One of the cheapest models in the directory at $0.10/1M input
Multimodal — handles images alongside text at this price point
Fast and efficient for simple, well-defined tasks
Weaknesses
Weak on complex reasoning, hard coding, and nuanced writing
Not suitable for tasks requiring deep context retention or multi-step logic
Limited to simpler use cases compared to Codestral or DeepSeek V3
Real-world use cases
What people actually use Mistral Small 3.1 for.
Bulk document classification and tagging pipelines at near-zero cost
Image description and OCR-adjacent tasks where full multimodal models are overkill
High-frequency lightweight summarisation in cost-sensitive products
How Mistral Small 3.1 compares
The nearest models people weigh against it, and what actually separates them.
vs Mistral Medium 3.1 — Against Mistral Medium 3.1 (Mistral), Mistral Small 3.1 runs about 83% cheaper per token, gives up 1x on context and answers faster. Take Mistral Small 3.1 unless you specifically need what Mistral Medium 3.1 does better.
vs Voxtral Small 24B 2507 — Against Voxtral Small 24B 2507 (Mistral), Mistral Small 3.1 lands within a few percent on price, takes 4x the context and answers faster. Which one wins depends on whether context depth or latency is your constraint.
vs GPT Audio Mini — Against GPT Audio Mini (OpenAI), Mistral Small 3.1 runs about 87% cheaper per token and answers faster. Take Mistral Small 3.1 unless you specifically need what GPT Audio Mini does better.
Price History
Mistral Small 3.1 pricing over time
→0% since Jun 12
90 data points · tracked daily since Jun 12, 2026
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Start using Mistral Small 3.1
Ultra-high-volume classification, summarisation, and lightweight vision tasks. Start free — no card required.
Mistral Medium 3.1 is a multimodal mid-tier model from Mistral that supersedes Mistral Large 2, offering vision capabilities alongside strong text performance at a significantly reduced price point. It targets the sweet spot between budget models and expensive flagships, with a 128K context window and competitive multilingual support.
Verdict
The best Mistral model for budget-conscious builders who still need multimodal capability and solid multilingual output.
Quality score
70%
Pricing
$0.40/1M in
$2.00/1M out
Speed
Fast
4/5 speed
Context
131k tokens
Officially supersedes Mistral Large 2, representing a generational shift in Mistral's lineup toward multimodal capability at lower cost tiers. Available via Mistral API and select cloud providers. No function calling limitations noted at this tier.
BudgetMultimodalMultilingualMid-tierVision
Best for
Cost-sensitive teams needing solid coding, instruction-following, and basic vision tasks without paying flagship prices.
Voxtral Small 24B is Mistral's audio-capable language model, designed for speech transcription, voice understanding, and spoken language tasks at a budget-friendly price point. It supersedes Mistral Small 3.1 with native audio input support built on a 24B parameter base.
Verdict
A purpose-built budget audio model that excels at voice tasks but stumbles on context length and general-purpose depth.
Quality score
47%
Pricing
$0.10/1M in
$0.30/1M out
Speed
Fast
4/5 speed
Context
32k tokens
Voxtral Small is audio-in capable but does not support image input. The 32K context window is notably short for a 2025 model. Pricing is via Mistral's API; availability through third-party providers may vary. Check whether your use case requires audio input — the text-only version of Mistral Small 3.1 may be more appropriate for pure text workloads.
Audio AIBudgetMultilingualSpeechMistral
Best for
Transcribing, analyzing, and responding to audio input cost-effectively without needing a separate speech-to-text pipeline.
GPT Audio Mini is OpenAI's cost-efficient audio-capable model that handles real-time speech input and output alongside text, built on the GPT-4o Mini architecture. It's designed for voice-driven applications where low latency and affordable pricing matter more than peak intelligence.
Verdict
The most practical choice for cost-conscious voice application developers who need native audio I/O without compromising too much on intelligence.
Quality score
44%
Pricing
$0.60/1M in
$2.40/1M out
Speed
Fast
4/5 speed
Context
128k tokens
Audio tokens are priced differently from text tokens in OpenAI's API — audio input/output carries a significant premium over text tokens, so real-world costs for voice-heavy workloads will be substantially higher than the listed text token price suggests. Check OpenAI's audio token pricing separately.
AudioVoice AIReal-timeBudgetMultimodal
Best for
Building voice assistants, audio bots, and speech-enabled applications that need real-time audio processing at scale without breaking the budget.
Mistral Small 3.1 costs $0.1 per million input tokens and $0.3 per million output tokens on the API. A month of 10M input and 2M output tokens runs about $1.60 at list price, before any batch or caching discounts.
What is Mistral Small 3.1 best for?
Mistral Small 3.1 is best for ultra-high-volume classification, summarisation, and lightweight vision tasks. 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 Small 3.1?
You need reliable multi-step reasoning or coding quality — it won't hold up.
What is a cheaper alternative to Mistral Small 3.1?
Voxtral Small 24B 2507 (Mistral) at $0.10/1M/1M input against Mistral Small 3.1's $0.10/1M/1M. A purpose-built budget audio model that excels at voice tasks but stumbles on context length and general-purpose depth. Compare it first if Mistral Small 3.1's pricing is the thing stopping you.
What is a faster alternative to Mistral Small 3.1?
Mistral Medium 3.1 — fast against Mistral Small 3.1's very fast, with 131k 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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