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LTX 2 Fast Text-to-Video

Create short generative video with LTX 2 Fast Text-to-Video on LLM.API, with resolution-aware pricing and a unified developer workflow.

Start Using API

What is LTX 2 Fast Text-to-Video?

LTX 2 Fast Text-to-Video targets motion content rather than chat. Video generation model by Muapi. On LLM.API it sits alongside other media models under a single developer account.

Providers

LLM.API routes LTX 2 Fast Text-to-Video to the providers below, with discounted effective rates versus list price.

List price by provider ($ / 1M tokens)

InputOutput
muapi$0 in
$0 out

Provider list prices; the LLM.API discount applies on top.

ProviderPricingContextCapabilities
muapi30% offin —; out $0.06 per second of video——

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.

Try this model

Test LTX 2 Fast Text-to-Video right here — free to start.

LTX 2 Fast Text-to-Video
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call LTX 2 Fast Text-to-Video through the OpenAI-compatible API — Video generation via LLM.API (see docs for media endpoints).

python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAP_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="ltx-2-fast-t2v",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)
{
  "model": "ltx-2-fast-t2v",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

5 Core Capabilities

  • Motion coherence

    Targets temporally consistent motion across frames for the requested scene. Relevant for `ltx-2-fast-t2v` workloads on LLM.API.

  • Iterative creatives

    Enables prompt-based A/B exploration of motion concepts. Tuned to how teams typically call LTX 2 Fast Text-to-Video.

  • Cinematic prompting

    Responds to camera, lighting, and mood language in creative briefs. Grounded in the model's video role rather than generic chat claims.

  • Short-form content

    Fits vertical and horizontal short-form marketing formats. Tuned to how teams typically call LTX 2 Fast Text-to-Video.

  • Text-to-video generation

    Synthesizes short video clips from prompts for ads, explainers, and social.

6 Most Valuable Use Cases

  • Scene tests for world and environment design with LTX 2 Fast Text-to-Video
  • Internal pitch videos for stakeholders
  • UGC-style drafts for performance marketing with LTX 2 Fast Text-to-Video
  • Social-first product teaser generation
  • Short promotional clips from written briefs with LTX 2 Fast Text-to-Video
  • Explainer openers before live-action shoots

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Intelligent AI Routing

    Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.

    One endpoint, every model.

  • Cost-Aware Execution

    Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.

    More performance, less spend.

  • Resilient Fallback Flows

    Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.

    Never go dark on users.

  • Deep LLM Observability

    Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.

    See every token, everywhere.

  • Task-Level Orchestration

    Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.

    Think tasks, not models.

  • High-Throughput Batch APIs

    Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.

    Scale from 1 to millions.

Why run LTX 2 Fast Text-to-Video on LLM.API?

  • Unified AI Routing

    Production: Reach LTX 2 Fast Text-to-Video and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Production: Compare provider price points and keep spend visible as you scale LTX 2 Fast Text-to-Video.

  • Reliability Layer

    Retry and route across configured providers when a single upstream blips.

  • Observability

    Trace prompts, tokens, and errors for LTX 2 Fast Text-to-Video alongside the rest of your stack.

  • Drop-in SDKs

    Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.

  • Model Breadth

    Production: Swap LTX 2 Fast Text-to-Video for chat, media, or embedding alternatives without rewriting auth.

When to Use — When NOT to Use

Use it if...

  • You can tolerate longer generation times than chat (LTX 2 Fast Text-to-Video)
  • You are building short-form generative video features (LTX 2 Fast Text-to-Video)
  • You need resolution-tiered pricing for motion content (LTX 2 Fast Text-to-Video)

Avoid if...

  • You only need still images or chat text
  • You require sub-second interactive generation
  • You need broadcast-length films or guaranteed cinema-grade continuity

COMMUNITY

What creators say about AI video models

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to LTX 2 Fast Text-to-Video.

  • Creator reviews of the latest Kling releases praise multi-shot consistency, native audio and genuine 4K output.
  • Comparisons with Sora and Runway focus on prompt adherence and motion stability rather than raw resolution.
  • The common workflow advice: generate many short takes cheaply, then upscale or extend the ones that land.

SOURCES

Frequently Asked Questions

  • Can I use tools or structured outputs with LTX 2 Fast Text-to-Video?

    Tooling support varies; for pure video models, prefer the modalities listed rather than assuming chat tools.

  • What is LTX 2 Fast Text-to-Video?

    Video generation model by Muapi. On LLM.API it is addressed as `ltx-2-fast-t2v`.

  • How long do LTX 2 Fast Text-to-Video jobs take?

    Video generation typically takes longer than chat; latency scales with duration and resolution and may vary by queue.

  • Where is the canonical page for LTX 2 Fast Text-to-Video?

    https://llmapi.ai/models/ltx-2-fast-t2v/

  • How do I call LTX 2 Fast Text-to-Video via API?

    Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "ltx-2-fast-t2v" and your LLM.API key. See the code snippet on this page.

  • When should I choose LTX 2 Fast Text-to-Video?

    You can tolerate longer generation times than chat — especially when you specifically need LTX 2 Fast Text-to-Video.

  • What is the context length for LTX 2 Fast Text-to-Video?

    Reported context for LTX 2 Fast Text-to-Video is See provider specs. Always verify the active provider row if multiple providers are listed.

  • How is LTX 2 Fast Text-to-Video priced on LLM.API?

    Listed pricing metadata shows: 1080p: $0.06/sec. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

  • Does LTX 2 Fast Text-to-Video support streaming?

    Streaming depends on the active provider; check the providers table on this page for flags.

  • Which providers serve LTX 2 Fast Text-to-Video?

    LLM.API currently lists: muapi. Availability can vary by region and account.

  • What modalities does LTX 2 Fast Text-to-Video support?

    LTX 2 Fast Text-to-Video accepts text and produces video according to its architecture metadata on LLM.API.

  • Is LTX 2 Fast Text-to-Video a chat model?

    No—LTX 2 Fast Text-to-Video is categorized as a video model. Use the matching API surface rather than assuming chat completions.

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