Mistral Nemo is open-weight (Apache 2.0 license), so self-hosting is an option for teams that want to eliminate API costs entirely. Pricing via API is through Mistral's La Plateforme. The model uses a Tekken tokenizer which is more efficient than older Mistral tokenizers, especially for non-English text.
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
Real-world use cases
What people actually use Mistral Nemo for.
Classifying thousands of customer support tickets into categories at minimal cost
Summarizing multilingual news articles in French, Spanish, or Italian pipelines
Generating boilerplate code snippets or simple SQL queries in a high-volume CI tool
How Mistral Nemo compares
The nearest models people weigh against it, and what actually separates them.
vs Ministral 3 14B 2512 — Against Ministral 3 14B 2512 (Mistral), Mistral Nemo runs about 25% cheaper per token, gives up 2x on context and answers slower. Which one wins depends on whether context depth or latency is your constraint.
vs Ministral 3 3B 2512 — Against Ministral 3 3B 2512 (Mistral), Mistral Nemo costs about 33% more per token and answers slower. Ministral 3 3B 2512 is the one to check first if the price difference matters more than the ceiling.
vs Ministral 3 8B 2512 — Against Ministral 3 8B 2512 (Mistral), Mistral Nemo lands within a few percent on price, gives up 2x on context and answers slower. Which one wins depends on whether context depth or latency is your constraint.
Price History
Mistral Nemo pricing over time
→0% since Sep 3
7 data points · tracked daily since Sep 3, 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.
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.
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
5/5 speed
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
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.
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 Nemo costs $0.15 per million input tokens and $0.15 per million output tokens on the API. A month of 10M input and 2M output tokens runs about $1.80 at list price, before any batch or caching discounts.
What is the context window of Mistral Nemo?
Mistral Nemo has a 128k tokens context window, with up to 128k tokens of output per response. That is the total of prompt plus response the model can hold in one request.
What is the knowledge cutoff of Mistral Nemo?
Mistral Nemo's training data runs through April 2024, and the model was released on July 18, 2024. For anything after that date it needs web search or documents in the prompt.
What is Mistral Nemo best for?
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 Nemo?
You need reliable multi-step reasoning, advanced code generation, or any image/multimodal processing.
What is a cheaper alternative to Mistral Nemo?
Ministral 3 3B 2512 (Mistral) at $0.10/1M/1M input against Mistral Nemo's $0.15/1M/1M — roughly 33% less per token all in. The cheapest viable option for simple NLP tasks, but don't expect small-flagship performance. Compare it first if Mistral Nemo's pricing is the thing stopping you.
What is a faster alternative to Mistral Nemo?
Ministral 3 14B 2512 — very fast against Mistral Nemo's 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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