation.
• Broken chain. Free lyrics tools rarely connect to audio, so you copy text between three sites to get one finished song.
• Unclear terms. If you plan to release the track, you want to know what the provider claims about the output before you build on it.

The paid version is cheaper than most people assume. At 1 credit per lyrics run, a month of heavy drafting, say 300 generations, costs around $3 of a $25 Starter plan, and the leftover credits cover the audio, images and chat calls for the same project because one key spans every model in the catalog. That is the actual argument for calling an AI song lyrics generator over an API rather than through a form: not raw quality, but that the lyrics, the track, the timestamps and the artwork all come out of one pipeline with one bill.

Start with the lyrics model page for the current request shape, grab a key at /keys, and read the docs for the job polling contract you will reuse for every other model.