One API for Multiple AI Models: Single Key, Single Balance, Single Bill

Using multiple AI models through one API means your application reaches models from different vendors through a single base_url, a single API key, and a single balance. On Onysoft AI Gateway, one sk-ony- key unlocks 708+ models from more than 60 providers; switching models is nothing more than changing the model field in the request, and all usage lands on one invoice.

Multi-model is no longer a luxury — it is the default architecture of serious production systems. The best price/performance for classification comes from one vendor's economy model while another vendor's flagship leads in code generation, and that leaderboard reshuffles every few months. Managing a separate account, SDK, and invoice per vendor is real operational overhead.

This guide covers what a single-API architecture buys you, which models developers actually call on our platform (a live 30-day usage ranking), current pricing with Turkish lira equivalents, and task-based model selection advice.

What Does One API for Multiple Models Mean, and Why Does It Matter?

One API for multiple models means placing a single OpenAI-compatible layer between your application and the model vendors: your app always calls the same endpoint, and the model field in the request body decides which model answers. Authentication, billing, and error formats stay identical across every model.

The alternative is working with each vendor separately: separate signup, separate API key, separate SDK and schema, separate invoice — and, for teams in Türkiye, frequent foreign-card and payment friction on top. With two vendors that may look manageable, but a modern production system mixes different models for classification, summarization, code generation, and agent workflows; as the vendor count grows, integration cost compounds rather than adds.

What the single-API layer buys you:

• One key, one balance, one bill: usage across all models draws from the same balance and lands on a single corporate e-invoice.
• One schema: code written with the OpenAI SDK keeps working when the model name points at a different vendor's model.
• Less lock-in: when a new model ships, migrating is a one-line change and an A/B test takes minutes.
• One place to measure: usage, cost, and latency for every model show up in the same dashboard.

Which Models Do Developers Actually Call? (Real 30-Day Data)

Instead of theory, here is real traffic: the table below shows the ten most-requested models on api.onysoft.com over the last 30 days — not a survey or an estimate, but the platform's own usage data.

Rank	Model	Requests (last 30 days)
1	google/gemini-3.5-flash-lite	4,551
2	google/gemini-3.1-pro-preview	1,981
3	google/gemini-3-flash-preview	1,372
4	Claude Opus (current alias)	296
5	google/gemini-3.6-flash	284
6	Claude Sonnet (current alias)	141
7	perplexity/sonar-pro	104
8	google/gemini-3.1-flash-lite	94
9	anthropic/claude-opus-5	73
10	anthropic/claude-haiku-4.5	48

Measured: August 5, 2026 — api.onysoft.com live catalog (last 30 days, successful requests).

Three clear takeaways. First, the speed/cost class carries the volume: roughly 70% of top-10 requests go to flash- and lite-class models — most production traffic is classification, summarization, and high-volume mechanical work. Second, three different vendors share the same list: Google, Anthropic, and Perplexity models run side by side behind the same keys, which is the entire point of a single-API architecture. Third, the "current alias" rows at ranks 4 and 6 are telling: a meaningful share of developers use -latest aliases instead of pinned versions, so new releases roll in without touching their code.

Current Prices on One Bill: 12 Models from 7 Vendors, with TRY Equivalents

Here is what a single API means on the billing side: the 12 models below come from 7 different vendors, yet all of them draw from the same balance and appear on one invoice. Prices are per 1M tokens.

Model	Context	Input (1M)	Output (1M)
anthropic/claude-opus-5	1M	$7.50 · ₺356.66	$37.50 · ₺1,783.31
anthropic/claude-sonnet-5	1M	$3.00 · ₺142.67	$15.00 · ₺713.33
anthropic/claude-haiku-4.5	200K	$1.50 · ₺71.33	$7.50 · ₺356.66
openai/gpt-5.6-sol	1.05M	$7.50 · ₺356.66	$45.00 · ₺2,139.98
openai/gpt-5.6-terra	1.05M	$1.50 · ₺71.33	$9.00 · ₺428.00
openai/gpt-5.6-luna	1.05M	$0.15 · ₺7.13	$0.90 · ₺42.80
google/gemini-3.6-flash	1M	$2.25 · ₺107.00	$11.25 · ₺534.99
deepseek/deepseek-v4-flash	1M	$0.21 · ₺9.99	$0.42 · ₺19.97
deepseek/deepseek-v4-pro	1M	$0.65 · ₺30.91	$1.31 · ₺62.30
moonshotai/kimi-k3	1M	$4.50 · ₺214.00	$22.50 · ₺1,069.99
x-ai/grok-4.5	500K	$3.00 · ₺142.67	$9.00 · ₺428.00
qwen/qwen3-max	262K	$1.17 · ₺55.64	$5.85 · ₺278.20

Measured: August 5, 2026 — api.onysoft.com live catalog. TRY equivalents calculated at the August 5, 2026 TCMB rate (1 USD = 47.555 TRY).

Note the 50x spread in input pricing: the gpt-5.6-luna and deepseek-v4-flash class runs at a small fraction of what flagships like opus-5 and gpt-5.6-sol cost. The real power of a single API is managing that whole range from one codebase — sending the cheap-to-carry share of your traffic to economy models and the critical share to flagships is a parameter change, not an architecture decision. Run your monthly volume through the cost calculator to compare scenarios side by side.

How Does the Catalog Stay Current?

The catalog is updated by automated synchronization, not by hand: the live model list is scanned at regular intervals, newly released models appear shortly after launch, deprecated ones drop out, and price changes flow through to the listed prices. That is why the site never shows a hard-coded model count — the 708+ figure is counted from the live catalog, and by the time you read this it may be higher than the day the article was written.

Freshness has two practical consequences for developers. First, -latest aliases: identifiers like anthropic/claude-opus-latest always point at the family's current version. As the usage table shows, a meaningful share of developers pick this route because model upgrades then require no code change. Pinned version identifiers (like anthropic/claude-opus-5) remain the right choice for production pipelines where behavioral consistency is critical.

Second, programmatic discovery: GET /v1/models always returns the live catalog. If your application fetches its model list from this endpoint instead of hard-coding it, new models show up in your product automatically. Browse the current list on the model catalog page, or try any of them without writing code in the Playground.

How It Works on Onysoft: From Setup to Your First Multi-Model Request

The mechanics are simple: create a free account, generate your sk-ony- key from the dashboard, and add balance — usage is deducted pay-as-you-go. In code, the only requirement is pointing base_url at https://api.onysoft.com/v1. Running the same prompt against three different vendors' models is a loop over a single client:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.onysoft.com/v1",
    api_key="sk-ony-YOUR_KEY",
)

models = [
    "anthropic/claude-sonnet-5",
    "google/gemini-3.6-flash",
    "deepseek/deepseek-v4-flash",
]

for model in models:
    r = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": "Summarize this support ticket in one sentence: ..."}],
    )
    print(model, "→", r.choices[0].message.content)

The same logic with curl — only the model field changes:

curl https://api.onysoft.com/v1/chat/completions \
  -H "Authorization: Bearer sk-ony-YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-3.6-flash",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

On performance, the real numbers: over the last 14 days, 6,959 successful requests went through the platform with an average end-to-end response time of 3.8 seconds and the fastest response measured at 0.3 seconds. That average includes long-output flagship requests; with speed-class models and streaming, perceived latency is far lower. Streaming, function calling, and JSON mode work across all models with the same OpenAI schema — details in the API documentation.

Task-Based Model Selection: Which Model for Which Job?

The healthy starting point is classifying the task and picking a strong model in that class; combining our usage and pricing data produces this map:

• High-volume classification, labeling, summarization: usage champion google/gemini-3.5-flash-lite is not at rank 1 by accident; deepseek/deepseek-v4-flash ($0.21/$0.42) and openai/gpt-5.6-luna ($0.15/$0.90) are the most economical options in this class.
• Code generation and agent workflows: anthropic/claude-sonnet-5 is a strong default on price/quality balance; for deep reasoning and critical decisions, anthropic/claude-opus-5 and openai/gpt-5.6-sol are the flagship class. Onysoft's membership in the Anthropic Claude Partner Network is a verifiable assurance layer for teams standardizing on Claude in corporate projects.
• Long documents and wide RAG contexts: the 1M-token class is broad — Claude Opus/Sonnet 5, the GPT-5.6 series (1.05M), Gemini 3.6 Flash, DeepSeek V4, and Kimi K3 all carry long contexts in a single request.
• Answers that need current information: perplexity/sonar-pro, present in the top 10, is the pick for web-grounded response scenarios.

Where you are unsure, hand the decision to OnyRouter: set the model field to onysoft/auto and the system analyzes each request and routes it to a suitable model. The routing itself is free — only the selected model's usage is billed, and the model field in the response transparently shows which model was chosen. Details in the OnyRouter guide.

Last updated: August 5, 2026 · Data: api.onysoft.com live catalog