Mistral: Mistral Small 3.1 24B
Mistral Small 3.1 24B Instruct is an upgraded variant of Mistral Small 3 (2501), featuring 24 billion parameters with advanced multimodal capabilities. It provides state-of-the-art performance in text-based reasoning and...
Anyone in the Space can @-mention Mistral: Mistral Small 3.1 24B 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
- Cost-sensitive multimodal workflows
- Document extraction with embedded images
- High-volume content moderation tasks
- Prototyping before scaling to larger models
- Batch processing of mixed text-image inputs
Strengths
The 128K context window handles long documents without chunking, while vision support covers common use cases like screenshot analysis and invoice parsing. Pricing sits 40-50% below GPT-4o mini, making it viable for high-throughput pipelines where per-token cost compounds quickly. Mistral's architecture typically delivers faster inference than comparably-priced alternatives, reducing latency in user-facing applications. The model handles structured output formats reliably enough for JSON extraction and form-filling tasks.
Trade-offs
Without public benchmarks, performance on reasoning-heavy tasks remains unproven relative to peers. Early Mistral Small versions lagged behind Gemini Flash and GPT-4o mini on multi-step logic and nuanced instruction adherence. Vision capabilities likely trail GPT-4o and Claude Sonnet for complex visual reasoning—expect solid OCR and basic scene understanding, but not deep spatial analysis. The 24B parameter count suggests less world knowledge than 70B+ models, which surfaces in specialized domains or when handling ambiguous queries.
Specifications
- Provider
- mistralai
- Category
- llm
- Context length
- 128,000 tokens
- Max output
- 128,000 tokens
- Modalities
- text, image
- License
- proprietary
- Released
- 2025-03-17
Pricing
- Input
- $0.35/Mtok
- Output
- $0.56/Mtok
- Model ID
mistralai/mistral-small-3.1-24b-instruct
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 | 128k | $0.35/Mtok | $0.56/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. For each item, include description, quantity, unit_price, and total. Return only valid JSON with no additional commentary.Open in a Space →
Summarize Long Support Tickets
Read this entire support ticket thread and write a 3-sentence summary covering: the customer's core issue, steps already attempted, and current status. Focus on actionable facts.Open in a Space →
Moderate User-Generated Content
Review this user comment for policy violations: hate speech, spam, personal attacks, or off-topic content. Respond with a single word—APPROVE or REJECT—followed by a one-sentence reason if rejected.Open in a Space →
Generate Product Descriptions
Write a 50-word product description for this item. Include key features, target use case, and one benefit statement. Use an informative tone suitable for an online store.Open in a Space →
Analyze Screenshot UI Elements
Examine this app screenshot and list all interactive UI elements you see—buttons, input fields, dropdowns, links. For each, note its label and approximate position (top-left, center, etc.).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. It's supposed to merge two sorted lists but users report incorrect output when one list is empty.
The function has a boundary condition bug. When either input list is empty, the while loop never executes, but the remaining elements from the non-empty list aren't appended to the result. Add `result.extend(list1[i:])` and `result.extend(list2[j:])` after the main loop to handle leftover elements. Also consider adding input validation to handle None values, which would currently raise AttributeError.
Mistral Small 3.1 catches the core logic error and suggests the fix with correct Python syntax. At 24B parameters and $0.35/Mtok input pricing, it's positioned for high-volume debugging tasks where you need reliable analysis without the cost of frontier models. The 128K context window handles full file reviews comfortably.
Explain how OAuth 2.0 authorization code flow works to a junior developer who understands HTTP but hasn't worked with auth protocols before.
Think of it like a valet key system. When a user wants to give your app access to their data on another service, your app redirects them to that service's login page (authorization server). The user logs in and approves your app. The service gives your app a temporary code—not the actual access yet. Your app exchanges this code, plus a secret key only your server knows, for an access token. Now your app can make API requests on the user's behalf. The code step prevents malicious sites from stealing tokens, since they'd need your secret key to complete the exchange.
The explanation uses an effective analogy and walks through the flow sequentially without overwhelming detail. Mistral Small 3.1 handles technical education well at this scale. The response could go deeper on token refresh mechanics, but for a junior audience asking a broad question, the level of abstraction is appropriate.
Analyze this customer support transcript and extract: sentiment, main issue, whether it was resolved, and any product feedback mentioned.
**Sentiment:** Frustrated initially, neutral by end **Main issue:** Customer couldn't find the export button in the new dashboard redesign **Resolution status:** Resolved — agent provided screenshot showing the relocated feature under Settings > Data **Product feedback:** Customer stated the old placement (top toolbar) was more intuitive and suggested adding a tooltip or onboarding highlight for the new location. They also mentioned the export process itself worked fine once found.
Structured extraction tasks like this benefit from Mistral Small 3.1's 128K context window—you can batch dozens of transcripts in one call at $0.35/Mtok input. The model reliably identifies discrete fields and maintains factual accuracy. For higher-stakes sentiment analysis requiring nuance detection, you'd want human review or a larger model.
Use-case deep-dives
When Mistral Small 3.1 handles support routing under $100/month
A 4-person SaaS startup gets 200 support emails daily in English, French, and Spanish. Mistral Small 3.1 reads each message, tags urgency, extracts account details, and routes to the right agent—all for roughly $0.18 per 1,000 emails processed (assuming 800 input tokens average). The 128K context window means you can include your entire help center as reference material in every call, so the model catches edge cases your team documented six months ago. If you're processing under 500 emails/day and need multilingual coverage without the $2/Mtok Claude tax, this is the call. Beyond 1,000 emails daily, batch through a cheaper model and reserve Mistral Small for the ambiguous 20%.
Why solo consultants use this for MSA review at $0.35/contract
A freelance designer reviews 8-12 master service agreements monthly, each running 15-30 pages. Mistral Small 3.1 ingests the full contract (roughly 20K tokens), compares against a stored template of acceptable terms, and flags liability caps, IP assignment clauses, and non-compete radius in under 10 seconds. Total cost per contract: about $0.07 input plus $0.11 for a 200-token summary—$0.18 all-in. The model's vision capability means you can throw in scanned PDFs without OCR preprocessing. If you're reviewing more than 50 contracts monthly, the per-document cost still beats a paralegal's 15-minute scan at $75/hour. For teams doing this at scale (100+ contracts/month), you want Claude 3.5 Sonnet's benchmark edge on legal reasoning, but for solo/small-team contract triage, Mistral Small hits the price-performance sweet spot.
When 128K context beats embedding search for 10-person teams
A 10-person agency has 400 Notion pages documenting client preferences, design systems, and process runbooks. Instead of building a vector database, they dump the entire wiki (roughly 80K tokens) into Mistral Small 3.1's context window and let team members ask natural-language questions. At $0.35/Mtok input, each query costs about $0.028—cheaper than maintaining Pinecone and faster than keyword search. The model returns answers with page references, and because the full wiki is in-context, it catches cross-references a RAG system would miss. This works until your wiki hits 100K tokens or your team grows past 15 people and query volume makes the per-call cost add up. At that threshold, switch to embeddings + a cheaper model for retrieval, but for small teams with dense internal docs, Mistral Small's context window is the simplest path to wiki-as-chatbot.
Frequently asked
Is Mistral Small 3.1 good for production chatbots?
Yes, if you need multimodal support at a low price point. At $0.35/$0.56 per Mtok, it's significantly cheaper than GPT-4o or Claude Sonnet while handling both text and images. The 128k context window covers most chat histories. However, without public benchmarks, you're trusting Mistral's internal evals — test it against your actual use case before committing to production.
Is Mistral Small 3.1 cheaper than GPT-4o Mini?
No. GPT-4o Mini costs $0.15/$0.60 per Mtok, making input 57% cheaper than Mistral Small 3.1's $0.35. Output is roughly equal. If you're optimising for cost and don't need Mistral's specific European data residency or model behaviour, GPT-4o Mini wins on price. Mistral Small 3.1 makes sense if you're already in the Mistral ecosystem or need their compliance guarantees.
Can Mistral Small 3.1 handle 128k tokens reliably?
The 128k context window is advertised, but without public needle-in-haystack or long-context benchmarks, real-world performance is unverified. Mistral's larger models have shown solid long-context behaviour in community testing, so this likely works for retrieval and summarisation tasks. For mission-critical long-context work, run your own evals or wait for independent benchmarks before trusting the full window.
How does Mistral Small 3.1 compare to Mistral Small 2?
Mistral hasn't published direct comparisons, and no public benchmarks exist for Small 3.1 yet. The version bump suggests improvements in reasoning or multimodal handling, but you can't quantify the delta without testing. If you're using Small 2 and it works, wait for benchmarks. If you're starting fresh, the newer model is the safer bet at the same price tier.
Should I use Mistral Small 3.1 for document analysis with images?
Probably not as your first choice. While it supports images and costs less than GPT-4o, the lack of public vision benchmarks means you don't know how it performs on OCR, chart extraction, or diagram understanding. For document analysis, GPT-4o or Claude Sonnet have proven track records. Use Mistral Small 3.1 if cost is critical and you can tolerate lower accuracy.