Speech recognition has traditionally ended at the point of transcription. You send an audio file to a model like Whisper, receive a block of text, and handle the rest downstream. But raw transcripts are noisy. They lack punctuation, speaker labels, and structured metadata. They do not distinguish between actionable items and filler. Modern applications need more than a verbatim dump. They need reasoning, summarization, and structured extraction. Large language models can provide that layer of intelligence, yet running audio through a transcription API and then pushing long transcripts into a separate LLM API creates a cost problem on token-based platforms. Input tokens for a sixty-minute meeting transcript can scale into the tens of thousands, and every reasoning pass adds more. Oxlo.ai removes that constraint with request-based pricing. One flat cost per API call regardless of prompt length means that long-form speech recognition workloads become predictable. For developers building audio intelligence pipelines, Oxlo.ai is a natural fit because it unifies transcription and reasoning under a single API with economics that favor long context.
Beyond Raw Transcription: Why LLMs Matter for Audio
Automatic speech recognition models convert signal to text, but they do not understand intent. A transcript may confuse homophones, drop punctuation, or merge speakers. Without post-processing, a sixty-minute recording returns as an unreadable wall of text. This is where LLMs become essential. They can correct transcription errors, insert punctuation, infer speaker boundaries from context, and extract structured entities such as dates, action items, or product names.
Oxlo.ai offers the models needed for both stages of this pipeline. For transcription, the platform provides Whisper Large v3, Whisper Turbo, and Whisper Medium through the audio/transcriptions endpoint. For reasoning, the catalog includes general-purpose models like Llama 3.3 70B, multilingual agents like Qwen 3 32B, deep reasoning models like DeepSeek R1 671B MoE, and agentic specialists like Kimi K2.6 and GLM 5. Because everything is accessible through the same OpenAI-compatible base URL, you do not need to manage separate accounts or SDKs for audio and text.
A Unified Audio-to-Insight Pipeline
Building an enhanced speech recognition workflow on Oxlo.ai requires only the standard OpenAI SDK. You transcribe audio, then pass the resulting text into a chat model for structuring or analysis. The following Python example shows the pattern. The model identifiers correspond to Oxlo.ai's Whisper Large v3 and Llama 3.3 70B offerings.
from openai import OpenAI
client = OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_API_KEY"
)
# 1. Transcribe the audio
with open("meeting.wav", "rb") as audio:
transcript = client.audio.transcriptions.create(
model="whisper-large-v3",
file=audio,
response_format="text"
)
# 2. Structure the transcript with an LLM
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{
"role": "system",
"content": (
"You are a meeting assistant. Extract action items, key decisions, "
"and owners. Return the result as valid JSON."
)
},
{"role": "user", "content": transcript}
],
response_format={"type": "json_object"}
)
structured = response.choices[0].message.content
print(structured)This two-step pattern is simple, but under token-based pricing it becomes expensive when the transcript grows. A ninety-minute interview can easily exceed twenty thousand tokens. On providers that bill per token, both the transcription length and the subsequent LLM context drive up cost. Oxlo.ai treats each of these calls as a single request. The transcription is one request. The structuring call is one request. The price does not scale with the word count of the recording.
Why Request-Based Pricing Changes Long-Form Audio Economics
Speech recognition workloads are inherently long-context. Legal depositions, medical dictation, podcast episodes, and customer support recordings all generate transcripts that consume substantial token budgets. If you run multiple passes over the same transcript, for example, to summarize, extract entities, detect sentiment, and translate, a token-based bill multiplies with every pass.
Oxlo.ai uses request-based pricing. Each API call incurs one flat cost regardless of prompt length. For audio pipelines that feed large transcripts into reasoning models, this model can be 10 to 100 times cheaper than token-based alternatives. Competitors such as Together AI, Fireworks AI, OpenRouter, Replicate, and Anyscale scale cost with input length. On those platforms, longer audio means higher bills. On Oxlo.ai, cost remains tied to the number of operations you perform, not the duration of the recording.
Predictability matters for production systems. When you transcribe a batch of files and analyze them with an agentic workflow, you can forecast spend accurately. You will not face surprise charges because a user uploaded a two-hour file instead of a ten-minute clip. Oxlo.ai also eliminates cold starts on popular models, so latency remains low even when you process long audio in real time or near real time.
Structured Output and Function Calling
Modern speech recognition applications rarely want plain text. A call-center platform needs a JSON object with customer intent, urgency score, and resolution steps. A medical scribe needs structured encounter notes. A content platform needs chapter titles and timestamps.
Oxlo.ai supports both JSON mode and function calling across its chat models. After transcription, you can force the LLM to return a machine-readable schema. You can also define tools so the model invokes external functions, for instance, to schedule a follow-up calendar event from a meeting recording or to query a CRM from a sales call transcript.
The example below extends the pipeline with function calling. The model receives the transcript and decides whether to call an external tool to log a support ticket.
tools = [
{
"type": "function",
"function": {
"name": "create_support_ticket",
"description": "Log a customer issue from a call transcript",
"parameters": {
"type": "object",
"properties": {
"issue_type": {"type": "string"},
"severity": {"type": "string"},
"summary": {"type": "string"}
},
"required": ["issue_type", "severity", "summary"]
}
}
}
]
response = client.chat.completions.create(
model="qwen-3-32b",
messages=[
{"role": "system", "content": "Analyze the support call and extract ticket details."},
{"role": "user", "content": transcript}
],
tools=tools,
tool_choice="auto"
)Because Oxlo.ai is fully OpenAI SDK compatible, this code runs without modification in Python, Node.js, or cURL. You can switch between models such as Qwen 3 32B for agent workflows, DeepSeek V3.2 for coding-related transcripts, or Kimi K2.6 for advanced reasoning and vision tasks if the audio context includes referenced images or screen shares.
Building Agentic Audio Workflows
Speech recognition becomes significantly more powerful when you treat it as the first step in an agentic loop. Imagine a workflow that transcribes a daily standup recording, then uses an LLM to identify blockers, assign owners by matching names against a directory, and post summaries to a project management tool. Each iteration, clarification, or tool invocation is another API call.
On token-based platforms, agentic loops over long transcripts are costly. Each tool use and each multi-turn exchange bills for the full context window. Oxlo.ai’s per-request pricing removes that penalty. You can run multi-turn conversations, stream responses, and invoke functions without watching token meters accumulate.
Oxlo.ai offers models specifically suited for these agentic tasks. Qwen 3 32B and GLM 5 handle long-horizon agentic workflows. Kimi K2.6 and Kimi K2.5 provide advanced chain-of-thought reasoning for complex transcripts. DeepSeek R1 671B MoE excels at deep reasoning and complex coding discussions captured in technical recordings. With over forty-five models across seven categories, you can route audio tasks to specialized endpoints without leaving the platform.
Streaming responses and multi-turn conversation support mean that user-facing audio assistants feel responsive. Because there are no cold starts on popular models, the delay between uploading audio and receiving structured insight is determined by inference time, not queue overhead.
Getting Started
Oxlo.ai provides a free tier at $0 per month with sixty requests per day and access to more than sixteen free models, including a seven-day full-access trial so you can test long-context audio workflows before committing. The Pro plan at $80 per month includes one thousand requests per day across all models, while the Premium plan at $350 per month adds five thousand requests per day and priority queue access. For teams processing high volumes of audio, the Enterprise tier offers custom pricing, unlimited requests, dedicated GPUs, and a guaranteed thirty percent savings over your current provider.
The API base URL is https://api.oxlo.ai/v1. If you already use the OpenAI SDK in Python or Node.js, change the base URL and API key, and your existing transcription
