Back to models
VisionChat Models

Mistral AI

Ministral 3 14B

14B text-and-vision model for document and image workflows, with 256K context and open weights.

TextVision
Open AppBrowse All Models

At a glance

Know the model before you prompt.

Mistral AI API specifications
Context window
256K tokens (published)
Maximum output
Not verified
Inputs → output
Text + Images → Text
Knowledge cutoff
Not verified

API model ID: ministral-14b-2512

Capabilities & boundaries

What it supports. Where the limits are.

Tool support requires the appropriate API integration; a supported tool is not automatically active in every chat.

Supported API features and tools

  • The 14B member of the Ministral 3 family for text and vision workloads
  • Text and image understanding for supplied documents, screenshots and mixed-input questions
  • Function calling and structured outputs listed on the model card; configure and validate them in the API integration
  • Open weights under Apache 2.0; hosted API usage and self-hosted operation have separate costs and responsibilities

Before you choose

  • The model card publishes a rounded 256k context. A model-specific maximum output and knowledge cutoff were not established in the reviewed sources; neither is inferred from another model.
  • Image understanding does not mean native image, audio or video generation. Check image readability and verify visual claims against the original.
  • A tool call requests work from your integration; it does not itself run code, search the web or authorize an external action. Validate arguments and require approval for consequential changes.
  • Open weights do not guarantee that a local deployment reproduces the hosted API's tools, performance or context settings. Review the model license and serving requirements.

Reasoning behavior

The reviewed model card does not establish a configurable reasoning-effort list or default for this API ID. Do not copy Medium 3.5 controls or settings from separately named Ministral Reasoning weights into this model. Use a bounded task, clear evidence and an explicit answer format.

  • Use ministral-14b-2512 for the documented model reference. A -latest alias may change over time; record the resolved version and re-evaluate before changing aliases.
  • The model card links Chat Completions, document Q&A, function calling and structured outputs. Agent services and built-in tools are separate integration features, not automatic access granted by a prompt.
  • Keep repeatable instructions at the start of requests to make caching useful. prompt_cache_key can improve cache-hit likelihood but does not guarantee a hit; inspect usage.prompt_tokens_details.cached_tokens.

Put it to work

Start with a more useful prompt.

Original examples from EZ Ai Assist. Adapt these to your task and the features available in your workspace.

Workflow 01

Reconcile a table with a screenshot

Cross-check two supplied representations of the same data.

Compare this screenshot of a report with the supplied text table. Identify mismatched labels, values, units and missing rows. Cite both representations for each discrepancy and mark anything too small to read. Do not decide which source is authoritative unless the instructions say so; finish with a reconciliation checklist.

Workflow 02

Extract requirements with traceability

Turn a longer brief into testable requirements.

Extract requirements from this project brief into a table with ID, requirement, source section, acceptance check and unresolved dependency. Separate explicit requirements from suggested improvements. Preserve contradictions for review rather than silently resolving them, and identify the minimum questions needed before implementation.

Workflow 03

Choose a compact deployment candidate

Compare quality gains with observed serving costs.

Using these measured results for 3B, 8B and 14B candidates, compare error rate, response latency, memory use and cost for our workload. Show where a larger model improves outcomes and where the evidence is inconclusive. Recommend a candidate and fallback rule using only the data supplied, with no invented hardware benchmarks.

Developer reference

Mistral AI API pricing

These are Mistral API reference prices, not EZ Ai Assist subscription prices.

View EZ Ai Assist plans
Standard processing · USD per 1,000,000 tokens
Token typePrice
Input$0.20
Cached input$0.02
Output$0.20
  • The table uses the provider's standard processing rates. Batch, priority, regional deployments and other hosts may have different prices; recheck the chosen service before budgeting.
  • Cached input is billed at 10% of the ordinary input rate for eligible cache hits. Uncached input and generated output retain their own rates; caching does not make an entire request free.
  • The cache documentation describes shared-prefix matching in 64-token blocks; prompts shorter than 64 tokens do not qualify. Measure cached usage rather than assuming repeated requests are discounted.

Common questions

A few things worth knowing.

How should I compare 14B with 8B and 3B?

Keep prompts and the evaluation set fixed. Compare correctness, unsupported claims, latency and actual serving cost. More parameters do not guarantee a better result for every narrow workflow.

Which API ID does this guide cover?

ministral-14b-2512, the December 2025 Ministral 3 14B model card. It is not Mistral Large 3 and does not inherit that model's architecture or pricing.

Can I self-host the weights?

The card publishes open weights under Apache 2.0. Check the weight format, runtime requirements and license for your deployment; hosted service features are not automatically supplied by the weights.

Does 256K context also mean 256K output?

No. Context capacity and maximum generated output are different limits. The source publishes 256k context but the reviewed model card does not establish an output ceiling; this guide does not invent one.

Can it analyze images or generate new ones?

The model card supports text and vision tasks with text output. Use it to discuss visible content, and check the result against the image. That is not a claim of native image, audio or video generation.

Will repeated prompts always receive the cached rate?

No. A shared prefix and cache routing can help, but inspect reported cached tokens to confirm a hit. Only eligible cached input uses the lower rate; output is billed separately.

Are these features and rates included in my EZ Ai Assist plan?

This is a provider API reference, not the app's subscription terms or a promise that every API feature is exposed. Check the app for access and the EZ Ai Assist pricing page for your plan.

Check the source

Official documentation

Specifications and API prices checked on . Example prompts and workflow advice are editorial guidance from EZ Ai Assist.

Same provider

More from Mistral AI

Mistral AI

Mistral Medium 3.5

Text-and-vision model for coding and agent workflows, with 256K context and configurable reasoning.

Mistral AI

Mistral Small 4

Hybrid text-and-vision model combining instruction following, reasoning and coding with 256K context.

Mistral AI

Ministral 3 8B

Compact 8B text-and-vision model for extraction and constrained workflows, with 256K context.

Mistral AI

Ministral 3 3B

Small 3B text-and-vision model for narrow classification and extraction tasks, with 256K context.