Cheapest LLM API 2026: 14 Models by Cost per 1,000 Requests

The cheapest LLM API on Orux AI as of Oct 29, 2026 is LingDT 3.0 Flash at 20.4 Credits ($0.102) per 1,000 requests, followed closely by GPT-6 Luna at 24.3 Credits ($0.122). If you want a model with a published vendor price to compare against, GPT-6 Luna and Claude Haiku 5.5 (48.6 Credits, $0.243) are the cheapest, at 27% and 54% of their official list prices respectively.

Those numbers assume a fixed workload of 2,000 input tokens and 500 output tokens per request. Change the shape of the workload and the ranking moves, so this article gives you the full table, the assumptions behind it, and a script to measure your own prompts.

The short answer

Top five by cost per 1,000 requests (2,000 input + 500 output tokens each), Orux AI prices as of Oct 29, 2026:

1. LingDT 3.0 Flash: 20.4 Credits ($0.102). No official list price to compare against.
2. GPT-6 Luna: 24.3 Credits ($0.122) vs $0.45 at OpenAI's list price.
3. Claude Haiku 5.5: 48.6 Credits ($0.243) vs $0.45 at Anthropic's list price.
4. Ernie 4.5 Turbo 128K: 53.3 Credits ($0.267).
5. Qwen3.5 Plus: 75 Credits ($0.375) vs $0.574 at Alibaba Cloud's list price.

DeepSeek V4.1 Flash is sixth off-peak (91.7 Credits, $0.458) but doubles at peak hours, which matters more than most comparison tables admit. More on that below.

Methodology

Every price in this article is per 1M tokens, taken from the public model pages on Orux AI and from each vendor's own pricing page on Oct 29, 2026. Orux AI bills in Credits at a fixed rate of 1 Credit = $0.005 (200 Credits = $1); dollar figures in brackets are that conversion, not a separate price.

The reference workload is 2,000 input tokens and 500 output tokens per request, which is roughly a support reply with some retrieved context or a short summarization job. For 1,000 requests that is 2M input tokens and 0.5M output tokens, so:

cost per 1,000 requests = 2 × input rate + 0.5 × output rate

For example, GPT-6 Luna on Orux AI is 5.4 Credits per 1M input tokens and 27 Credits per 1M output tokens: 2 × 5.4 + 0.5 × 27 = 10.8 + 13.5 = 24.3 Credits.

We did not benchmark quality. A cheaper model that fails your task twice costs more than an expensive one that succeeds once, so treat this as a shortlist, not a verdict.

Full comparison table

Prices as of Oct 29, 2026. Orux AI rates in Credits per 1M tokens; official rates in USD per 1M tokens.

Swipe horizontally to view the full table.

Notes on the table:

• Alibaba Cloud marks the Qwen3.7 Plus official price as a limited-time 20% discount, so the 78% ratio can shift when that promotion ends.
• Gemini 4 Argon's official price rises to $4 / $20 on Dec 1, 2026, and Gemini 3.8 Flash rises to $1.50 / $7.50 on Jan 1, 2027. At those prices the official cost per 1,000 requests becomes $18.00 and $6.75. Orux AI rates after those dates will be published on the model pages; this article does not assume either way.
• We could not load Moonshot's pricing table on the day we checked, so Kimi K3 has no official comparison here.

Where Orux AI is not the cheaper option

Two DeepSeek details work against you if you pick the wrong alias or the wrong hour:

• deepseek-v4.1-flash-1017 is a fixed-price alias at 60 / 240 Credits per 1M tokens ($0.30 / $1.20). That equals DeepSeek's official peak price and is double its off-peak price, all day. For the reference workload it costs 240 Credits ($1.20) per 1,000 requests. Use deepseek-v4.1-flash instead unless you specifically need the 1017 snapshot.
• Orux AI applies DeepSeek peak pricing every day, 01:00–04:00 and 06:00–10:00 UTC. DeepSeek itself only applies peak pricing Monday to Friday. On weekend daytime hours (UTC+8), Orux AI charges the peak rate of $0.229 per 1M input tokens while DeepSeek direct charges the off-peak $0.15. If your traffic is weekend-heavy, DeepSeek direct is cheaper for those hours.

Some other models on the catalog, including MiniMax M3, Doubao and Kimi K2.x, are priced above their official rates on Orux AI, which is why they are not in this ranking. The DeepSeek API pricing guide covers peak and off-peak scheduling in detail.

Cheapest by workload shape

The reference workload is input-heavy. Output tokens cost 3 to 6 times more than input tokens on most models, so the ratio of input to output changes who wins. Costs below are Orux AI Credits per 1,000 requests, computed from the same Oct 29, 2026 rates.

Swipe horizontally to view the full table.

What this means in practice:

• For classification and RAG, where nearly all tokens are input, GPT-6 Luna edges out LingDT 3.0 Flash because its input rate (5.4) is lower.
• For generation-heavy work, LingDT 3.0 Flash's output rate (17.5 vs 27) makes it about a third cheaper than GPT-6 Luna.
• For JSON output, DeepSeek V4.1 Flash supports response_format: {"type": "json_object"} on Orux AI. It costs more per request than the two leaders, but strict JSON mode can save you a retry loop.
• For coding, we are not ranking quality. If you want a mid-priced option to test, MiniMax M3.1 Flash (96 Credits per 1,000 reference requests) is described on its model page as designed for coding and multi-step tasks.

Hidden cost factors

The per-token rate is only part of the bill. Check these before you commit.

• Long-context tiers. Claude Haiku 5.5 jumps from 10.8 / 54 to 56 / 280 Credits per 1M tokens once a request's input exceeds 100K tokens. A 120K-input, 1K-output request costs about 7.0 Credits instead of 1.35. GPT-6 Luna moves to 20 / 80 Credits at 270K input on Orux AI (OpenAI's own boundary is 272K). Qwen3.5 Plus and Qwen3.7 Plus also have higher tiers above 128K and 256K input; see their model pages.
• Peak and off-peak hours. DeepSeek V4.1 Flash and DeepSeek V4 Pro double at peak. Schedule batch jobs into off-peak windows.
• Prompt caching. Cache reads are far cheaper than fresh input: 1.1 Credits per 1M tokens on Claude Haiku 5.5, 0.54 on GPT-6 Luna. If 1,500 of your 2,000 input tokens are a stable system prompt that hits the cache, Haiku drops from 48.6 to about 34.1 Credits per 1,000 requests (before cache-write costs, which on Claude are 1.25x the input rate for a 5-minute cache).
• Minimum charge. Every chat request has a floor of 0.001 Credits. It only matters for very tiny calls.
• No batch discount. Anthropic offers 50% off for batch jobs on its own platform. Orux AI does not currently offer a batch mode, so for large offline jobs on Claude, compare against the vendor's batch price, not its standard price.
• Failed requests. On Orux AI, failed requests are not billed. You pay for actual usage from a prepaid balance, with no monthly fee.

Switch models without rewriting code

All models above use the same OpenAI-compatible endpoint, so a single client can compare them on your own prompts. The script below sends one prompt to five models, reads the token usage the API returns, and converts it to Credits using the published rates.

# pip install openai
import os
from datetime import datetime, timezone
from openai import OpenAI

client = OpenAI(base_url="https://orux.top/api/v1", api_key=os.environ["ORUX_API_KEY"])

# Credits per 1M tokens (input, output), checked 2026-10-29. Short-context tiers only.
RATES = {
    "lingdt-3.0-flash": (5.83, 17.5),
    "gpt-6-luna": (5.4, 27),
    "claude-haiku-5-5": (10.8, 54),
    "qwen3.5-plus": (15, 90),
    "deepseek-v4.1-flash": None,  # time-of-day pricing, see below
}

def deepseek_rate(now=None):
    """Orux AI applies DeepSeek peak pricing every day, 01-04 and 06-10 UTC."""
    h = (now or datetime.now(timezone.utc)).hour
    peak = 1 <= h < 4 or 6 <= h < 10
    return (45.83, 183.33) if peak else (22.92, 91.67)

PROMPT = "Classify this ticket as billing, bug or feature request. Reply with one word.\n\nTicket: I was charged twice this month."

for model, rate in RATES.items():
    rate = rate or deepseek_rate()
    params = {"model": model, "messages": [{"role": "user", "content": PROMPT}]}
    # GPT-6 Luna takes max_completion_tokens; the others take max_tokens
    params["max_completion_tokens" if model.startswith("gpt-6") else "max_tokens"] = 50
    resp = client.chat.completions.create(**params)
    u = resp.usage
    credits = (u.prompt_tokens * rate[0] + u.completion_tokens * rate[1]) / 1e6
    print(f"{model:22} {resp.choices[0].message.content.strip()[:30]:30} "
          f"{u.prompt_tokens}/{u.completion_tokens} tokens ≈ {credits:.6f} Credits")

Run it with your real prompts, multiply by your monthly request count, and you have an estimate grounded in your own token counts rather than a generic workload. The script only estimates; your balance history shows the exact charge.

For a deeper look at the Claude tiers in this table, see Claude API pricing. For a head-to-head of the mid-range options, see Claude Sonnet 5.5 vs GPT-6 Luna vs Gemini 4 Argon.

Prices checked on 2026-10-29. Sources: Anthropic pricing, OpenAI pricing, Gemini API pricing, DeepSeek pricing, Alibaba Cloud Model Studio pricing, MiniMax pricing, and the Orux AI model catalog.

FAQ

Q.What is the cheapest LLM API right now?

On Orux AI as of Oct 29, 2026, LingDT 3.0 Flash is the cheapest at 20.4 Credits ($0.102) per 1,000 requests of 2,000 input and 500 output tokens. GPT-6 Luna is close behind at 24.3 Credits ($0.122) and is cheaper for input-heavy work such as classification and RAG.

Q.Is a cheaper LLM worse?

Not necessarily, but price says nothing about fit. Small models such as GPT-6 Luna and Claude Haiku 5.5 are positioned for classification, extraction and short answers. Run your own prompts on two or three candidates and count retries; a model that needs a second attempt has doubled its effective price.

Q.How do I estimate monthly LLM API cost?

Multiply your average input and output tokens per request by the per-1M rates, then by monthly request volume. For example, 300,000 requests at 2,000 input and 500 output tokens on Claude Haiku 5.5 costs 48.6 × 300 = 14,580 Credits ($72.90) per month on Orux AI.

Q.Do LLM API prices change often?

Yes. Gemini 4 Argon's official price doubles on Dec 1, 2026 and Gemini 3.8 Flash's on Jan 1, 2027, and Qwen3.7 Plus is on a time-limited discount. We update this table monthly; always check the model page for the current rate before you commit to a budget.

Q.Can I use one API key for all of these models?

Yes. On Orux AI one key works for every model in this table through the same OpenAI-compatible endpoint, https://orux.top/api/v1. You only change the model field, plus max_completion_tokens instead of max_tokens for GPT-6 Luna. Create a key at https://orux.top/keys.

Q.Are failed requests charged?

No. Orux AI bills from a prepaid balance based on actual usage, with no monthly fee, and failed requests are not charged. Successful chat requests have a minimum charge of 0.001 Credits.