Mistral AI
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Ministral 3 14B API

The Ministral 3 14B API: the largest Ministral 3 model, with vision und tool calling, 70% below Mistral's-Listenpreis

Preis pro 1M Tokens70%Rabatt
Input
$0.06$0.20
Output
$0.06$0.20
Hol dir deinen API-KeyIm Playground ausprobieren
Model IDministral-3-14b
chat.py
from openai import OpenAI
client = OpenAI(
    base_url="https://api.zurelay.com/v1",
    api_key="YOUR_ZURELAY_KEY",
)
stream = client.chat.completions.create(
    model="ministral-3-14b",
    messages=[{"role": "user", "content": "Explain quicksort in two sentences."}],
    stream=True,
)
for chunk in stream:
    if chunk.choices:
        print(chunk.choices[0].delta.content or "", end="")

Base URL https://api.zurelay.com/v1. Funktioniert mit jedem OpenAI SDK. Chat-API-Doku

Kontextfenster
128K Tokens
Reasoning
Keines
Input
Text, Image
Veröffentlicht
2. Dez. 2025
Status
Betriebsbereit

Preise

Ministral 3 14B API-Preise. Ein Bruchteil des Listenpreises.

Du zahlst aus Prepaid-Guthaben, ohne Abo. Lade ab $10 auf. Fehlgeschlagene Anfragen werden nie berechnet.

PreisZurelayMistral AI-Listenpreis
Input
pro 1M tokens
$0.06$0.20
Output
pro 1M tokens
$0.06$0.20

Ein Preis unabhängig von der Prompt-Länge, up to the full 128K context window. Streaming, Tool-Aufrufe und strukturierte Ausgaben kosten nichts extra.

Sparrechner

Stell mit den Reglern deinen monatlichen Verbrauch ein.

Eingabe-Token pro Monat50M
Ausgabe-Token pro Monat10M
Zurelay
$3.60
Mistral AI-Listenpreis
$12.00
Du sparst jeden Monat$8.40

$100.80 pro Jahr

Übersicht

Was ist Ministral 3 14B?

Ministral 3 14B is the largest model in Mistral AI's Ministral 3 family, released on December 2, 2025: an open-weight model that reads text und images and, by Mistral's account, performs comparably to its larger Mistral Small 3.2 24B. The Zurelay Ministral 3 14B API serves its instruct version through one OpenAI-compatible endpoint with a 131,072-token context window, bei 70% below Mistral's list price.

Mistral AI released the Ministral 3 family on December 2, 2025, alongside Mistral Large 3, in three sizes: 3B, 8B und 14B. Each size comes as a base, an instruct and a reasoning model, all with image understanding and all under the Apache 2.0 license, with the weights on Hugging Face. Ministral 3 14B pairs a 13.5B-parameter language model with a 0.4B-parameter vision encoder, and Mistral says it fits in 24GB of GPU memory at FP8. On Zurelay the model ID is ministral-3-14b, and it kostet $0.06 per 1M input tokens and $0.06 pro 1M Output-Tokens.

Mistral built Ministral 3 for edge und local use, und points the 14B model at private chat and assistant deployments on limited hardware, local agents and fine-tuning. It handles dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean and Arabic. Mistral says its Ministral instruct models often write an order of magnitude fewer tokens than comparable models.

On Mistral's model card the instruct model scores 0.551 on Arena Hard, 68.5 on WildBench und 0.904 on MATH, ahead of Qwen3 14B without thinking (0.427, 65.1 und 0.870) and Gemma 3 12B (0.436, 63.2 and 0.854). On MM MTBench, a multimodal chat test, it scores 8.49 against 6.70 for Gemma 3 12B. These are Mistral's numbers, not independent ones.

The instruct model answers directly without reasoning first, so reasoning_effort has no effect. It calls tools, several at once und while streaming, und returns JSON for json_object or json_schema in response_format. Mistral lists a 256K context window on its own API; on Zurelay a request holds up to 131,072 tokens. In our tests it wrote about 170 tokens a second and knew little about events after 2024, so put the facts it needs in the prompt. You pay $0.06 per 1M input tokens and $0.06 per 1M output tokens against Mistral's list price of $0.20 and $0.20.

Stärken

Worin Ministral 3 14B glänzt. Und was Teams damit entwickeln.

01

Strong for its size

0.551 on Arena Hard und 68.5 on WildBench in Mistral's tests, ahead of Qwen3 14B und Gemma 3 12B, and comparable to Mistral Small 3.2 24B by Mistral's account.

02

Liest Bilder

8.49 auf MM MTBench in Mistral's tests. Send screenshots, charts und photos next to your text.

03

Tools und JSON

Calls several tools at once, also while streaming, und follows a JSON schema, for agent steps und extraction.

04

Offene Modellgewichte

Apache 2.0 weights on Hugging Face that fit in 24GB von GPU memory bei FP8, so what you build on Zurelay carries over to a self-hosted copy later.

Anwendungsfälle

  • Assistants und chat

    Customer-facing chat und internal assistants that need good everyday answers bei a very low price per token.

  • Agent steps

    Tool-calling steps such as looking things up, filling forms und choosing the next action, with JSON for the code that follows.

  • Extraction from text and Bilder

    Pull fields from emails, documents und screenshots into a JSON schema.

  • Multilingual work

    Translate, summarize und answer in the languages Mistral lists, from French und German to Chinese, Japanese and Arabic.

Jetzt starten

Nutze Ministral 3 14B in drei Schritten.

Keine Warteliste und kein neues SDK. Wenn dein Code bereits mit OpenAI kommuniziert, funktioniert er auch mit Zurelay.

  1. 1

    Key erstellen

    Sign up, add credit und create an API key. Set a monthly budget or a rate limit per key if you like.

    Hol dir deinen API-Key
  2. 2

    Point your SDK bei Zurelay

    Change the base URL. Everything else in your code stays the same.

    https://api.zurelay.com/v1
  3. 3

    Use ministral-3-14b

    Send chat completions as usual. Streaming, tool calls und usage reporting work as you expect.

    ministral-3-14b

Funktioniert mit den Tools, die du schon nutzt

Vergleich

Ähnliche Modelle. Alle mit demselben Schlüssel.

70%Rabatt

Ministral 3 3B

$0.03 / $0.03 pro 1M tokens

Pick Ministral 3 3B for the simplest, highest-volume steps bei half the price per token, about twice as fast in our tests.

Ministral 3 3B API
53%Rabatt

Qwen 3.8 Flash

$0.07 / $0.22 pro 1M tokens

Pick Qwen 3.8 Flash for a 1M-token context, video input und a thinking mode when a task needs more than a small model.

Qwen 3.8 Flash API
61%Rabatt

Gemini 3.1 Flash-Lite

$0.10 / $0.59 pro 1M tokens

Pick Gemini 3.1 Flash-Lite for Google's low-cost model with a 1M-token context und audio, video and PDF input.

Gemini 3.1 Flash-Lite API

FAQ

Fragen zur Ministral 3 14B API.

Noch Fragen? Frag unser Team – wir antworten dir per E-Mail.

Was kostet die Ministral 3 14B API?

On Zurelay, ministral-3-14b costs $0.06 per 1M input tokens und $0.06 per 1M output tokens. Mistral's list price is $0.20 input und $0.20 output per 1M, so you save 70%. One rate applies at every prompt length, and failed requests are never billed.

Wie rufe ich the Ministral 3 14B API mit dem OpenAI SDK auf?

Install the official openai package, set base_url to https://api.zurelay.com/v1 und use your Zurelay API key. Then send a Chat Completions request with model set to "ministral-3-14b". Zurelay also accepts ministral-3-14b-instruct, mistralai/ministral-3-14b und ministral-14b. Mistral recommends a temperature of 0.1 or lower for production work.

Was ist the Ministral 3 14B context window?

On Zurelay a request holds up to 131,072 tokens. Mistral lists a 256K window on its own API. The model reads text und images und writes text.

Can Ministral 3 14B read Bilds or PDFs?

It reads Bilds: send them in image_url parts, as https links or base64 data URLs, und they count as input tokens. Mistral suggests images close to square, with very wide or thin ones cropped. Zurelay doesn't take PDFs, audio or video for Ministral 3 14B: a request that includes one is turned away with a clear error before anything is billed.

Does Ministral 3 14B reason before it answers?

No. Zurelay serves the instruct model, which answers directly, so reasoning_effort has no effect und there are no reasoning tokens bis pay for. Mistral also publishes a separate Ministral 3 14B Reasoning model, which Zurelay doesn't serve. For answers with thinking, use Qwen 3.8 Flash or DeepSeek V4.1 Flash.

Does Ministral 3 14B support tool calling und JSON output?

Yes. Pass tools as you would to any OpenAI model; it can call several at once, also while streaming. A tool forced by name is called, und tool_choice "none" works. Tool call ids must be at least 9 characters long, as the model's own und OpenAI's are. For JSON, send response_format with json_object or json_schema and the answer comes back as JSON.

Should I use Ministral 3 14B or Ministral 3 3B?

Both read text und images, hold 131,072 tokens on Zurelay und call tools. 14B is the stronger model, with a 13.5B-parameter language model against 3.4B, and scores higher in Mistral's tests: 0.551 against 0.305 on Arena Hard and 68.5 against 56.8 on WildBench. 3B costs half as much and was about twice as fast in our tests, so it suits the simplest, highest-volume steps.

Is Ministral 3 14B open source?

Its weights are open under the Apache 2.0 license, which allows commercial use und changes. Mistral publishes base, instruct und reasoning versions on Hugging Face and says the 14B fits in 24GB of GPU memory at FP8. The API is the quick way to use it without running your own hardware.

Is this the real Ministral 3 14B?

Yes. Requests for ministral-3-14b run on Mistral AI's Ministral 3 14B Instruct, und Zurelay doesn't swap in a different model. Only smart routing, which you turn on yourself, can answer with a close alternative when the model is down, und the x-zurelay-model header names the model that answered. Responses are sampled, so wording varies between runs. Zurelay is independent and is not affiliated with or endorsed by Mistral AI.

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