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Mistral Medium 3.5 vs Pareto 26.9: API price, context & features · api14

Mistral: Mistral Medium 3.5 vs Pareto 26.9

A side-by-side comparison of Mistral: Mistral Medium 3.5 ($5.25 / 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

	Mistral: Mistral Medium 3.5	Pareto 26.9
Provider	Mistral	Unbiased
Input	$5.25 / 1M tokens	$8.75 / 1M tokens
Output	$26.25 / 1M tokens	$26.25 / 1M tokens
Cache read	-	$0.88 / 1M tokens
Context window	262,144	262,144
Max output	209,715	131,072
Input	text, image, file	text, image
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Apr 30, 2026	Oct 13, 2026

Key differences

Mistral: Mistral Medium 3.5 is 40% cheaper on input tokens.
Only Mistral: Mistral Medium 3.5 supports structured JSON output.
Only Mistral: Mistral Medium 3.5 supports reasoning effort control.
A request with 1,000 input and 500 output tokens costs $0.02 with Mistral: Mistral Medium 3.5 and $0.02 with Pareto 26.9.

In depth

Mistral: Mistral Medium 3.5 is made by Mistral, and Pareto 26.9 is made by Unbiased. Both are text generation models, meaning both take input and return text, and both are served through api14 alongside many other models. Mistral Medium 3.5 was released by its provider on 2026-04-30, while Pareto 26.9 carries a provider release date of 2026-10-13. If you are choosing between them, the practical differences come down to modalities, parameter support and price rather than to any published score.

The two models share the same context length of 262144 tokens, so neither gives you a longer working window than the other. Where they diverge is in what they accept and what knobs they expose. Mistral Medium 3.5 accepts text, image and file input, while Pareto 26.9 accepts text and image only, so file input is available on the Mistral side and not on the Pareto side. Mistral Medium 3.5 also supports structured outputs and reasoning parameters (including reasoning and reasoning_effort), and neither of those is listed for Pareto 26.9. Beyond that, Mistral Medium 3.5 exposes a wider set of sampling controls such as frequency_penalty, presence_penalty, seed and stop, whereas Pareto 26.9 lists a smaller set: max_tokens, response_format, temperature, tool_choice, tools and top_p. Both models support tool calling, so that is not a differentiator.

For day to day work, the choice is fairly mechanical. If your task involves reasoning-heavy prompts, structured output that must conform to a schema, or documents supplied as files, Mistral Medium 3.5 is the one that supports those features, and Pareto 26.9 does not list them. If your task is straightforward text or image input with a plain text answer, both models can serve it, and the decision then rests on price and on how much control you want over generation. Mistral Medium 3.5 is the cheaper option on input pricing, while output pricing is about the same between the two, so workloads with large prompts and modest completions tend to favour the Mistral model on cost. Workloads dominated by output tokens see little price difference.

Calling either model through api14 works the same way, because the API is OpenAI-compatible. You point your client at the api14 base URL, send your request in the usual chat completions shape, and set the model field to the id you want. To use Mistral Medium 3.5, set the model id to "mistralai/mistral-medium-3-5". To use Pareto 26.9, set it to "unbiased/pareto-26.9". Everything else in the request body, the messages, the temperature, the tools array and the rest, stays as it is, subject to the parameters each model supports. That means you can switch between the two by changing a single string, which makes it easy to route different requests to different models or to compare their outputs on the same prompt.

There is still a case for Pareto 26.9. If your prompts are text or image only, you do not need structured outputs or reasoning controls, and your token usage is weighted toward output rather than input, then the two models are close enough on price that Pareto 26.9 is a reasonable pick, and it is the newer of the two by provider release date. In that situation the simpler parameter surface is not a limitation, and you can keep the request shape minimal.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "mistralai/mistral-medium-3-5" to "unbiased/pareto-26.9" and prompts, streaming and tools keep working.

Mistral: Mistral Medium 3.5 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);

Mistral: Mistral Medium 3.5 vs Pareto 26.9

Specifications

Key differences

In depth

Switch with one string

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