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o3 vs Pareto 26.9: API price, context & features · api14

OpenAI: o3 vs Pareto 26.9

A side-by-side comparison of OpenAI: o3 ($7.00 / 1M tokens) and Pareto 26.9 ($8.75 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	OpenAI: o3	Pareto 26.9
Provider	OpenAI	Unbiased
Input	$7.00 / 1M tokens	$8.75 / 1M tokens
Output	$28.00 / 1M tokens	$26.25 / 1M tokens
Cache read	$1.75 / 1M tokens	$0.88 / 1M tokens
Web search	$35.00 / 1K searches	-
Context window	200,000	262,144
Max output	100,000	131,072
Input	image, text, file	text, image
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Apr 16, 2025	Oct 13, 2026

Key differences

OpenAI: o3 is 20% cheaper on input tokens.
Pareto 26.9 is 6% cheaper on output tokens.
Pareto 26.9 reads 1.3× more context (262,144 tokens).
Only OpenAI: o3 supports structured JSON output.
Only OpenAI: o3 supports reasoning effort control.
A request with 1,000 input and 500 output tokens costs $0.02 with OpenAI: o3 and $0.02 with Pareto 26.9.

In depth

OpenAI: o3 is a text model from OpenAI, released on 2025-04-16. Pareto 26.9 is a text model from Unbiased, released on 2026-10-13. Both are served through api14 as chat style models, and both accept text and image input and return text output, so at the level of basic request shape they are interchangeable.

The differences show up in context, modalities and parameters. Pareto 26.9 has the larger context window at 262144 tokens, while o3 sits at 200000 tokens. o3 accepts file input in addition to text and image, which Pareto 26.9 does not. On parameters, o3 exposes reasoning controls (include_reasoning and reasoning) and structured_outputs, and Pareto 26.9 exposes none of those. Pareto 26.9 instead exposes temperature and top_p, which o3 does not list. Both support tools, tool_choice, max_tokens and response_format, so function calling and JSON style output are available on either side. The pricing fields point in opposite directions: o3 is the cheaper option on input, Pareto 26.9 is the cheaper option on output. Treat those as directional only and check the live price table on this page for the numbers that apply to your traffic mix.

For task selection, the reasoning and structured output parameters make o3 the natural pick when you need the model to work through a problem before answering, or when you want to constrain the response to a schema via structured_outputs rather than parsing free text. It is also the pick when your input includes files, since that modality is only on o3. Pareto 26.9 is the pick when the prompt itself is very large, because its context window is bigger, and when you want direct control over sampling through temperature and top_p. If your workload is output heavy, Pareto 26.9 is also the cheaper side on output pricing, which matters more the longer your completions run.

Calling either model through api14 works the same way. Both are OpenAI compatible, so you point your existing client at the api14 base URL, send a standard chat completions request, and change only the model id: "openai/o3" for o3, "unbiased/pareto-26.9" for Pareto 26.9. Your message array, tool definitions and response handling stay as they are. The one thing to watch is parameters: if you send temperature or top_p to o3, or reasoning or structured_outputs to Pareto 26.9, those fields are not in that model's supported list, so keep per model parameter sets rather than one shared payload.

There are still cases where the other model wins. If you have settled on Pareto 26.9 for a long context pipeline but a single step needs a schema constrained response, o3 is the better pick for that step because structured_outputs is only listed there. Conversely, if you are using o3 for a task that is mostly long output and no reasoning is required, Pareto 26.9 is the better pick on output price and gives you temperature control that o3 does not.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "openai/o3" to "unbiased/pareto-26.9" and prompts, streaming and tools keep working.

OpenAI: o3 details & playground →Pareto 26.9 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": "unbiased/pareto-26.9",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

OpenAI: o3 vs Pareto 26.9

Specifications

Key differences

In depth

Switch with one string

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