Mistral: Ministral 3 14B 2512
The largest model in the Ministral 3 family, Ministral 3 14B offers frontier capabilities and performance comparable to its larger Mistral Small 3.2 24B counterpart. A powerful and efficient language...
Anyone in the Space can @-mention Mistral: Ministral 3 14B 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
- Long-context document analysis with images
- Cost-sensitive multimodal applications
- Screenshot and diagram interpretation
- High-throughput vision tasks at scale
- Prototyping multimodal workflows cheaply
Strengths
The 262K context window handles entire codebases, long PDFs, or multi-page documents with embedded images in a single pass. At $0.20/Mtok for both input and output, it undercuts most vision-capable models by 50-70% while maintaining the speed advantage of a 14B parameter architecture. The flat pricing structure eliminates the usual input/output cost asymmetry, making it predictable for high-output use cases like document generation or detailed image descriptions.
Trade-offs
No public benchmarks means you're flying blind on accuracy relative to established models — expect to run your own evals before production deployment. The 14B size suggests it will struggle with nuanced visual reasoning tasks where GPT-4o or Claude Sonnet 4.5 excel, particularly complex charts, dense infographics, or multi-step visual problem-solving. Mistral's newer multimodal models haven't yet proven themselves against incumbents in real-world accuracy tests.
Specifications
- Provider
- mistralai
- Category
- llm
- Context length
- 262,144 tokens
- Max output
- —
- Modalities
- text, image
- License
- proprietary
- Released
- 2025-12-02
Pricing
- Input
- $0.20/Mtok
- Output
- $0.20/Mtok
- Model ID
mistralai/ministral-14b-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 | 262k | $0.20/Mtok | $0.20/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 Line Items
Extract all line items from this invoice image into a JSON array. Include item description, quantity, unit price, and total for each line. Preserve exact amounts and formatting.Open in a Space →
Analyze Architecture Diagram
Describe the architecture shown in this diagram. Identify each component, explain the data flow between them, and note any potential bottlenecks or single points of failure.Open in a Space →
Summarize Multi-Page Report
Read this 50-page quarterly report and provide a 300-word executive summary. Focus on revenue trends, key risks mentioned, and strategic initiatives. Reference specific page numbers for critical claims.Open in a Space →
Compare Product Screenshots
Compare these two product screenshots and list every visual difference you can identify. Include changes to layout, text, colors, icons, and button placement. Be exhaustive.Open in a Space →
Generate Alt Text at Scale
Write detailed alt text for this image suitable for screen readers. Describe the scene, key objects, text visible in the image, and any important context. Keep it under 125 characters.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 and suggest improvements. Focus on edge cases and performance. ```python def merge_sorted_lists(list1, list2): result = [] i = j = 0 while i < len(list1) and j < len(list2): if list1[i] < list2[j]: result.append(list1[i]) i += 1 else: result.append(list2[j]) j += 1 return result ```
The function has a critical bug: it drops remaining elements when one list is exhausted. After the while loop exits, you need to append the remaining elements from whichever list still has items. Add these lines after the loop: ```python result.extend(list1[i:]) result.extend(list2[j:]) ``` Performance-wise, this is already O(n+m) which is optimal for merging. However, if you're merging many lists repeatedly, consider using `heapq.merge()` from the standard library instead—it's implemented in C and handles multiple iterables efficiently.
Ministral 3 14B demonstrates strong code review capabilities with its 262K token context window, allowing it to handle large codebases. The response correctly identifies the bug and provides actionable fixes. However, at $0.20 per Mtok for both input and output, this model sits at a higher price point than some alternatives—teams should evaluate whether the extended context justifies the cost for their specific use cases.
Analyze this customer support ticket and draft a response. Ticket: "I've been charged twice for my January subscription. Transaction IDs: TXN-8847392 and TXN-8847401. Both posted on Jan 3rd. I need a refund immediately or I'm canceling." Context: Customer has been with us 14 months, no prior billing issues, premium tier ($49/mo).
**Draft Response:** Hi [Customer Name], I've reviewed your account and confirmed the duplicate charge—both TXN-8847392 and TXN-8847401 posted on January 3rd for $49 each. This appears to be a payment gateway retry error. I've initiated a refund for TXN-8847401, which should appear in your account within 5-7 business days. I've also added a $10 credit to your account as an apology for the inconvenience. Your subscription remains active with no interruption. If you don't see the refund by January 15th, please reply to this email and I'll escalate immediately. Thank you for your patience and for being with us for over a year.
This example shows Ministral 3 14B's ability to process multimodal context (the model accepts both text and image inputs, useful for analyzing screenshots of receipts or error messages). The response balances empathy with concrete action steps. The 262K context window means you could include extensive customer history, past tickets, and policy documents in a single prompt without truncation—though the uniform $0.20 pricing means long contexts cost the same per token as outputs.
I'm preparing a presentation on supply chain resilience. Generate three compelling data visualization ideas that would work well on slides, explaining what data to show and why each would be impactful for a C-suite audience.
**1. Geographic Risk Heat Map** Overlay your supplier locations on a world map with color-coded risk scores (combining geopolitical stability, climate vulnerability, and logistics reliability). Add your top 20 suppliers as labeled pins. Impact: Executives immediately see concentration risk—if 60% of suppliers cluster in one high-risk region, that's a board-level conversation. **2. Time-to-Recovery Waterfall Chart** Show cascading recovery times across your supply tiers. Start with Tier 1 (direct suppliers), then show how delays compound through Tier 2 and 3. Use your 2023 chip shortage as a case study. Impact: Makes abstract "multi-tier risk" concrete with real numbers. **3. Dual-Source Coverage Dashboard** Simple percentage bars showing what portion of critical components have qualified alternate suppliers, broken down by category. Include cost delta of maintaining dual sources. Impact: Quantifies the resilience-vs-cost trade-off executives need to approve budget for.
Ministral 3 14B handles strategic business communication effectively, providing specific, actionable recommendations rather than generic advice. The model's image input capability means you could feed it existing slide decks or competitor presentations for analysis. One limitation: without public benchmarks, teams can't compare its performance objectively against models like GPT-4 or Claude—you'll need to run your own evaluations on representative tasks before committing to this pricing tier.
Use-case deep-dives
When 262K context makes quarterly report synthesis trivial
A 4-person fintech startup needs to compare clauses across 40+ investor agreements before each board meeting. Ministral 3 14B's 262K context window swallows all documents in one prompt—no chunking, no retrieval pipeline, no hallucinated cross-references. At $0.20/Mtok symmetrical pricing, a 200K-token analysis run costs $0.04 input plus output, making it cheaper than engineer time spent wrangling a RAG stack. The 14B parameter count keeps inference fast enough for same-day turnaround. If your document sets exceed 250K tokens regularly, you'll need to batch or upgrade to a larger-context model, but most quarterly review cycles fit comfortably. This is the model when your bottleneck is stitching context, not raw intelligence.
Why image+text moderation works at $0.20 symmetrical
A 12-person e-commerce platform reviews 300 user-submitted product photos daily, each with a caption and seller notes. Ministral 3 14B processes image+text in one call, flagging policy violations and suggesting edits without a separate vision API. The symmetrical $0.20 pricing means a 10K-token output (detailed moderation report) costs the same as input, simplifying budget forecasts. At 300 reviews/day with ~5K tokens average per item, you're spending ~$3/day total. The model handles straightforward policy checks well, but if you need nuanced brand-safety calls or need to defend decisions to advertisers, step up to a frontier model with published safety benchmarks. For high-volume, clear-cut moderation where speed and cost matter more than edge-case precision, this is the call.
When 14B parameters hit the sweet spot for live support
A 20-seat SaaS support team takes 80+ chats per day, each running 15-30 minutes. Ministral 3 14B generates handoff summaries in under 2 seconds, fast enough to fire during the chat without the customer noticing. The 14B size keeps latency low and cost predictable: a 12K-token chat transcript costs $0.0024 to summarize, or ~$0.20/day for the full team. The 262K context means you can include the last 10 chats with the same customer for continuity without stitching. If your chats involve complex troubleshooting where a wrong summary breaks the next agent's flow, test against a larger model first—but for standard SaaS support where speed and volume matter, this model's efficiency wins. Deploy it and stop paying $0.50+ per summary elsewhere.
Frequently asked
Is Mistral Ministral 3 14B good for general text tasks?
Yes, it handles general text work well for a 14B model. The 262k context window lets you process long documents or maintain extended conversations. At $0.20/Mtok both ways, it's positioned as a mid-tier option. Without public benchmarks, you're trusting Mistral's internal testing, but their track record with previous Ministral releases suggests solid performance for summarization, Q&A, and content generation.
Is Ministral 3 14B cheaper than GPT-4o mini?
No. GPT-4o mini runs $0.15 input / $0.60 output per Mtok, so Ministral 3 costs 33% more on input but 67% less on output. If you generate short responses from long inputs, Ministral wins. If you generate long outputs from short prompts, GPT-4o mini is cheaper. For balanced workloads, they're roughly equivalent on cost.
Can Ministral 3 14B handle multimodal inputs effectively?
It accepts text and image inputs, but we lack public vision benchmarks to confirm quality. Mistral's multimodal models historically lag behind GPT-4V or Claude Sonnet for complex image reasoning. Use it for basic image-text tasks like receipt parsing or simple diagram analysis. For detailed visual reasoning or OCR-heavy work, test carefully before committing.
How does Ministral 3 14B compare to Ministral 2?
We can't give specifics without benchmarks for either version. The "2512" suffix suggests a December 2025 release, so it's newer. Mistral typically improves instruction-following and reduces refusals between generations. The 262k context is standard for recent Mistral models. If you're already using Ministral 2, test both on your actual workload before switching.
Should I use Ministral 3 14B for production chatbots?
Only if you need the 262k context for conversation history or reference documents. The 14B size means reasonable latency, and symmetric pricing simplifies cost prediction. However, the lack of public benchmarks makes quality harder to verify upfront. Run a pilot with real user queries first. For standard chat without huge context needs, Claude Haiku or GPT-4o mini offer better-documented performance.