AI API Pricing 2026: Live Price Table in USD and Turkish Lira, With Real Cost Scenarios

AI API pricing is quoted per one million tokens: as of August 5, 2026, input prices for popular models range from $0.15/1M (GPT-5.6 Luna, ~7.13 TL) up to $7.50/1M (Claude Opus 5, ~356.66 TL), and output tokens typically cost 2-6x more than input. Below you will find a 12-model USD + TL table and two real-world monthly cost calculations.

Because price lists are almost always published in dollars, teams budgeting from Turkey wrestle with two extra questions: "how many lira will this cost per month?" and "which tier is enough for this job?". This guide answers both with real platform data: sale prices taken from the live catalog, Turkish lira equivalents at the official TCMB rate, and two production scenarios with the math shown in full.

The figures in the table were measured on the api.onysoft.com live catalog on August 5, 2026; the always-current list lives on the models page.

How to Read Token-Based Pricing

AI API billing is usage-based rather than subscription-based: the text you send (input) and the text the model generates (output) are split into units called tokens, and the two sides are billed separately. Prices are almost always quoted per one million tokens, and the critical detail is this: output tokens cost 2-6x more than input on nearly every model. That is why an application that generates long reports and one that reads long documents but replies briefly can produce wildly different bills at the same traffic level.

One million tokens corresponds to roughly a few hundred thousand words — thousands of pages of documents. Since an average chat turn consumes a few hundred to a few thousand tokens, 1M tokens can cover thousands of customer messages. A note that matters for teams in Turkey: Turkish, as an agglutinative language, produces noticeably more tokens than the equivalent English text, so budget a margin for it.

Before comparing any two models, pin down three numbers: your input-to-output ratio, your total monthly token volume, and how much of the context window you actually fill.

The Live Price Table: 12 Models in USD and Turkish Lira

The table below shows current sale prices for 12 popular models on the api.onysoft.com live catalog; Turkish lira equivalents are computed at the August 5, 2026 TCMB rate (1 USD = 47.555 TL). All prices are per one million tokens.

Model	Input ($/1M)	Output ($/1M)	Input (TL/1M)	Output (TL/1M)	Context
Claude Opus 5	$7.50	$37.50	356.66 TL	1,783.31 TL	1M
Claude Sonnet 5	$3.00	$15.00	142.67 TL	713.33 TL	1M
Claude Haiku 4.5	$1.50	$7.50	71.33 TL	356.66 TL	200K
GPT-5.6 Sol	$7.50	$45.00	356.66 TL	2,139.98 TL	1.05M
GPT-5.6 Terra	$1.50	$9.00	71.33 TL	428.00 TL	1.05M
GPT-5.6 Luna	$0.15	$0.90	7.13 TL	42.80 TL	1.05M
Gemini 3.6 Flash	$2.25	$11.25	107.00 TL	534.99 TL	1M
DeepSeek V4 Flash	$0.21	$0.42	9.99 TL	19.97 TL	1M
DeepSeek V4 Pro	$0.65	$1.31	30.91 TL	62.30 TL	1M
Kimi K3	$4.50	$22.50	214.00 TL	1,069.99 TL	1M
Grok 4.5	$3.00	$9.00	142.67 TL	428.00 TL	500K
Qwen3 Max	$1.17	$5.85	55.64 TL	278.20 TL	262K

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

This is the most-asked slice, not the whole catalog: the models page lists 708+ models from more than 60 providers with live TL prices. Claude models are offered under our Anthropic Claude Partner Network membership. Note the ratio the table reveals: a 50x gap between the highest and lowest input price (Opus 5 / Sol versus Luna) — which means the real budget decision is not the provider but the tier.

Two Real Scenarios: What Does a Month Actually Cost?

Applying the table to two real workloads: a chatbot summarizing 1,000 customer queries per day costs about 642 TL/month on GPT-5.6 Luna, and a coding assistant for a five-developer team costs about 5,650 TL/month on Claude Sonnet 5. The full math is below; swap in your own traffic and verify it in the calculator.

Scenario 1 — Support chatbot (GPT-5.6 Luna): 1,000 customer queries summarized/answered per day.

• Assumption: 1,200 input tokens per request (message + system prompt + context) and 300 output tokens.
• Monthly volume (30 days): 30,000 requests → 36M input + 9M output tokens.
• Input: 36 × $0.15 = $5.40 · Output: 9 × $0.90 = $8.10 · Total: $13.50/month ≈ 642 TL/month.

Scenario 2 — Coding assistant (Claude Sonnet 5): 5 developers, 40 requests each per day, 22 working days.

• Assumption: 4,000 input tokens per request (code context) and 1,000 output tokens.
• Monthly volume: 5 × 40 × 22 = 4,400 requests → 17.6M input + 4.4M output tokens.
• Input: 17.6 × $3.00 = $52.80 · Output: 4.4 × $15.00 = $66.00 · Total: $118.80/month ≈ 5,650 TL/month — roughly 1,130 TL per developer.

Both calculations use the August 5, 2026 TCMB rate (47.555). Validate the assumptions by measurement, not guesswork: run your real prompt in the Playground and read the actual input/output token counts — most teams underestimate output length.

Which Tier for Which Job? Model Selection Backed by Real Usage Data

Our platform's real usage data from the last 30 days shows where experienced teams route their volume: first place goes to the economy-class Gemini 3.5 Flash Lite with 4,551 requests, while the flagship Claude Opus 5 sits in ninth place with 73. In production, volume concentrates in the economy tier and flagships are reserved for a small number of critical tasks — exactly what the table's 50x price spread calls for.

A practical tier matrix:

• High volume + simple task (intent detection, tagging, short summaries, routing): economy tier — GPT-5.6 Luna, DeepSeek V4 Flash, the flash-lite class. Using a flagship here is like delivering local parcels by airplane.
• Customer-facing production app (support bots, document Q&A over RAG, content drafts): mid tier — Claude Haiku 4.5, GPT-5.6 Terra, Gemini 3.6 Flash, DeepSeek V4 Pro; the most balanced region of the quality-cost curve.
• Low volume + high stakes (contract analysis, architectural code decisions, multi-step agents): flagship tier — Claude Opus 5, GPT-5.6 Sol, Grok 4.5; the cost of a mistake dwarfs the cost of tokens.

Advanced teams apply cascade routing: every request goes to an economy model first and is escalated only when the model is not confident. The usage distribution above is that pattern showing up in the field. Compare the Claude family tier by tier on the Claude models page and the GPT family on the GPT models page.

How It Works on Onysoft: TL Balance, TCMB Rate, One API

The mechanics take four steps: register for free, generate your sk-ony- prefixed API key in the panel, top up a Turkish lira balance, and usage is deducted from it at the current TCMB rate. No foreign card, no dollar account, no VPN; corporate e-invoices are issued and there is no subscription. The API is fully OpenAI-compatible, so only two lines change in existing code:

from openai import OpenAI

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

response = client.chat.completions.create(
    model="openai/gpt-5.6-luna",
    messages=[
        {"role": "user", "content": "Summarize this customer message in one sentence: ..."}
    ],
    max_tokens=300,
)
print(response.choices[0].message.content)

The same key reaches the entire catalog; switching models is as easy as changing a parameter. The Scenario 2 coding-assistant request with curl:

curl https://api.onysoft.com/v1/chat/completions \
  -H "Authorization: Bearer sk-ony-YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "anthropic/claude-sonnet-5",
    "messages": [{"role": "user", "content": "Refactor this function: ..."}],
    "max_tokens": 1000
  }'

On speed, a real measurement: across 6,959 successful requests in the last 14 days, the average end-to-end response time was 3.8 seconds, with the fastest response at 0.3 seconds (it varies by model and prompt length). See the documentation for parameters, error codes, and per-language examples.

Token Economy: Five Practical Ways to Lower the Bill

In most applications the largest line item is output tokens, so the most effective savings levers are the ones that manage output length and tier choice.

1. Cap the output: set max_tokens and add instructions like "answer briefly and precisely" to your prompt. Since output costs 2-6x more than input, every trim here lands directly on the bill.
2. Build cascade routing: send requests to an economy model first and escalate only those below your confidence threshold; in most production pipelines only a small minority of requests reach the upper tier.
3. Slim down the system prompt: a long system prompt re-sent with every request is the invisible line item of your input bill. In Scenario 1, shortening the system prompt by 400 tokens saves 12M tokens per month — measurable even at Luna prices.
4. Measure the Turkish overhead: Turkish text produces more tokens than the equivalent English. Run your own text in the Playground, read the real token count, and budget from that measurement.
5. Price three tiers and run a sensitivity test: compute the same volume in the calculator on an economy, a mid-tier, and a flagship model, then increase and decrease output length by 50%. You will see which variable your bill really depends on before you ship.

For the full set of routing and prompt-slimming techniques, see the cost optimization guide.

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