Mistral AI
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Ministral 3 14B API

The Ministral 3 14B API: the largest Ministral 3 model, with vision और tool calling, 70% below Mistral's की सूचीबद्ध कीमत

प्रति 1M टोकन की कीमत70%छूट
इनपुट
$0.06$0.20
आउटपुट
$0.06$0.20
अपनी API कुंजी पाएँप्लेग्राउंड में आज़माएँ
मॉडल 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="")

बेस URL https://api.zurelay.com/v1. किसी भी OpenAI SDK के साथ काम करता है। चैट API के दस्तावेज़

कॉन्टेक्स्ट विंडो
128K टोकन
रीजनिंग
कोई नहीं
इनपुट
Text, Image
रिलीज़ की तारीख
2 दिस॰ 2025
स्थिति
चालू

कीमतें

Ministral 3 14B API की कीमतें। तय कीमत का एक छोटा-सा हिस्सा।

बिना किसी सदस्यता के, प्रीपेड क्रेडिट से जितना इस्तेमाल करें, उतना भुगतान करें। $10 से टॉप-अप करें। असफल अनुरोधों का कभी शुल्क नहीं लगता।

दरZurelayMistral AI की सूचीबद्ध दर
इनपुट
प्रति 1M टोकन
$0.06$0.20
आउटपुट
प्रति 1M टोकन
$0.06$0.20

हर लंबाई के प्रॉम्प्ट के लिए एक ही दर, up to the full 128K context window। स्ट्रीमिंग, टूल कॉल और स्ट्रक्चर्ड आउटपुट का कोई अतिरिक्त शुल्क नहीं है।

बचत कैलकुलेटर

स्लाइडर से अपना मासिक इस्तेमाल सेट करें.

प्रति माह इनपुट टोकन50M
प्रति माह आउटपुट टोकन10M
Zurelay
$3.60
Mistral AI की सूचीबद्ध कीमत
$12.00
हर महीने आपकी बचत$8.40

$100.80 प्रति वर्ष

खास जानकारी

Ministral 3 14B क्या है?

70% below Mistral's list price. पर 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 और 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,

Mistral AI released the Ministral 3 family on December 2, 2025, alongside Mistral Large 3, in three sizes: 3B, 8B और 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 की कीमत प्रति 1M आउटपुट टोकन $0.06 per 1M input tokens and $0.06 है।

Mistral built Ministral 3 for edge और local use, और 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 और 0.904 on MATH, ahead of Qwen3 14B without thinking (0.427, 65.1 और 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 और while streaming, और 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.

खूबियाँ

Ministral 3 14B कहाँ बेहतरीन है. और टीमें इससे क्या बनाती हैं.

01

Strong for its size

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

02

तस्वीरें पढ़ता है

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

03

टूल और JSON

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

04

ओपन वेट्स

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

इस्तेमाल के तरीके

  • Assistants और chat

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

  • Agent steps

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

  • Extraction from text and इमेज

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

  • Multilingual work

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

शुरू करें

तीन चरणों में Ministral 3 14B को कॉल करें.

न कोई वेटलिस्ट, न नया SDK. अगर आपका कोड पहले से OpenAI से कनेक्ट होता है, तो वह Zurelay से भी कनेक्ट हो सकता है.

  1. 1

    कुंजी बनाएँ

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

    अपनी API कुंजी पाएँ
  2. 2

    Zurelay पर Point your SDK

    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 और usage reporting work as you expect.

    ministral-3-14b

आपके मौजूदा टूल के साथ काम करता है

तुलना करें

मिलते-जुलते मॉडल. सभी के लिए एक ही कुंजी.

70%छूट

Ministral 3 3B

प्रति 1M टोकन $0.03 / $0.03

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

Ministral 3 3B API
53%छूट

Qwen 3.8 Flash

प्रति 1M टोकन $0.07 / $0.22

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

Qwen 3.8 Flash API
61%छूट

Gemini 3.1 Flash-Lite

प्रति 1M टोकन $0.10 / $0.59

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

Gemini 3.1 Flash-Lite API

अक्सर पूछे जाने वाले सवाल

Ministral 3 14B API से जुड़े सवाल.

कुछ और पूछना है? हमारी टीम से पूछें और हम ईमेल से जवाब देंगे.

Ministral 3 14B API की कीमत कितनी है?

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

मैं OpenAI SDK से the Ministral 3 14B API को कैसे कॉल करूँ?

Install the official openai package, set base_url to https://api.zurelay.com/v1 और 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 और ministral-14b. Mistral recommends a temperature of 0.1 or lower for production work.

यह Ministral 3 14B context window क्या है?

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

Can Ministral 3 14B read इमेजs or PDFs?

It reads इमेजs: send them in image_url parts, as https links or base64 data URLs, और 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 और there are no reasoning tokens से 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 और 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, और tool_choice "none" works. Tool call ids must be at least 9 characters long, as the model's own और 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 और images, hold 131,072 tokens on Zurelay और 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 और changes. Mistral publishes base, instruct और 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, और 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, और 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.

Zurelay पर और मॉडल

सभी मॉडल

पूरी कीमत देना बंद करें.
आज से बचत शुरू करें.

सेकंडों में एक कुंजी बनाएं, अपने मौजूदा SDK को Zurelay से जोड़ें, और पहले टोकन से ही हर अनुरोध पर 90% तक कम खर्च करें.

अपनी API कुंजी पाएँइंजीनियर से बात करें