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Mistral Medium 3.5 vs o3: API price, context & features · api14

Mistral: Mistral Medium 3.5 vs OpenAI: o3

A side-by-side comparison of Mistral: Mistral Medium 3.5 ($5.25 / 1M tokens) and OpenAI: o3 ($7.00 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Mistral: Mistral Medium 3.5	OpenAI: o3
Provider	Mistral	OpenAI
Input	$5.25 / 1M tokens	$7.00 / 1M tokens
Output	$26.25 / 1M tokens	$28.00 / 1M tokens
Cache read	-	$1.75 / 1M tokens
Web search	-	$35.00 / 1K searches
Context window	262,144	200,000
Max output	209,715	100,000
Input	text, image, file	image, text, file
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Apr 30, 2026	Apr 16, 2025

Key differences

Mistral: Mistral Medium 3.5 is 25% cheaper on input tokens.
Mistral: Mistral Medium 3.5 is 6% cheaper on output tokens.
Mistral: Mistral Medium 3.5 reads 1.3× more context (262,144 tokens).
A request with 1,000 input and 500 output tokens costs $0.02 with Mistral: Mistral Medium 3.5 and $0.02 with OpenAI: o3.

In depth

Mistral: Mistral Medium 3.5 is made by Mistral, and OpenAI: o3 is made by OpenAI. Both are text models, meaning the reply you get back is text, and both are served through api14 as part of the same catalog. The two differ in when they appeared: o3 has a provider release date of 2025-04-16, while Mistral Medium 3.5 carries a provider release date of 2026-04-30. That is the whole of the framing you need before looking at what each one accepts and how much room it gives you.

On capabilities, the two are closer than they look. Both accept text, image and file input, and both return text only, so neither is an audio or video model. The context windows are where they separate: Mistral Medium 3.5 offers 262144 tokens, and o3 offers 200000 tokens, so Mistral Medium 3.5 gives you the larger window. On parameters, Mistral Medium 3.5 supports frequency_penalty, presence_penalty, temperature, top_p, stop, seed, max_tokens, response_format, structured_outputs, tools, tool_choice, reasoning, reasoning_effort and include_reasoning. o3 supports a narrower set: max_tokens, response_format, structured_outputs, tools, tool_choice, seed, reasoning and include_reasoning. Notably, o3 does not list temperature, top_p, frequency_penalty or presence_penalty, and it does not list reasoning_effort, so if your code tunes sampling or sets a reasoning effort level, Mistral Medium 3.5 is the one that will accept those fields. Neither model is flagged as uniquely owning tools, structured outputs or reasoning, so treat those as shared ground rather than a differentiator.

For picking between them, start with the shape of the job. If you are feeding long documents, large codebases or multi-file context and you want the extra headroom, Mistral Medium 3.5 is the natural default because its window is bigger. If your prompt pipeline already relies on temperature, top_p or penalty knobs, or if you want to dial reasoning effort explicitly, Mistral Medium 3.5 is again the safer fit since those parameters are supported. If your workload is a straightforward reasoning or tool-calling task where you do not need to tune sampling and 200000 tokens is comfortably enough, o3 is a reasonable choice and keeps your parameter surface smaller. Cost is a factor too: Mistral Medium 3.5 is the cheaper of the two on both input and output, so for high-volume traffic where the larger window is not strictly required, the price difference alone may decide it.

Calling either model through api14 is the same motion. The API is OpenAI-compatible, so you keep your existing client, your existing request body shape and your existing auth. The only thing that changes is the model id string: use "mistralai/mistral-medium-3-5" to route to Mistral Medium 3.5, or "openai/o3" to route to o3. Everything else, the messages array, the tools array, the response_format object, stays where it is. If you are switching from o3 to Mistral Medium 3.5 and you want to use temperature or top_p, add those fields at the same time, because o3 would not have accepted them and your current payload probably omits them.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "mistralai/mistral-medium-3-5" to "openai/o3" and prompts, streaming and tools keep working.

Mistral: Mistral Medium 3.5 details & playground →OpenAI: o3 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/o3",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Mistral: Mistral Medium 3.5 vs OpenAI: o3

Specifications

Key differences

In depth

Switch with one string

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