Claude Sonnet 5.5 vs GPT-6 Luna vs Gemini 4 Argon: API Cost

On price alone, GPT-6 Luna wins by a wide margin: 24.3 Credits ($0.122) per 1,000 requests on Orux AI, against 594 Credits ($2.97) for Gemini 4 Argon and 855 Credits ($4.275) for Claude Sonnet 5.5, assuming 2,000 input and 500 output tokens per request (prices as of Oct 29, 2026). At official list prices the same workload costs $0.45, $9.00 and $9.00, and Gemini 4 Argon's official price doubles on Dec 1, 2026.

That gap is not an accident. GPT-6 Luna is positioned for focused, high-volume tasks such as classification and extraction; Claude Sonnet 5.5 and Gemini 4 Argon are general-purpose models for heavier reasoning and writing. The useful question is not which one is cheapest, but whether your task actually needs the expensive one.

Quick verdict table

Swipe horizontally to view the full table.

The "positioned for" row quotes each model's description on its Orux AI model page. It is not a quality ranking.

Pricing compared

All prices below are per 1M tokens, checked on Oct 29, 2026. Orux AI bills in Credits at a fixed 1 Credit = $0.005; the dollar figures in brackets are that conversion.

Swipe horizontally to view the full table.

Three details worth knowing:

• GPT-6 Luna's long-context boundary on Orux AI is 270K input tokens. OpenAI's own boundary is 272K. A request with between 270K and 272K input tokens is billed at the higher tier on Orux AI and the lower one at OpenAI.
• Claude Sonnet 5.5 cache writes cost extra: 237.5 Credits per 1M tokens for a 5-minute cache (1.25x input) and 380 Credits for a 1-hour cache (2x input). Reads at 12 Credits are where the savings come from.
• Gemini 4 Argon's official price doubles on Dec 1, 2026. Any Orux AI price change will be published on the model page; this article does not assume one either way. The Gemini API pricing guide tracks the timeline.

Cost per task: three workloads

Per-token prices are hard to reason about, so here is the same arithmetic on three request shapes. Costs are per 1,000 requests, computed from the rates above.

Swipe horizontally to view the full table.

At current official prices Claude Sonnet 5.5 and Gemini 4 Argon cost exactly the same. On Orux AI, Gemini 4 Argon is about 30% cheaper than Claude Sonnet 5.5 for every workload shape, because both its input and output rates are 69.5% of Sonnet's.

If GPT-6 Luna is too small for your task but Sonnet is more than you need, Claude Haiku 5.5 sits in between: 10.8 / 54 Credits per 1M tokens for input up to 100K, or 48.6 Credits per 1,000 reference requests. The full ranking of 14 models is in Cheapest LLM API in 2026.

Same prompt, three models

We have not published a three-way quality result for this article, and we are not going to guess one. What we can show is one real measured call and what the same token counts would cost on the other two.

In an earlier Orux AI test, Claude Sonnet 5.5 extracted four museum inventory records into validated JSON. The first call used 402 input tokens and 195 output tokens, which at Orux AI rates is 402 × 190 / 1M + 195 × 950 / 1M = 0.2616 Credits (about $0.0013). Priced at the same token counts:

Swipe horizontally to view the full table.

Each model uses its own tokenizer and writes answers of different length, so real counts will differ. The comparison script below records actual usage, output and latency for each model so you can run the test on your own task. For the full JSON extraction walkthrough, including the validator, see the Claude Sonnet 5.5 API guide.

Context window and parameters

The three models take the same OpenAI-compatible request on Orux AI, with two differences that will break naive code:

• GPT-6 Luna uses max_completion_tokens, which limits both visible output and reasoning tokens. Claude Sonnet 5.5 and Gemini 4 Argon use max_tokens.
• GPT-6 Luna exposes a reasoning_effort setting; the Orux AI playground offers it in "direct answer" mode.

All three support streaming and context caching on Orux AI, and none of them currently support batch mode on Orux AI. Anthropic's own Batch API is 50% off, so for large offline Claude jobs, compare against that price.

Response time: GPT-6 Luna averaged 7.8 seconds across 5 recent completed requests on Orux AI in October 2026. That sample is too small to rank speed, and there is no comparable sample for the other two yet.

Note on GPT-6 Sol and Astra

GPT-6 Sol and GPT-6 Astra are not available on Orux AI, so this comparison does not include them. If you need either one, call OpenAI directly and check the current rates on the OpenAI pricing page. On Orux AI, GPT-6 Luna is the only GPT-6 model today, which is why it is the GPT-6 entry here even though it is positioned as a smaller, high-volume model.

Which one to start with

Based on price and positioning only, since we have not published a quality benchmark:

• Start with GPT-6 Luna for classification, routing, tagging, short extraction and any task where the answer is a few words or a small JSON object. At 24.3 Credits per 1,000 reference requests, you can afford to run a validation pass on every output and still spend less than one Sonnet call.
• Try Gemini 4 Argon when Luna's answers are not good enough and you want a general-purpose model at the lower of the two larger prices. Budget for the Dec 1 official price change if you also run it outside Orux AI.
• Use Claude Sonnet 5.5 where its positioning fits, such as debugging and multi-step drafting, and where you have already confirmed it does the job better than the other two on your prompts.

A common pattern is to route by task: Luna first, escalate to Argon or Sonnet only when a check fails. Because all three share one endpoint and one key on Orux AI, the routing logic is a few lines in your own code.

Python: swap models with one line

This script sends one prompt to all three models through a single client, handles the parameter difference, and prints the answer, token usage, an estimated Credits charge and wall-clock time.

# pip install openai
import os, time
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. GPT-6 Luna: under-270K tier.
RATES = {
    "claude-sonnet-5-5": (190, 950),
    "gpt-6-luna": (5.4, 27),
    "gemini-4-argon": (132, 660),
}

PROMPT = """Extract every record as a JSON array of objects with keys
id, title, location, estimated_value_usd. Use null when a value is unknown
or conflicting. Return JSON only.

1. ID A-104, "Blue Vase", Gallery A, valued at $1,200
2. ID A-105, "Bronze Bell", Gallery B, valued at $800 or $950 (two appraisals)"""

def ask(model, prompt, limit=600):
    params = {"model": model, "messages": [{"role": "user", "content": prompt}]}
    # The one parameter difference between the three
    params["max_completion_tokens" if model == "gpt-6-luna" else "max_tokens"] = limit
    start = time.perf_counter()
    resp = client.chat.completions.create(**params)
    return resp, time.perf_counter() - start

for model, (rin, rout) in RATES.items():
    resp, secs = ask(model, PROMPT)
    u = resp.usage
    credits = (u.prompt_tokens * rin + u.completion_tokens * rout) / 1e6
    print(f"--- {model}: {u.prompt_tokens}/{u.completion_tokens} tokens, "
          f"≈ {credits:.6f} Credits, {secs:.1f} s, finish={resp.choices[0].finish_reason}")
    print(resp.choices[0].message.content)

Switching a production call from one model to another is the model string plus, for GPT-6 Luna, the output-limit parameter name. Nothing else changes: same base URL, same key, same response shape.

Prices checked on 2026-10-29. Sources: Anthropic pricing, OpenAI pricing, Gemini API pricing, and the Orux AI model pages linked above. For the full Claude price breakdown, including Opus 5.5 and Haiku 5.5, see Claude API pricing.

FAQ

Q.Which is cheapest: Claude Sonnet 5.5, GPT-6 Luna or Gemini 4 Argon?

GPT-6 Luna is by far the cheapest: 5.4 / 27 Credits per 1M input and output tokens on Orux AI ($0.027 / $0.135), or 24.3 Credits per 1,000 requests of 2,000 input and 500 output tokens. Gemini 4 Argon costs 594 Credits and Claude Sonnet 5.5 costs 855 Credits for the same workload.

Q.Which is best for coding?

We have not run a coding benchmark across these three, so we will not name a winner. Claude Sonnet 5.5's model page describes it for debugging and iterative problem solving. On cost, a 3,000-input, 2,000-output coding request costs 2.47 Credits on Sonnet, 1.72 on Argon and 0.07 on Luna at Orux AI rates.

Q.Can I switch between them without code changes?

Almost. On Orux AI all three use the same OpenAI-compatible endpoint and key, so switching is a change to the model field. The one exception is the output limit: GPT-6 Luna takes max_completion_tokens, while Claude Sonnet 5.5 and Gemini 4 Argon take max_tokens.

Q.Is Gemini 4 Argon getting more expensive?

Yes, at Google. Gemini 4 Argon's official price rises from $2 / $10 to $4 / $20 per 1M tokens on Dec 1, 2026. The Orux AI price on Oct 29, 2026 is 132 / 660 Credits; any change will be published on the model page before it takes effect.

Q.Is GPT-6 Luna a replacement for Claude Sonnet 5.5?

Not by default. GPT-6 Luna is positioned for classification, extraction and concise answers, while Claude Sonnet 5.5 targets broader work such as drafting and debugging. Route narrow, high-volume tasks to Luna and test whether quality holds; at 35 times cheaper per request, it is worth checking.