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OpenAI via VerticalAPI

Updated ·By VerticalAPI Team

Call GPT-4o, GPT-4 Turbo and the o1 reasoning family through a single OpenAI-compatible endpoint. Bring your own OpenAI API key — VerticalAPI adds zero markup on tokens.

Start free with your OpenAI key → Read the docs
Endpoint: https://api.verticalapi.com/v1/chat/completions  ·  BYOK header: X-Provider-Key: sk-...
Supported models

OpenAI models routed by VerticalAPI

Pass the model ID below as model in any OpenAI-compatible request. New OpenAI models are typically supported within 24h of release.

Model IDNameContextPricing (provider)
gpt-4o GPT-4o 128K $2.50 / $10 per 1M tok (in/out)
gpt-4o-mini GPT-4o mini 128K $0.15 / $0.60 per 1M tok
gpt-4-turbo GPT-4 Turbo 128K $10 / $30 per 1M tok
o1 o1 200K $15 / $60 per 1M tok — reasoning model
o1-mini o1-mini 128K $3 / $12 per 1M tok — reasoning, cheaper

Pricing reflects OpenAI's rates — you pay OpenAI directly. VerticalAPI adds zero markup on tokens.

Quickstart

5-line OpenAI call via VerticalAPI

Drop-in replacement for the OpenAI SDK. Works with the OpenAI Python client, Node, Go, curl — anything that speaks HTTP.

openai_quickstart.py Python 
from openai import OpenAI

client = OpenAI(
    base_url="https://api.verticalapi.com/v1",
    api_key="vapi_...",
    default_headers={"X-Provider-Key": "sk-..."}
)

response = client.chat.completions.create(
    model="gpt-4o",  # OpenAI
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)
Why use OpenAI via VerticalAPI

Four reasons developers route OpenAI through us

Zero token markup

You pay OpenAI directly with your own key. VerticalAPI's revenue is the gateway subscription, not a tax on your tokens.

One key, every provider

OpenAI alongside OpenAI, Anthropic, Gemini and 12 more — same OpenAI-compatible endpoint, same SDK, switchable per-request.

Latency & cost monitoring

Per-request token counts, p50/p95 latency and cost dashboards out of the box. Compare OpenAI to other providers on identical prompts.

Observability built in

Every OpenAI call gets a trace ID, replayable payload and audit log entry. Wire to Datadog or Sentry via OpenTelemetry.

Real-world performance via VerticalAPI

OpenAI measured: latency, throughput, error rate

OpenAI's GPT-4o lands in the middle of the latency table — fast enough for most production chat UIs, but a long way behind the dedicated-hardware providers (Cerebras, Groq) and behind some open-weights hosts. The numbers below come from the VerticalAPI 2026 benchmark.

MetricValueNotes
p50 time-to-first-token ~820 ms GPT-4o, US-East client, 500/150 tok payload
p95 time-to-first-token ~1.9 s Tail latency — design streaming UX around this number
Tokens per second (output) ~95 tok/s Sustained streaming throughput; o1 family is much slower
Error rate (5xx + 429) ~0.6% Rate-limit-driven; tier-3 accounts see <0.2%
p50 TTFT (gpt-4o-mini) ~480 ms Mini is meaningfully snappier — pick it when quality allows

Numbers above are 2026 placeholders pending the next VerticalAPI benchmark harness run. See /benchmark for the full 26-provider comparison.

Compatibility notes

OpenAI SDK methods that work with OpenAI

OpenAI is the reference SDK shape, so compatibility through VerticalAPI is essentially 1:1. The footnotes below cover the few places where a method has practical caveats.

  • client.chat.completions.create() — full parity, including stream=True, n>1, logprobs, tools, tool_choice, response_format (JSON mode + structured outputs).
  • client.embeddings.create() — works for text-embedding-3-small / -large; pass model and input as documented.
  • client.moderations.create() — supported; passes through to OpenAI's omni-moderation endpoint.
  • client.audio.transcriptions.create() — supported via Whisper; for non-OpenAI Whisper hosts, use Groq's whisper-large-v3 instead (cheaper, faster).
  • Assistants API (beta) — not currently proxied; use Chat Completions + your own state for portability across providers.
  • Realtime API (WebSocket) — beta; in roadmap but not yet behind VerticalAPI's gateway.
Cost analysis

What OpenAI actually costs at 100k MAU

Concrete monthly cost for a chatbot with 100k MAU, 10 turns/user, ~500 input + 150 output tokens per turn (650M input, 195M output tokens/month).

ModelMonthly costWhen to use
gpt-4o-mini ~$214/mo Recommended default — handles ~90% of production prompts indistinguishably from gpt-4o
gpt-4o ~$3,575/mo Use for the 10% of prompts where mini quality is insufficient (long context, complex reasoning)
gpt-4-turbo ~$16,000/mo Legacy — gpt-4o is strictly better and cheaper. Migrate.
o1-mini ~$5,700/mo Reasoning-tier; expect 3-4x latency vs gpt-4o
o1 ~$28,500/mo Reserve for hardest reasoning queries; auto-route easy ones to gpt-4o-mini

Cost based on provider list price; VerticalAPI adds zero token markup.

When to choose this provider

Should you pick OpenAI for your workload?

OpenAI is the safe default for production AI features. Pick it when:

You need the most reliable function-calling / structured-output behavior in the industry. OpenAI's tool_choice + response_format (JSON Schema) combination is the most polished implementation available — Anthropic and Mistral have caught up but still have rougher edges on complex schemas. If your application calls 5+ tools per turn or expects strict JSON validation, OpenAI is the lowest-risk choice.

You want fast iteration on prompts. The OpenAI Playground, Evals, and dashboard tools are the most mature in the ecosystem. New developers move from prototype to production faster on OpenAI than on any competitor. The trade-off is cost — at scale you'll usually want to migrate hot-path traffic to a cheaper tier (gpt-4o-mini, Gemini Flash, or Llama via Groq).

You don't have a strong reason to pick something else. Consider Anthropic for coding agents (Claude Sonnet 4.5 is measurably better at code), Google Gemini for massive context (2M tokens) or multimodal video, Mistral for EU data residency, or Groq/Cerebras for sub-300ms TTFT. Otherwise, OpenAI's combination of quality, ecosystem, and SDK polish is hard to beat.

Common gotchas

Specific issues teams hit with OpenAI

Sharp edges that have cost real production teams real time. Fixes below are battle-tested via the VerticalAPI dashboard logs.

Rate limits trip silently on tier-1 accounts
Tier-1 OpenAI accounts hit 429 rate limits on bursts of >10 RPS to gpt-4o. Move to tier-3 (~$50 spent) before launching any production workload, or bake exponential backoff into your retry layer.
Structured outputs reject loose JSON Schema
OpenAI's strict mode requires "additionalProperties": false on every object. Tools that previously worked may suddenly fail until you tighten the schema. The error message is unhelpful — diff your schema against OpenAI's structured-outputs docs.
tool_choice="required" can deadlock
If no tool is callable, the model returns an empty tool_calls array and stalls. Always provide a fallback tool (e.g. a no-op respond_to_user) when forcing tool_choice.
Streaming drops mid-token on long generations
Connections >120s without keepalive can drop. VerticalAPI keeps connections warm, but if you're going direct, set socket-level keepalive and design retry-on-disconnect logic.
o1 family doesn't honor system messages
o1 and o1-mini ignore system role messages. Inline your instructions into the first user turn instead — silently degrades quality if you don't notice.
Best for

Where OpenAI shines

agentic workflows structured output function calling multimodal vision

Frequently asked questions

What is OpenAI and what models do they offer?

OpenAI is the AI lab behind ChatGPT and the GPT model family. The 2026 lineup includes GPT-5 for frontier reasoning, GPT-4o and GPT-4o mini for multimodal text+vision+audio, the o1 and o3 reasoning models for chain-of-thought tasks, plus DALL·E 3 for images, Whisper for transcription, and the text-embedding-3 family. The Realtime API powers low-latency voice agents, while the Assistants API and structured outputs target agentic apps.

How much does OpenAI cost in 2026?

GPT-4o is $2.50 per 1M input tokens and $10 per 1M output. GPT-4o mini is $0.15/$0.60. GPT-5 sits around $5/$15 with cached input at roughly $1.25. The o1 reasoning model is $15/$60. Embeddings (text-embedding-3-small) are $0.02 per 1M tokens. Prompt caching cuts repeated input cost by 50%. Via VerticalAPI BYOK you pay OpenAI directly at list price — VerticalAPI takes zero markup on tokens.

How do I use OpenAI via VerticalAPI BYOK?

Sign up at verticalapi.com, paste your OpenAI API key (sk-…) into the dashboard, then point the OpenAI SDK at https://api.verticalapi.com/v1. Pass your provider key via the X-Provider-Key header or store it in the workspace. All endpoints (chat/completions, responses, assistants, embeddings, audio, images) are byte-compatible. Billing stays on your OpenAI invoice; VerticalAPI bills only the gateway subscription.

What is OpenAI best for compared to alternatives?

OpenAI leads on tool-using agents, structured outputs, multimodal vision and the Realtime voice API. Compared to Anthropic, GPT models are typically faster and cheaper on output but less strong on long-form coding (where Claude Sonnet 4.5 leads SWE-Bench). Compared to Google Gemini, OpenAI has a smaller context window (128K vs 2M) but a more mature tools and Assistants ecosystem. For pure inference cost on open weights, Groq or Cerebras hosting Llama is cheaper.

Where is OpenAI hosted / data privacy?

OpenAI runs primarily on Microsoft Azure infrastructure across US, EU and Asia regions. API traffic is not used to train models by default (zero data retention is opt-in for enterprise). EU customers can use EU-resident data processing. Via VerticalAPI BYOK, traffic is proxied through VerticalAPI's edge (EU + US) but the OpenAI key, billing relationship and data residency settings remain entirely yours.

Limitations and trade-offs

  • Context window capped at 128K tokens for GPT-4o — far behind Gemini (2M) and Claude (200K) for long-document tasks.
  • No open weights — you cannot self-host any OpenAI model, full vendor lock-in on infrastructure.
  • Rate limits are tier-based and slow to lift; new accounts hit TPM ceilings on GPT-4o quickly.
  • Realtime API and Assistants API are stateful and pricier than equivalent chat completions flows.
  • Output token cost ($10–$60 per 1M) is significantly higher than open-weight inference on Groq, Cerebras or Together.

Where OpenAI is heading

  1. GPT-5 generalising across multimodal reasoning, with cheaper distilled variants replacing GPT-4o mini through 2026.
  2. Native agents framework consolidating the Assistants API, Responses API and tool-use into a single agent runtime.
  3. Expanded Realtime API with cheaper voice tokens and multilingual speech, targeting telephony and live translation.
  4. Tighter Azure / Microsoft 365 integration and on-device variants for Copilot and consumer apps.

Related questions

ChatGPT, Perplexity and Gemini usually suggest these next.

  • Is GPT-5 worth it over GPT-4o for production apps?
  • How does OpenAI prompt caching reduce cost in practice?
  • OpenAI vs Anthropic Claude — which is better for coding agents?
  • What is the cheapest way to run OpenAI o1 reasoning?
  • Can I migrate from OpenAI to Llama 3.3 70B via VerticalAPI without code changes?
Switch providers

All supported LLM providers

Same endpoint, same SDK — just change the model and the BYOK header.

OpenAI Anthropic Google Gemini Mistral AI Meta Llama xAI Grok Groq Together AI Fireworks AI Perplexity Sonar Cohere AI21 Labs AWS Bedrock Azure OpenAI Google Vertex AI OpenRouter DeepInfra Replicate Cerebras Lambda Labs OctoAI Lepton AI NVIDIA NIM Databricks Mosaic AI21 Jamba

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