Apply your own routing policies, reduce token costs automatically, and see every routing decision in real time with Merge Gateway.
What GPT Audio Mini costs to run
Test GPT Audio Mini
with Gateway’s Simulator
See a prompt's output, token spend, latency, and more with GPT Audio Mini.
Route requests to GPT Audio Mini in minutes
1$ pip install merge-gateway-sdk1from merge_gateway import MergeGateway
2
3client = MergeGateway(api_key="YOUR_API_KEY")
4
5response = client.responses.create(
6 model="openai/gpt-5.2",
7 input=[
8 {"type": "message", "role": "system", "content": "You are a helpful programming tutor. Explain the concepts clearly with practical examples."},
9 {"type": "message", "role": "user", "content": "Explain the concept of recursion in programming with a simple set of examples."},
10 ],
11)
12
13print(response.output[0].content[0].text)1response = client.responses.create(
2 model="anthropic/claude-sonnet-4-20250514",
3 input=[
4 {"type": "message", "role": "system", "content": "You are a helpful programming tutor. Explain the concepts clearly with practical examples."},
5 {"type": "message", "role": "user", "content": "Explain the concept of recursion in programming with a simple set of examples."},
6 ],
7)1from openai import OpenAI
2
3client = OpenAI(
4 api_key="YOUR_API_KEY",
5 base_url="https://api-gateway.merge.dev/v1/openai",
6)1response = client.chat.completions.create(
2 model="gpt-5.2",
3 messages=[
4 {"role": "system", "content": "You are a helpful programming tutor. Explain the concepts clearly with practical examples."},
5 {"role": "user", "content": "Explain the concept of recursion in programming with a simple set of examples."},
6 ],
7)
8
9print(response.choices[0].message.content)1npm install merge-gateway-ai-sdk-provider ai1import { createMergeGateway } from "merge-gateway-ai-sdk-provider";
2
3const gateway = createMergeGateway({
4 apiKey: "YOUR_API_KEY",
5});1import { generateText } from "ai";
2
3const { text } = await generateText({
4 model: gateway("openai/gpt-4o"),
5 prompt: "Explain the concept of recursion in programming with a simple set of examples.",
6});
7
8console.log(text);1import { createOpenAI } from "@ai-sdk/openai";
2
3const gateway = createOpenAI({
4 apiKey: "YOUR_API_KEY",
5 baseURL: "https://api-gateway.merge.dev/v1/ai-sdk",
6});
7
8// All generateText/streamText calls work unchanged1from anthropic import Anthropic
2
3client = Anthropic(
4 api_key="YOUR_API_KEY",
5 base_url="https://api-gateway.merge.dev/v1/anthropic",
6)
7
8message = client.messages.create(
9 model="claude-sonnet-4-20250514",
10 max_tokens=1024,
11 messages=[
12 {"role": "user", "content": "Explain the concept of recursion in programming with a simple set of examples."},
13 ],
14)
15
16print(message.content[0].text)Explore other models available in Merge Gateway
GPT Audio Mini FAQ
Heading
What other models does OpenAI offer?
OpenAI ships general-purpose text flagships alongside modality-specific models for audio and images. Here are some other models OpenAI supports:
- GPT Audio: the higher-quality audio model, for speech tasks that need stronger comprehension and generation than the Mini tier
- GPT-5.5: the current general-purpose flagship, strong across reasoning, coding, and agentic tasks
- GPT-5: the prior general-purpose flagship, still widely used across production text workloads
How does GPT Audio Mini differ from OpenAI's other models?
GPT Audio Mini is the smaller, cheaper model in OpenAI's speech-native audio line, built for high-volume voice tasks where cost matters most.
- Modality: takes audio in and returns speech, and it handles transcription and audio understanding, unlike the text-first GPT-5 line
- Cost vs GPT Audio: priced below the full GPT Audio model, trading some audio quality and comprehension for lower cost
- Turn-based vs streaming: processes discrete audio requests, where GPT Realtime is tuned for live, low-latency streaming
- Use case fit: aimed at scaled voice workloads like transcription and routine speech interactions rather than the most demanding audio tasks
Reach for GPT Audio Mini when you need affordable speech understanding and generation at volume and can trade some quality for cost.
What models should I consider using alongside GPT Audio Mini?
No single model is optimal for every task. Here are models worth pairing with GPT Audio Mini depending on what your product needs:
- GPT Audio for the audio tasks that need higher comprehension or generation quality than the Mini tier
- GPT-5.5 for the text reasoning behind a voice agent once speech has been turned into text
- Gemini 3.5 Flash for high-volume, low-cost multimodal steps that mix audio and text
- Claude Opus 4.8 for complex reasoning or drafting over transcribed content
What are the challenges of using GPT Audio Mini in my product?
Like any production LLM, GPT Audio Mini comes with tradeoffs worth planning for:
- Quality tradeoff: as the smaller audio tier, it trails GPT Audio on comprehension and generation, so escalate the hardest audio tasks
- Audio token cost: audio tokens still cost more than text, so very high volumes compound without active budgeting
- Latency: turn-based audio adds round-trip latency, so use a streaming model for live conversation UX
- Voice data sensitivity: spoken audio can carry personal or biometric data, which raises retention, consent, and DLP requirements
- Provider dependency: relying only on OpenAI is fragile during outages or model retirements
Why should I use Merge Gateway to route LLM requests with GPT Audio Mini and every other model?
Using GPT Audio Mini through Merge Gateway gives you access to the model itself and the infrastructure layer around it:
- One API, every provider: Reach GPT Audio Mini and every other major LLM through a single endpoint and API key, swapping the model string to change providers without touching application code
- Intelligent routing and automatic failover: Merge routes around OpenAI outages automatically, and a Mini-to-full-quality audio ladder is easy to express as a policy that can cut spend 40 to 60%
- Cost governance: Set hard or soft project budgets so audio spend stays in plan, with every request attributed to a model, project, and tag in one billing dashboard
- Build Your Own Router: Define what "best" means with curated benchmarks or your own evals, and the router keeps routine audio on the Mini tier and escalates only when needed
- Security and compliance controls: Apply DLP rules and prompt injection protection before requests reach OpenAI, and enforce per-project model and region policies outside your application
How can I start routing requests to GPT Audio Mini via Merge Gateway?
Getting GPT Audio Mini running through Merge Gateway takes a few minutes:
1. Create an account and get your API key from the dashboard.
2. Install the Merge Gateway SDK: run pip install merge-gateway-sdk (Python) or npm install merge-gateway-sdk (Node). Alternatively, if you're already using the OpenAI SDK, set base_url = "https://api-gateway.merge.dev/v1/openai" and your existing code works as-is.
3. Make your first request using the provider/model format. For GPT Audio Mini, the model string is openai/gpt-audio-mini (confirm the exact slug in the dashboard). Swap the model string to route to any other provider without changing anything else.
4. Configure a routing policy in the dashboard to set failover behavior, cost limits, and optimization strategy. Your first policy can be as simple as naming GPT Audio Mini as primary with one fallback.
Full setup instructions and SDK references are in the Merge Gateway docs.
Try GPT Audio Mini through Merge Gateway
Route, observe, and control AI requests across providers from one API.
