We use cookies. Cookies and browser storage keep you signed in and remember your preferences. With your permission we also measure how the site is used. You can any time. See our cookie policy.

New here? See how it works A 1-minute tour of the playground, models and your API key.

DeepSeek V4 Pro 0423 vs GPT-6 Luna: API price, context & features · api14

DeepSeek: DeepSeek V4 Pro 0423 vs OpenAI: GPT-6 Luna

A side-by-side comparison of DeepSeek: DeepSeek V4 Pro 0423 ($5.60 / 1M tokens) and OpenAI: GPT-6 Luna ($0.35 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	DeepSeek: DeepSeek V4 Pro 0423	OpenAI: GPT-6 Luna
Provider	DeepSeek	OpenAI
Input	$5.60 / 1M tokens	$0.35 / 1M tokens
Output	$11.20 / 1M tokens	$1.75 / 1M tokens
Cache read	$0.47 / 1M tokens	$0.04 / 1M tokens
Cache write	-	$0.44 / 1M tokens
Web search	-	$35.00 / 1K searches
Context window	1,048,576	1,050,000
Max output	393,216	128,000
Input	text	file, image, text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Apr 24, 2026	Sep 22, 2026

Key differences

OpenAI: GPT-6 Luna is 94% cheaper on input tokens.
OpenAI: GPT-6 Luna is 84% cheaper on output tokens.
A request with 1,000 input and 500 output tokens costs $0.01 with DeepSeek: DeepSeek V4 Pro 0423 and $0.00123 with OpenAI: GPT-6 Luna.

In depth

DeepSeek: DeepSeek V4 Pro 0423 is a text model from DeepSeek, released on 2026-04-24. OpenAI: GPT-6 Luna is a text model from OpenAI, released on 2026-09-22. Both are served through api14 under the same chat completions style interface, so the choice between them comes down to what your task actually needs rather than to any difference in how you reach them.

The two models are close on context. DeepSeek V4 Pro 0423 accepts 1,048,576 tokens and GPT-6 Luna accepts 1,050,000, which is not a meaningful difference in practice, so neither wins on how much you can fit into a single request. The real split is modality. DeepSeek V4 Pro 0423 takes text in and returns text out. GPT-6 Luna also takes text, but additionally accepts images and files as input, still returning text. If your pipeline needs to hand a model a screenshot, a scanned page or an attached document, GPT-6 Luna is the one that can accept it directly. On parameters, DeepSeek V4 Pro 0423 exposes a wider sampling surface, including frequency_penalty, presence_penalty, logit_bias, logprobs, top_logprobs, min_p, top_k, repetition_penalty and seed, alongside tools, tool_choice, structured_outputs, response_format and the reasoning controls. GPT-6 Luna covers tools, tool_choice, structured_outputs, response_format, seed and the reasoning controls, plus a verbosity parameter, but leaves out the logprob and penalty style knobs. Neither model is listed as uniquely supporting tools, structured outputs or reasoning, so those are not tiebreakers. On price, GPT-6 Luna is the cheaper option for both input and output.

For a straightforward text task, such as summarising long documents, extracting structured fields, or running an agent loop over tool calls, either model will do the job, and the deciding factors are cost and the sampling controls you rely on. If you tune generation with penalties, top_k, min_p or logprobs, DeepSeek V4 Pro 0423 gives you those levers directly. If you want a lower bill on high volume text traffic, GPT-6 Luna is the cheaper call on both input and output tokens. When your input includes an image or a file, GPT-6 Luna is the only one of the two that accepts it, so that case decides itself.

Calling either model through api14 works the same way. Point your client at the OpenAI-compatible endpoint, send a normal chat completions request, and set the model field to "deepseek/deepseek-v4-pro" or "openai/gpt-6-luna". Everything else, your messages array, your tool definitions, your response_format, stays as it is. Switching between the two is a one line change to the model id, which makes it cheap to benchmark both against your own workload before committing.

There is one case where DeepSeek V4 Pro 0423 remains the better pick even though GPT-6 Luna is cheaper: when you need fine grained control over sampling and inspection. The penalty parameters, top_k, min_p, logit_bias and the logprob outputs are all available on DeepSeek V4 Pro 0423 and absent from GPT-6 Luna, so work that depends on constraining or measuring token level behaviour should stay on the DeepSeek model.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "deepseek/deepseek-v4-pro" to "openai/gpt-6-luna" and prompts, streaming and tools keep working.

DeepSeek: DeepSeek V4 Pro 0423 details & playground →OpenAI: GPT-6 Luna details & playground →

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api14.com/api/v1",
  apiKey: process.env.API14_API_KEY,
});

const result = await client.chat.completions.create({
  "model": "openai/gpt-6-luna",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

DeepSeek: DeepSeek V4 Pro 0423 vs OpenAI: GPT-6 Luna

Specifications

Key differences

In depth

Switch with one string

More comparisons