A historically significant but now outdated budget model crippled by an unusably small context window.
38
Coding
42
Writing
22
Research
0
Images
72
Value
4
Long Context
Use this when
Ultra-low-cost simple text tasks like classification, short summarization, or lightweight chatbot responses where context length is not a concern.
Strengths
Exceptionally low inference cost at $0.11/$0.19 per 1M tokens
Fast inference due to small 7B parameter footprint
Decent instruction-following for a model of its size and vintage
Open-weight architecture enables self-hosting and fine-tuning
Weaknesses
Critically limited 2,824-token context window — shorter than most documents or conversations
Monthly cost estimate
See what Mistral: Mistral 7B Instruct v0.1 actually costs at your usage level
Input tokens / month1M
10k50M
Output tokens / month500k
10k25M
Input cost
$0.110
Output cost
$0.095
Total / month
$0.205
Based on Mistral: Mistral 7B Instruct v0.1 API pricing: $0.11/1M input · $0.19/1M output. Real costs vary by provider discounts and caching. Check the provider for exact current rates.
Price History
Mistral: Mistral 7B Instruct v0.1 pricing over time
→0% since May 9
4 data points · tracked daily since May 9, 2026
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Ultra-low-cost simple text tasks like classification, short summarization, or lightweight chatbot responses where context length is not a concern.. 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
Change history
Pricing moves, ranking shifts, and capability updates.
New ModelMar 27, 2026
Mistral: Mistral 7B Instruct v0.1 — added to UseRightAI
Mistral: Mistral 7B Instruct v0.1 (Mistral) is now indexed. It supersedes Mistral Large 2. A historically significant but now outdated budget model crippled by an unusably small context window.
What is Mistral: Mistral 7B Instruct v0.1 best for?
Mistral: Mistral 7B Instruct v0.1 is best for ultra-low-cost simple text tasks like classification, short summarization, or lightweight chatbot responses where context length is not a concern.. It is a strong fit when that workflow matters more than the tradeoffs around budget pricing and very fast speed.
When should I avoid Mistral: Mistral 7B Instruct v0.1?
You need to process documents, maintain multi-turn conversations, or require any task that exceeds a few paragraphs of combined input and output.
What is a cheaper alternative to Mistral: Mistral 7B Instruct v0.1?
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 7B Instruct v0.1?
Mistral: Ministral 3 14B 2512 is the better pick when response time matters more than maximum depth or premium quality.
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Skip this if
You need to process documents, maintain multi-turn conversations, or require any task that exceeds a few paragraphs of combined input and output.
Significantly outperformed on reasoning and coding by newer models like Mistral 7B v0.3, Llama 3.1 8B, and Gemma 2 9B
v0.1 is an early iteration superseded by multiple improved versions from Mistral itself
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.
