Multimodal workloads are no longer research demos. Production systems now routinely combine vision, audio, and language in a single inference pipeline, turning raw media into structured decisions. A support ticket might arrive as a voice message with a screenshot attachment. A robotics controller might ingest a camera feed and emit natural-language status reports. A content tool might draft copy, generate a hero image, and produce a voiceover in one pass. For developers, the challenge is not finding a model that understands images or transcribes speech, but finding infrastructure that runs these models under a single API contract with predictable latency and no pricing surprises. Oxlo.ai provides exactly that: a developer-first inference platform that hosts vision LLMs, image generation, audio transcription, text-to-speech, embeddings, and object detection behind one OpenAI-compatible endpoint.
Moving Beyond Text in Production
Modern applications now process video frames, audio streams, and generated images inside the same pipeline that handles structured reasoning. A customer-support agent might transcribe a voice memo with Whisper, extract visual context from an attached screenshot, and then generate a response with a reasoning model like Kimi K2.6 or DeepSeek R1. Each step adds latency, cost, and integration complexity. When infrastructure is fragmented across token-based providers, billing becomes unpredictable. Long audio transcripts or high-resolution image sequences inflate input token counts, which directly increases cost on per-token platforms such as Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale. Oxlo.ai removes that uncertainty with a flat per-request pricing model for its chat and reasoning endpoints, so multimodal pipelines that feed long context into an LLM do not trigger runaway bills. This matters most for agentic loops, where a model may iterate across multiple tool calls, each appending new text and images to a growing conversation history.
A Unified Multimodal Stack
Oxlo.ai hosts more than 45 models across seven categories behind a single base URL, https://api.oxlo.ai/v1. Instead of managing separate accounts for image generation, speech transcription, and chat, developers can route all traffic through one OpenAI-compatible API. The catalog includes:
- LLMs and reasoning: Qwen 3, Llama 3 and 4, DeepSeek R1 and V3, Kimi K2.5, Kimi K2 Thinking, and Kimi K2.6, GPT-Oss, Mistral, GLM 5, and Minimax M2.5.
- Vision: Gemma 3 27B and Kimi VL A3B for image understanding.
- Image generation: Oxlo.ai Image Pro, Oxlo.ai Image Ultra, Flux.1, SDXL, and Stable Diffusion 3.5.
- Audio: Whisper Large v3, Whisper Turbo, Whisper Medium for transcription, and Kokoro 82M for text-to-speech.
- Embeddings: BGE-Large and E5-Large.
- Object detection: YOLOv9 and YOLOv11.
Supported endpoints cover chat/completions, embeddings, images/generations, audio/transcriptions, and audio/speech. Because the surface area mirrors the OpenAI SDK, switching existing code usually requires only a change of base_url and API key. That uniformity reduces boilerplate and eliminates the need to maintain multiple client libraries, authentication handlers, and error-handling strategies across different providers.
Vision and Language Reasoning
Vision-capable LLMs on Oxlo.ai accept image inputs through the standard chat/completions endpoint. Kimi K2.6 supports a 131K context window, advanced reasoning, agentic coding, and vision, making it suitable for analyzing long documents that contain mixed text and figures. Gemma 3 27B and Kimi VL A3B offer additional options for visual question answering and UI parsing.
Developers can also combine vision with function calling and JSON mode. A single request can ingest a screenshot, identify interactive elements via tool definitions, and return a structured JSON payload describing bounding boxes and actions. This is useful for automated testing, accessibility auditing, and agentic workflows that must manipulate graphical interfaces. Because Oxlo.ai supports streaming responses, these vision-to-text chains can deliver partial results to the user interface while the model is still reasoning over the image.
Below is a minimal Python example using the OpenAI SDK with Oxlo.ai:
import os
import openai
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key=os.environ["OXLO_API_KEY"]
)
response = client.chat.completions.create(
model="<vision-model-id>", # e.g., Kimi K2.6 or Gemma 3 27B
messages=[{
"role": "user",
"content": [
{"type": "text", "text": "Summarize the chart in one sentence."},
{"type": "image_url", "image_url": {"url": "https://example.com/chart.png"}}
]
}],
stream=True
)
for chunk in response:
print(chunk.choices[0].delta.content or "", end="")
Audio Transcription and Speech
Audio is a natural complement to text pipelines. Oxlo.ai offers Whisper Large v3, Whisper Turbo, and Whisper Medium through the audio/transcriptions endpoint for converting speech to text. For the reverse direction, the Kokoro 82M text-to-speech model is available via audio/speech. These models integrate cleanly into agentic systems. For example, a voice memo can be transcribed, fed into a reasoning chain with Qwen 3 or Llama 3.3 70B, and the resulting text can be spoken back to the user with Kokoro, all within the same API contract. The OpenAI SDK handles multipart file uploads for transcription and binary audio output for speech, so the developer experience is identical to OpenAI's own audio stack.
curl https://api.oxlo.ai/v1/audio/transcriptions \
-H "Authorization: Bearer $OXLO_API_KEY" \
-F file="@recording.mp3" \
-F model="whisper-large-v3"
