Mistral: Ministral 3 3B 2512
The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.
Anyone in the Space can @-mention Mistral: Ministral 3 3B 2512 with the team's shared context - pooled credits, one chat, one memory.
Starter is free forever - 1 Space, 100 credits/month, 1 MCP. No card.
Verdict
Best for
- High-volume image classification tasks
- Cost-sensitive document OCR pipelines
- Edge deployment with vision requirements
- Batch processing of screenshots or receipts
- Lightweight chatbots with image understanding
Strengths
The 131K context window is unusually generous for a 3B model, letting you process long documents or multiple images in one call. Pricing at $0.10/Mtok makes it the cheapest multimodal option in Mistral's lineup, suitable for applications where you're running tens of thousands of inferences daily. The small parameter count means fast inference and low memory footprint, ideal for edge devices or serverless functions with tight resource limits.
Trade-offs
At 3B parameters, this model will struggle with nuanced reasoning, complex instruction-following, and tasks requiring deep domain knowledge. Expect higher error rates on ambiguous prompts compared to 8B or 22B alternatives. Vision capabilities are functional but not state-of-the-art—fine for extracting text from forms or identifying objects, but less reliable for detailed image analysis or subtle visual reasoning. No public benchmarks yet means you're flying blind on comparative performance.
Specifications
- Provider
- mistralai
- Category
- llm
- Context length
- 131,072 tokens
- Max output
- —
- Modalities
- text, image
- License
- proprietary
- Released
- 2025-12-02
Pricing
- Input
- $0.10/Mtok
- Output
- $0.10/Mtok
- Model ID
mistralai/ministral-3b-2512
Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.
Team cost calculator
5 seats · 80 msgs/day
Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.
Providers
| Provider | Context | Input | Output | P50 latency | Throughput | 30d uptime |
|---|---|---|---|---|---|---|
| mistralai | 131k | $0.10/Mtok | $0.10/Mtok | — | — | — |
Performance
Benchmarks
Works well with
Top MCPs
Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.
How Switchy teams use it
Starter prompts
Extract Invoice Data
Extract the following fields from this invoice image: vendor name, invoice number, date, total amount, and line items. Return as JSON.Open in a Space →
Classify Product Photos
Classify this product image into one of these categories: Electronics, Clothing, Home Goods, Toys, or Other. Provide only the category name.Open in a Space →
Summarize Screenshot Content
Describe what's shown in this screenshot in 2-3 sentences. Focus on the main UI elements and any visible text or actions.Open in a Space →
Validate Form Submissions
Review this form image and confirm whether these required fields are filled: name, signature, date. List any missing or illegible fields.Open in a Space →
Generate Alt Text
Write a concise alt text description for this image, suitable for screen readers. Keep it under 125 characters and focus on key visual elements.Open in a Space →
Example outputs
Illustrative - representative of the model's voice and quality, not literal recordings.
Review this Python function for potential bugs. Focus on edge cases around empty inputs and type mismatches: ```python def calculate_average(numbers): total = sum(numbers) return total / len(numbers) ```
This function has several edge case vulnerabilities: 1. **Empty list handling**: If `numbers` is empty, `len(numbers)` returns 0, causing a ZeroDivisionError. 2. **Type validation**: No check that `numbers` contains numeric types. Passing strings or mixed types will raise TypeError in `sum()`. 3. **None handling**: If `numbers` is None, both `sum()` and `len()` will fail. Recommended fix: Add guard clauses at the start—check if the list is empty or None, and optionally validate element types before processing.
Demonstrates the model's code analysis capability within its 131K token context window, suitable for reviewing functions with extensive surrounding code. The response is methodical but lacks the nuanced refactoring suggestions you'd see from larger models—it identifies problems clearly but stops short of proposing architectural improvements.
I have a screenshot of a dashboard with several charts. The bar chart in the top-right shows declining user engagement. Suggest three hypotheses for why this might be happening and what data I should pull to investigate each one.
Based on the visual trend in your dashboard's bar chart, here are three investigation paths: **Hypothesis 1: Seasonal drop-off** — User engagement naturally declines during this period annually. Pull: year-over-year data for the same timeframe to identify cyclical patterns. **Hypothesis 2: Feature change impact** — A recent product update reduced engagement. Pull: deployment logs and A/B test results from the past 30 days, segmented by user cohort. **Hypothesis 3: Acquisition channel shift** — New users from lower-quality sources are diluting metrics. Pull: engagement rates broken down by acquisition channel and user registration date. Prioritize Hypothesis 2 if you've shipped recently; otherwise start with seasonal analysis.
Showcases the model's multimodal capability—it can process image inputs alongside text to provide contextual analysis. At $0.10 per million tokens for both input and output, this makes vision-enabled workflows economical. The reasoning is structured but doesn't match the depth of frontier models when connecting multiple data dimensions.
Explain how OAuth 2.0 authorization code flow works to a junior developer who understands HTTP but hasn't worked with authentication systems. Use a real-world analogy, then map it to the technical steps.
**Analogy**: Imagine you want to use a valet service (third-party app) to park your car (access your data) at a hotel (resource server). You don't give the valet your house keys—you give them a special valet key (access token) that only works for parking, issued by the hotel's front desk (authorization server). **Technical flow**: 1. User clicks "Login with Google" in your app 2. Your app redirects to Google's authorization server 3. User approves access; Google redirects back with an authorization code 4. Your app exchanges that code for an access token (backend-to-backend) 5. Your app uses the token to request user data from Google's API The code-for-token exchange happens server-side so the token never touches the browser, preventing theft.
Illustrates the model's ability to adapt technical explanations for different audiences—a key strength for documentation and onboarding tasks. The 131K context window means it can handle this alongside full API documentation or multiple related concepts in one session. The explanation is clear but less comprehensive than what specialized instruction-tuned models produce.
Use-case deep-dives
When Ministral 3B handles 500+ daily support tickets under budget
A 12-person SaaS company routing 500-800 support emails daily needs fast classification without burning through their AI budget. Ministral 3B at $0.10/Mtok both ways makes this scenario work: each ticket averages 400 tokens in, 150 tokens out, so you're spending roughly $0.000055 per classification. That's $27.50 for 500 tickets versus $137.50 with a $0.50/Mtok model doing identical work. The 131K context window means you can include your entire knowledge base as reference material in a single call, so the model routes tickets to the right team with full product context. If your tickets need deep reasoning or multi-step problem-solving, step up to a larger model. But for high-volume categorization where speed and cost matter more than nuance, this is the call.
Why this model tags product images and descriptions at e-commerce scale
A 20-person e-commerce team uploads 200-300 new product listings weekly, each with 3-5 images and a text description. They need consistent tagging for search and recommendations. Ministral 3B's multimodal input means one API call processes both the product photo and the description, generating tags, category assignments, and SEO-friendly alt text. At $0.10/Mtok, processing 250 listings with 800 tokens average per listing costs about $20 versus $100+ with premium multimodal models. The 131K window handles batch processing: send 50 products in one call with your tagging schema as context. If you need pixel-perfect image analysis or creative copywriting, pay more. But for structured tagging where accuracy matters more than artistry, this is the price-performance winner.
How Ministral 3B summarizes 40-page contracts without context splitting
A 4-person legal tech startup ingests client contracts averaging 35-50 pages (roughly 25K-35K tokens) and needs 500-word summaries highlighting key terms, dates, and obligations. Ministral 3B's 131K context window swallows the entire contract in one call, so you avoid the fragile chunking logic that breaks on mid-clause splits. At $0.10/Mtok, a 30K-token contract with a 600-token summary costs $0.0036 per document. Process 1,000 contracts monthly for $3.60 instead of $18 with a $0.50/Mtok alternative. The model won't catch every legal nuance a senior associate would, but it flags the 80% of standard clauses reliably. If you're summarizing M&A agreements or complex IP licensing, pay for a reasoning-focused model. For high-volume intake where speed and cost trump perfection, this is the move.
Frequently asked
Is Ministral 3 3B good for coding tasks?
Yes, for lightweight coding tasks. At 3B parameters, it handles code completion, simple debugging, and documentation reasonably well. Don't expect it to architect complex systems or refactor large codebases — use Codestral or GPT-4 for that. The 131K context window helps with reviewing full files, but the small size limits reasoning depth.
Is Ministral 3 3B cheaper than other small models?
At $0.10 per Mtok for both input and output, it's competitive with Gemini Flash and cheaper than GPT-4o mini ($0.15/$0.60). The symmetric pricing is unusual — most models charge 3-5x more for output. If you generate long responses, this pricing structure saves money compared to asymmetric alternatives.
Can Ministral 3 3B handle 131K token context in practice?
The 131K window is real, but a 3B model struggles with reasoning across that much context. You can feed it entire codebases or long documents, but expect degraded accuracy beyond 40-50K tokens. Use it for retrieval and summarization over the full window, not complex multi-document reasoning.
How does Ministral 3 3B compare to Mistral 7B?
It's faster and cheaper but less capable. The 3B size means 2-3x lower latency and half the cost, but you lose reasoning quality on anything beyond straightforward tasks. Choose Ministral 3 for high-throughput, low-stakes work like classification or simple chat. Use 7B when accuracy matters more than speed.
Should I use Ministral 3 3B for production chatbots?
Only for simple, high-volume scenarios. It works for FAQ bots, basic customer service, or internal tools where speed matters more than nuance. The low latency and cost make it viable at scale. For anything requiring personality, complex reasoning, or handling edge cases gracefully, use a larger model.