Multimodal LLMs have moved beyond research demos. Production systems now routinely send high-resolution images, audio clips, and structured document scans into the same inference endpoint that handles text. The problem is that most platforms bill by the token. When a single image can encode into thousands of tokens, and an audio transcript can stretch context windows to six figures, costs become unpredictable. Oxlo.ai treats every API call as one flat request, which makes it a natural fit for multimodal pipelines that need to move large payloads without ballooning spend.
What Multimodal LLMs Actually Do
A multimodal model processes discontinuous data types through a shared transformer backbone or a mixture of expert encoders. Vision-language models tokenize image patches into embeddings that sit alongside text tokens. Audio models either feed spectrogram features directly or transcribe speech into text for downstream reasoning. On Oxlo.ai, you access these capabilities through standard endpoints: chat/completions for vision and text, audio/transcriptions for speech-to-text, audio/speech for text-to-speech, and images/generations for visual synthesis. Because the API is fully OpenAI SDK compatible, you can point an existing client to https://api.oxlo.ai/v1 and start sending image URLs or base64 strings immediately.
The Vision Stack on Oxlo.ai
Oxlo.ai hosts several vision-capable models. Gemma 3 27B handles image understanding alongside text reasoning with strong efficiency. Kimi VL A3B is built for vision-language tasks where latency matters. For heavier agentic workloads that combine vision, coding, and long-document analysis, Kimi K2.6 supports a 131K context window and accepts image inputs natively. If your pipeline generates images rather than consuming them, Oxlo.ai offers Oxlo.ai Image Pro, Oxlo.ai Image Ultra, Flux.1, SDXL, and Stable Diffusion 3.5 through the images/generations endpoint. The platform currently carries more than 45 open-source and proprietary models across seven categories, so you can keep vision, text, and image generation inside one provider boundary.
Audio as a First-Class Modality
Audio is often treated as an afterthought, but robust multimodal support requires both ingestion and synthesis. Oxlo.ai runs Whisper Large v3, Whisper Turbo, and Whisper Medium for transcription through the audio/transcriptions endpoint. For outbound voice, Kokoro 82M text-to-speech generates speech from the audio/speech endpoint. You can chain these into a single workflow: transcribe user audio with Whisper, reason over the text with Llama 3.3 70B or DeepSeek R1 671B MoE, then return a spoken response via Kokoro. All of this uses the same base URL and request-based pricing structure, so you do not need to reconcile separate token counts across speech and language models.
Why Token-Based Billing Breaks Down for Multimodal Workloads
Token-based providers scale cost with input length. An image encoded at high resolution can consume more tokens than the entire text prompt. A 30-minute audio file, once converted to tokens, can dwarf the actual reasoning budget. For agentic systems that loop screenshots, documents, and transcripts through multiple turns, the token count compounds quickly. Oxlo.ai charges one flat cost per API request regardless of prompt length. For long-context and agentic multimodal workloads, this model can be significantly cheaper than token-based alternatives because the presence of an image or a long audio transcript does not change the unit price. You can compare plans at https://oxlo.ai/pricing.
Code Example: Vision Inference with the OpenAI SDK
Here is a minimal example using the OpenAI Python SDK to send an image to a vision model on Oxlo.ai.
import openai
import base64
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
def encode_image(path):
with open(path, "rb") as f:
return base64.b64encode(f.read()).decode("utf-8")
image_b64
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