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HomeModelsMinistral 3 3B 2512
MistralBudget

Ministral 3 3B 2512

The cheapest viable option for simple NLP tasks, but don't expect small-flagship performance.

42
Coding
48
Writing
38
Research
0
Images
94
Value
60
Long Context
Use this when

High-volume, low-latency tasks where cost and speed matter more than frontier-level reasoning.

Skip this if

You need reliable multi-step reasoning, complex code generation, or high-quality long-form writing — even budget alternatives like GPT-4o Mini will outperform it significantly.

Pricing
$0.10/1M in
$0.10/1M out
→0%since May 2026
Context
131k tokens
Speed
Very fast

Priced at a flat $0.10/1M for both input and output, making cost estimation predictable. The '2512' suffix indicates a December 2025 release version. Best suited for batch processing, classification, or extraction pipelines where volume is high and task complexity is low.

How to access
API
$0.09999999999999999/1M input tokens
Subscription = chat interface. API = build with it. Compare all subscription plans
Switch to instead if...
Best overall
GPT-6 Astra
Cheaper option
GPT-5.1-Codex-Max
Faster option
Ministral 3 14B 2512

Strengths

Exceptionally low cost at $0.10/1M tokens for both input and output — among the cheapest available

128K context window is generous for a 3B model, enabling document summarization on a budget

Fast inference suitable for edge or real-time applications

Solid instruction-following for its size class, outperforming older small models like GPT-3.5-level tasks

Weaknesses

3B parameters means significantly weaker reasoning, math, and complex multi-step tasks compared to 7B+ models

Not competitive with GPT-4o Mini or Claude Haiku 3.5 on nuanced writing or coding tasks

No multimodal support — text only

Real-world use cases

What people actually use Ministral 3 3B 2512 for.

Classifying thousands of customer support tickets into categories at near-zero cost

Summarizing short documents or extracting key fields from structured text at scale

Powering a lightweight chatbot or FAQ assistant on an edge device or embedded system

How Ministral 3 3B 2512 compares

The nearest models people weigh against it, and what actually separates them.

vs Ministral 3 14B 2512 — Against Ministral 3 14B 2512 (Mistral), Ministral 3 3B 2512 runs about 50% cheaper per token and gives up 2x on context. Take Ministral 3 3B 2512 unless you specifically need what Ministral 3 14B 2512 does better.

vs Ministral 3 8B 2512 — Against Ministral 3 8B 2512 (Mistral), Ministral 3 3B 2512 runs about 33% cheaper per token and gives up 2x on context. Take Ministral 3 3B 2512 unless you specifically need what Ministral 3 8B 2512 does better.

vs Mistral Large 3 2512 — Against Mistral Large 3 2512 (Mistral), Ministral 3 3B 2512 runs about 90% cheaper per token, gives up 2x on context and answers faster. Take Ministral 3 3B 2512 unless you specifically need what Mistral Large 3 2512 does better.

Price History

Ministral 3 3B 2512 pricing over time

→0% since May 30

$0.108$0.104$0.100$0.096$0.092May 30Jun 17Jul 9Jul 26Aug 13Sep 7

90 data points · tracked daily since May 30, 2026

Ready to try it?

Start using Ministral 3 3B 2512

High-volume, low-latency tasks where cost and speed matter more than frontier-level reasoning.. Start free — no card required.

Try Ministral 3 3B 2512 freeCompare alternatives

Recommendations are made independently based on real-world use and public benchmarks. See our disclosures for details.

Compare alternatives

Similar models worth checking before you commit.

All Ministral 3 3B 2512 alternatives →
MistralBudget

Ministral 3 14B 2512

Ministral 3B is Mistral's compact edge-optimized model designed for high-throughput, low-latency tasks at an extremely competitive price point. Despite its small size, it supports a 262K context window, making it unusually capable for a sub-$0.20/1M token model.

Verdict
An ultra-cheap, fast model with a surprisingly large context window, but quality limitations make it a pipeline tool rather than a general assistant.
Quality score
48%
Pricing
$0.20/1M in
$0.20/1M out
Speed
Very fast
5/5 speed
Context
262k tokens
Model name suggests a December 2025 revision ('2512'). Pricing is symmetric at $0.20/1M for both input and output, which simplifies cost modeling. Confirm availability on your target API platform as Mistral model availability varies by provider.
budgetedgesmall modellong contexthigh throughput
Best for
High-volume, cost-sensitive workflows like document triage, classification, summarization, and lightweight coding assistance where budget is the primary constraint.
View model
MistralBudget

Ministral 3 8B 2512

Ministral 3B is Mistral's ultra-compact edge model designed for low-latency, cost-sensitive deployments. It punches above its weight for a sub-4B parameter model, handling instruction following, summarization, and lightweight reasoning at near-negligible cost.

Verdict
The go-to model for bulk processing tasks where cost and speed trump quality.
Quality score
50%
Pricing
$0.15/1M in
$0.15/1M out
Speed
Very fast
5/5 speed
Context
262k tokens
The '8B 2512' in the model name likely refers to a specific versioned release; despite the naming, this is based on Mistral's 3B architecture. Confirm parameter count and capabilities with Mistral's official documentation before production use.
budgetedgefastlong-contextcompact
Best for
High-volume, latency-sensitive applications where cost per token matters more than top-tier quality.
View model
MistralBudget

Mistral Large 3 2512

Mistral Large 3 2512 is Mistral's flagship dense model updated in December 2025, offering strong multilingual reasoning and coding capabilities at a significantly reduced price point compared to its predecessor. It targets enterprise workloads that need high-quality outputs without paying top-tier frontier model prices.

Verdict
The best price-per-quality ratio in the non-mini flagship tier, especially for multilingual and long-context enterprise tasks.
Quality score
69%
Pricing
$0.50/1M in
$1.50/1M out
Speed
Balanced
3/5 speed
Context
262k tokens
Pricing of $0.50 input / $1.50 output per 1M tokens places it firmly in the budget-flagship category. Available via Mistral API (La Plateforme) and major cloud providers. December 2025 update ('2512') improves instruction following over the earlier 2407 release.
Budget flagshipMultilingualLong contextEnterpriseCode
Best for
Multilingual enterprise tasks, code generation, and long-document analysis where cost efficiency matters more than absolute state-of-the-art performance.
View model

Change history

Pricing moves, ranking shifts, and capability updates.

New ModelMar 27, 2026

Mistral: Ministral 3 3B 2512 — added to UseRightAI

Mistral: Ministral 3 3B 2512 (Mistral) is now indexed. The cheapest viable option for simple NLP tasks, but don't expect small-flagship performance.

View model

FAQ

How much does Ministral 3 3B 2512 cost?

Ministral 3 3B 2512 costs $0.09999999999999999 per million input tokens and $0.09999999999999999 per million output tokens on the API. A month of 10M input and 2M output tokens runs about $1.20 at list price, before any batch or caching discounts.

What is Ministral 3 3B 2512 best for?

Ministral 3 3B 2512 is best for high-volume, low-latency tasks where cost and speed matter more than frontier-level reasoning.. It is a strong fit when that workflow matters more than the tradeoffs around budget pricing and very fast speed.

When should I avoid Ministral 3 3B 2512?

You need reliable multi-step reasoning, complex code generation, or high-quality long-form writing — even budget alternatives like GPT-4o Mini will outperform it significantly.

What is a cheaper alternative to Ministral 3 3B 2512?

GPT-5.1-Codex-Max (OpenAI) at $1.25/1M/1M input against Ministral 3 3B 2512's $0.10/1M/1M. The strongest choice for serious software engineering work, provided you can absorb the output-side pricing. Compare it first if Ministral 3 3B 2512's pricing is the thing stopping you.

What is a faster alternative to Ministral 3 3B 2512?

Ministral 3 14B 2512 — very fast against Ministral 3 3B 2512's very fast, with 262k tokens of context. Worth the swap when response time is what your users notice rather than the last few points of reasoning depth.

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