Exceptionally low cost at $0.02/$0.04 per 1M tokens — among the cheapest available via API
Strong multilingual performance for a model its size, covering European languages well
128K context window is generous for a budget-tier model
Solid instruction-following for routine tasks like classification, extraction, and summarization
Weaknesses
Noticeably weaker than frontier models (GPT-4o, Claude Sonnet 4.6) on complex multi-step reasoning
Not competitive with larger open-weight models like Llama 3.1 70B on coding benchmarks
No native multimodal or image capabilities
Monthly cost estimate
See what Mistral: Mistral Nemo actually costs at your usage level
Input tokens / month1M
10k50M
Output tokens / month500k
10k25M
Input cost
$0.020
Output cost
$0.020
Total / month
$0.040
Based on Mistral: Mistral Nemo API pricing: $0.02/1M input · $0.04/1M output. Real costs vary by provider discounts and caching. Check the provider for exact current rates.
Price History
Mistral: Mistral Nemo pricing over time
→0% since Mar 27
2 data points · tracked daily since Mar 27, 2026
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Teams needing a cheap, fast, multilingual workhorse for classification, summarization, or light coding tasks at scale.. Start free — no card required.
Recommendations are made independently based on real-world use and public benchmarks. See our disclosures for details.
Compare alternatives
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Mistral: 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
Best for high-volume, cost-sensitive workflows like document triage, classification, summarization, and lightweight coding assistance where budget is the primary constraint.
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.
Ministral 3B is Mistral's ultra-compact 3-billion parameter edge model designed for lightweight inference, on-device deployment, and cost-sensitive applications. It delivers surprisingly capable text understanding and generation at a fraction of the cost of larger models.
Verdict
The cheapest viable option for simple NLP tasks, but don't expect small-flagship performance.
Quality score
41%
Pricing
$0.10/1M in
$0.10/1M out
Speed
Very fast
Best for high-volume, low-latency tasks where cost and speed matter more than frontier-level reasoning.
Context
131k tokens
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.
3BEdgeUltra-budgetMistralLightweight
Best for
High-volume, low-latency tasks where cost and speed matter more than frontier-level reasoning.
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
Best for high-volume, latency-sensitive applications where cost per token matters more than top-tier quality.
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.
Pricing moves, ranking shifts, and capability updates.
New ModelMar 27, 2026
Mistral: Mistral Nemo — added to UseRightAI
Mistral: Mistral Nemo (Mistral) is now indexed. A dirt-cheap multilingual model perfect for bulk text tasks, but don't expect frontier-level reasoning.
Mistral: Mistral Nemo is best for teams needing a cheap, fast, multilingual workhorse for classification, summarization, or light coding tasks at scale.. It is a strong fit when that workflow matters more than the tradeoffs around budget pricing and fast speed.
When should I avoid Mistral: Mistral Nemo?
You need reliable multi-step reasoning, advanced code generation, or any image/multimodal processing.
What is a cheaper alternative to Mistral: Mistral Nemo?
Meta: Llama 3.1 8B Instruct is the lower-cost option to compare first when you want a similar workflow fit with less token spend.
What is a faster alternative to Mistral: Mistral Nemo?
Mistral: Ministral 3 14B 2512 is the better pick when response time matters more than maximum depth or premium quality.
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