Chrome ExtensionMistral Prompt Generator
Updated for Mistral Large 4 (October 2026), Mistral's new ~1T-parameter multimodal model, and Mistral Medium 3.5, the everyday default for chat, reasoning and coding, plus Small 4 and Large 3. Generate concise, role-led prompts with a reasoning-effort setting, explicit output formats, function calling, structured JSON schemas and European multilingual coverage — ready for Le Chat, the Mistral API, AWS Bedrock, Azure, and Vertex AI.
Be specific about what you want the AI to do
Generated Prompt
Fill in the form and click "Generate" to create an optimized Mistral (Large 4 / Medium 3.5) prompt.
Tip: The more specific your task description and context, the better the generated prompt will perform.
Mistral (Large 4 / Medium 3.5) Tips
- • Mistral Medium 3.5 is one model for chat, reasoning and coding — set reasoning_effort to "high" for complex work and "none" for quick answers
- • Mistral Large 4 takes reasoning_effort "low", "medium" or "high" (default high) and rejects "none"; Mistral recommends temperature 1.0 and top_p 0.95 at every effort level
- • For agentic setups on Large 4, Mistral advises a system prompt that describes the environment and use case, and a small, well-defined tool set
- • Mistral recommends temperature 0.7 with reasoning_effort "high", and 0.0–0.7 with "none" (lower for extraction and code, higher for creative writing)
- • Mistral favors concise, efficient prompts — cut filler aggressively, every sentence should earn its place
- • Lead with a specific role: "You are a [specific expert]" — Mistral aligns tone and depth to the role
- • For function calling and structured output, name exact JSON keys and types — Mistral is strong here
- • For coding, name language, framework, version, dependencies, and coding standards precisely
- • Strong multilingual coverage — state the target language explicitly (English, French, Spanish, German, Italian, Portuguese, Dutch)
- • Pick Mistral Large 4 (October 2026) for the hardest multi-step, long-document or high-stakes work — it has a 1M-token context and native image input
- • Large 4, Medium 3.5, Small 4 and Large 3 all accept images — attach the screenshot, chart or document page and say exactly what to extract from it
Mistral Prompt Templates
Copy-ready starting points for the most common jobs people bring to this model. Swap the [BRACKETED] parts for your own details, or use them as a shape to imitate when you write your own.
Concise Technical Answer
EngineeringMistral rewards precise, unpadded instructions.
You are a [ROLE]. [THE SPECIFIC QUESTION] Context: [STACK, VERSIONS, CONSTRAINTS]. Answer in [FORMAT, e.g. numbered steps], maximum [NUMBER] items. Code in fenced blocks. No preamble.
Document Summarisation
AnalysisExplicit compression target and what must survive it.
Summarise the document below for [AUDIENCE]. Must survive the summary: [KEY THINGS THAT CANNOT BE DROPPED]. Target length: [LENGTH]. Tone: [TONE]. Do not add interpretation the document does not support. Document: [PASTE DOCUMENT]
Function Calling Setup
AgenticClear tool definitions and calling policy.
Task: [THE GOAL] Tools: - `[name](args)` — [purpose, when to use] - `[name](args)` — [purpose, when to use] Policy: call a tool rather than guessing; chain calls without asking me between steps; if arguments are missing, ask once. Return the final answer plus the call trace.
Multilingual Content
LanguageMistral's European-language strength with an explicit register.
Write [DELIVERABLE] in [LANGUAGE] about [TOPIC] for [AUDIENCE]. Register: [FORMAL / INFORMAL]. Regional variant: [e.g. European Portuguese, not Brazilian]. Requirements: idiomatic rather than translated-sounding; keep [TERMS] untranslated; [LENGTH].
Code Generation
EngineeringTight spec, no ambiguity.
Write [WHAT] in [LANGUAGE + VERSION]. Signature: [SIGNATURE] Behaviour: [WHAT IT MUST DO] Edge cases: [LIST] Constraints: [NO DEPENDENCIES / PERFORMANCE / STYLE] Return the code, then a two-line explanation of the approach. No other commentary.
Structured Extraction
DataSchema-first extraction with strict null handling.
Extract the following from the text below into JSON:
Schema: {"[field1]": "string", "[field2]": "number|null", "[field3]": "string[]"}
Rules: JSON only, no commentary; null where the text is silent; never infer a value.
Text:
[PASTE TEXT]Mistral Model & Prompting Update Log
Mistral Large 4 open weights
● LatestLate October 2026- Mistral released the open weights of an improved Mistral Large 4 on Hugging Face (mistralai/Mistral-Large-4-1T-A52B) under the Mistral Ecosystem Open-Weight licence, a modified MIT licence.
- Three more weeks of reinforcement learning than the preview, with gains in agentic coding, defensive cybersecurity and reasoning efficiency (fewer output tokens).
- Reasoning effort is now low, medium or high (default high). The preview's "none" maps to low and is no longer accepted; medium is new. Mistral recommends temperature 1.0 and top_p 0.95 at every level.
- About 49B parameters active per token (52B counting embedding and output layers). The licence allows commercial use, but offering it as a model-as-a-service business needs a separate licence from Mistral.
- API pricing: $0.70 per million input tokens, $2.10 per million output tokens.
Mistral Large 4 preview
October 6, 2026- Mistral released Mistral Large 4 ("le Chonk"), its largest and most capable model, as a public preview API on Mistral Studio (API name mistral-large-4).
- A natively multimodal hybrid instruct-and-reasoning Mixture-of-Experts model with about 1 trillion total parameters and 52B active per token, and a 1M-token context in Mistral's docs.
- Aimed at hard coding, agentic, cyber, finance and legal work.
- Preview launch pricing: $1.36 per million input tokens, $4.18 per million output tokens.
Mistral Medium 3.5
Late April 2026- Mistral released Mistral Medium 3.5 (mistral-medium-3-5), which it calls its "first flagship merged model": one dense 128B model for instruction-following, reasoning and coding.
- It replaces three separate models — Mistral Medium 3.1, Magistral and Devstral 2 — and is aimed at agentic and coding work.
- 256K context window, text and image input, and open weights under a modified MIT licence (with restrictions for high-revenue companies).
- Reasoning is configurable per request with reasoning_effort: "none" for standard answers, "high" for complex tasks.
- Recommended sampling: temperature 0.7 and top_p 0.95 with reasoning "high"; temperature 0.0–0.7 and top_p 1.0 with reasoning "none".
Mistral Small 4
March 16, 2026- Mistral Small 4 (mistral-small-2603) is a 119B Mixture-of-Experts model with 128 experts and 6.5B parameters active per token.
- It was the first Mistral model to merge Magistral (reasoning), Pixtral (vision) and Devstral (agentic coding) into one model, with configurable reasoning effort.
- 256K context, text and image input, Apache 2.0 licence.
Mistral Large 3
December 2025- Mistral Large 3 (mistral-large-2512) is a sparse Mixture-of-Experts model with 675B total and 41B active parameters.
- Open weights under Apache 2.0, a 256K context window and native image input.
How to Use the Prompt Generator
Define Your Task
Pick the Mistral model and reasoning effort, select a category, set your task focus (general, coding, or structured output), and describe your task. Be direct and concise — Mistral rewards efficiency.
Set Format & Constraints
Choose output format (especially JSON/API schemas for function calling), set tone, and specify constraints including language preferences.
Generate & Use
Click "Generate" to get an optimized prompt. Copy and paste into Le Chat, the Mistral API, or any Mistral-compatible platform.
Frequently Asked Questions
What is Mistral?
Mistral is a leading European AI company building state-of-the-art language models. Its most capable model is Mistral Large 4 (October 6, 2026), a natively multimodal Mixture-of-Experts model with about 1 trillion parameters, while Mistral Medium 3.5, a dense 128B open-weight model with a 256K context window, remains the everyday default. Mistral models are known for efficiency, structured-output strength, native function calling, multilingual European coverage, and an EU-centric perspective on data, privacy, and regulation.
What is Mistral Large 4?
Mistral Large 4 (nicknamed "le Chonk") is Mistral's largest and most capable model, first released on October 6, 2026 as a public preview API on Mistral Studio, with open weights following at the end of October (Hugging Face: mistralai/Mistral-Large-4-1T-A52B, modified-MIT licence). It is a hybrid instruct-and-reasoning Mixture-of-Experts model with about 1 trillion total parameters and 52B active per token, text and image input, and a 1M-token context (API name mistral-large-4). The open-weight release got three more weeks of reinforcement learning, improving coding, defensive cybersecurity and reasoning efficiency. Its reasoning_effort is low, medium or high (default high). Pricing is $0.70 per million input tokens and $2.10 per million output tokens.
What's new in Mistral Medium 3.5?
Mistral released Mistral Medium 3.5 (API name mistral-medium-3-5) at the end of April 2026. Mistral calls it its "first flagship merged model": one dense 128B set of weights that handles instruction-following, reasoning and coding, replacing Mistral Medium 3.1, Magistral and Devstral 2. It has a 256K context window, takes text and image input, and its weights are open under a modified MIT licence (with restrictions for high-revenue companies). Reasoning is set per request with reasoning_effort: "none" for standard answers, "high" for complex tasks.
Which Mistral model should I pick?
Use Mistral Medium 3.5 for most work, including coding and agentic tasks, and switch reasoning_effort to "high" when the problem is hard. Step up to Mistral Large 4 (October 2026) for the hardest jobs: very long documents or codebases, multi-step agentic work and high-stakes analysis. Mistral Small 4 (March 16, 2026) is a 119B Mixture-of-Experts model with only 6.5B parameters active per token, a 256K context and an Apache 2.0 licence; it was the first Mistral model to merge Magistral (reasoning), Pixtral (vision) and Devstral (coding), and it suits fast, low-cost jobs. The older Mistral Large 3 (December 2025) is a 675B MoE model with 41B active parameters and a 256K context, available under Apache 2.0.
What temperature and reasoning settings should I use?
Mistral's guidance for Medium 3.5: with reasoning_effort="high", use temperature 0.7 (and top_p 0.95). With reasoning_effort="none", use a temperature between 0.0 and 0.7 — near 0 for extraction, classification and code, higher for creative writing. Mistral Large 4 is different: reasoning_effort is "low", "medium" or "high" (default high, "none" is rejected), with temperature 1.0 and top_p 0.95 at every level. Use high for complex or agentic work, medium for everyday tasks and low for quick replies. In Le Chat you don't set these directly, so the generated prompt carries the reasoning instruction in words instead.
How is prompting Mistral different from other tools?
Mistral favors concise, well-structured prompts over verbose ones. Lead with role assignment, add detail for context, and explicitly name the output format. Mistral is especially strong at function calling and structured outputs — name exact JSON keys and types. It also handles multiple European languages natively.
How does this prompt generator work?
You provide structured inputs — task, role, context, audience, Mistral model, reasoning effort, task focus (general / coding / structured output), and output language. Our AI applies Mistral's 2026 prompting framework: tight role assignment, explicit format, few-shot examples where they help, and clear constraint boundaries.
Is this tool free to use?
Yes. You get 1 free prompt generation per day with no signup required. For unlimited access, sign up for a Promptslove membership which includes all AI tools and 29,000+ premium prompts.
What is Mistral best at?
Mistral excels at structured output generation (JSON, API schemas), native function calling, multilingual European content, efficient code generation, and EU-specific analytical tasks (GDPR, regulatory compliance, multilingual marketing).
Where can I use the generated prompts?
Use them with Le Chat, the Mistral API, or any platform hosting Mistral models (Hugging Face, AWS Bedrock, Azure, Vertex AI). The structured approach also degrades gracefully on other tools — ChatGPT, Claude, Gemini, Grok, MiniMax, Qwen.
Does Mistral support function calling?
Yes. Mistral has native function calling and is known for being strong at structured output. When you pick "Structured Output / JSON" as the task focus, this generator names the exact keys, types, and example values you want returned.
How many prompts can I generate for free?
You get 1 free prompt generation every 24 hours. This resets automatically. For unlimited generations, consider signing up for a Promptslove membership.
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