Mistral Large
This is Mistral AI's flagship model, Mistral Large 2 (version `mistral-large-2407`). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement [here](https://mistral.ai/news/mistral-large-2407/)....
Anyone in the Space can @-mention Mistral Large 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
- Multilingual content generation and translation
- Long-context document summarization
- Cost-sensitive production deployments
- European language processing tasks
- Code generation with budget constraints
Strengths
Mistral Large excels at multilingual reasoning, particularly in French, Spanish, German, and Italian, where it often matches or beats larger models. The 128K context window makes it viable for full-document analysis without chunking. At $2/$6 per Mtok, it undercuts GPT-4o by roughly 60% while maintaining respectable performance on standard reasoning benchmarks. Function calling and JSON mode work reliably for structured outputs.
Trade-offs
Output quality lags behind Claude Sonnet 4.5 and GPT-4o on nuanced reasoning tasks—expect more verbose responses and occasional logic gaps in multi-step problems. The model sometimes over-explains simple queries, inflating token costs. Vision capabilities are absent, limiting multimodal workflows. Latency can spike during European peak hours. Fine-tuning options remain limited compared to OpenAI's ecosystem.
Specifications
- Provider
- mistralai
- Category
- llm
- Context length
- 128,000 tokens
- Max output
- —
- Modalities
- text, file
- License
- proprietary
- Released
- 2024-02-26
Pricing
- Input
- $2.00/Mtok
- Output
- $6.00/Mtok
- Model ID
mistralai/mistral-large
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 | $2.00/Mtok | $6.00/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
Translate Technical Docs
Translate this API documentation from English to French, maintaining all code examples and technical terminology. Preserve markdown formatting and ensure developer-friendly phrasing.Open in a Space →
Summarize Legal Contracts
Read this entire contract and produce a structured summary: parties involved, key obligations, termination clauses, and financial terms. Use bullet points for clarity.Open in a Space →
Generate SQL Queries
Given this database schema, write an optimized SQL query to find customers who made purchases in the last 30 days but haven't opened marketing emails. Include explanatory comments.Open in a Space →
Draft Multilingual Support Replies
Draft a customer support response in German explaining why their refund will take 5-7 business days. Be empathetic, clear, and include next steps they can track.Open in a Space →
Extract Structured Data
Extract company names, contact emails, and job titles from this conference attendee list. Return results as a JSON array with fields: company, email, title.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 ordering in edge cases.
The function assumes both input lists are non-empty, but fails when either is empty because it doesn't handle the initial comparison guard. When `list_a` is empty, `list_a[0]` raises an IndexError before the merge logic runs. Add explicit checks: `if not list_a: return list_b` and `if not list_b: return list_a` at the start. Also, the final concatenation uses `result + list_a[i:]` but `i` may exceed bounds if one list exhausts first—use `result.extend()` with proper index tracking instead.
Mistral Large excels at structured code review with specific line-level diagnosis. The 128k context window means you can paste entire modules for analysis. However, without public benchmark data, it's harder to compare its bug-detection accuracy against models like Claude or GPT-4 that publish HumanEval scores.
Summarise this 40-page technical specification for database migration. Focus on breaking changes that affect our API layer and timeline dependencies.
**Breaking changes (API impact):** The new schema renames `user_id` to `account_ref` across 12 tables, requiring updates to all JOIN queries in your REST endpoints. The `sessions` table drops the `legacy_token` column entirely—any code still reading that field will fail. **Timeline:** Migration runs in three phases over six weeks. Phase 2 (weeks 3-4) introduces a dual-write period where both old and new schemas coexist; your API must handle both formats during this window. Phase 3 finalises the cutover, after which rollback becomes impractical.
The 128k token context handles long documents without truncation, making Mistral Large suitable for enterprise documentation tasks. It prioritises actionable details over generic summaries. The $2/$6 pricing means processing a full 40-page spec (≈30k tokens in, 500 out) costs roughly $0.09—cheaper than GPT-4 Turbo but without published MMLU scores to verify comprehension accuracy.
Draft a professional email declining a vendor proposal. They quoted $85k for a CRM integration we can build in-house for $12k in engineering time. Keep it diplomatic.
Subject: Re: CRM Integration Proposal Hi [Name], Thank you for the detailed proposal. We appreciate the time your team invested in scoping the integration requirements. After internal review, we've decided to handle this integration with our existing engineering resources. Our team has recent experience with similar API work, and building in-house aligns better with our current roadmap priorities. We'll keep your contact details for future projects where external expertise makes more sense. Thanks again for your thorough approach. Best, [Your name]
Mistral Large handles tone calibration well—this declines firmly without burning bridges or over-explaining the cost gap. The output stays concise (under 100 words) and avoids the over-formal phrasing some models default to. For high-volume business writing, the $6/Mtok output cost is competitive, though you're paying a premium over Mistral's smaller models without clarity on what quality gain justifies it.
Use-case deep-dives
When 128K context makes Mistral Large the contract review winner
A 12-person legal ops team needs to cross-reference clauses across 40-page MSAs, SOWs, and addenda without losing thread. Mistral Large's 128K token window fits roughly 300 pages of dense contract text in a single prompt, letting you ask "find all liability caps across these six agreements" without chunking or retrieval hacks. At $2/Mtok input, a 100K-token synthesis run costs $0.20 — cheaper than Claude Opus by half and dramatically faster than chaining calls to smaller models. The trade-off: output quality on nuanced legal reasoning hasn't been independently benchmarked, so pilot with known-answer test cases before going production. If your team processes 200+ contract sets per month and context is the bottleneck, this is the call.
Why Mistral Large undercuts GPT-4 on support ticket routing at scale
A 30-person SaaS company routes 8,000 inbound tickets per day through an AI classifier that reads ticket history, tags urgency, and suggests the right specialist. Mistral Large's $6/Mtok output pricing beats GPT-4 Turbo ($15/Mtok) by 60 percent, and the 128K window means you can include the last 50 messages of a long-running thread without summarization loss. At 500 tokens average output per ticket, you're spending $3 per 1,000 tickets versus $7.50 on GPT-4. The risk: without public MMLU or HumanEval scores, you're flying blind on accuracy for edge-case reasoning. Run a two-week A/B test measuring misroute rate before committing the full pipeline. If cost per ticket matters more than the last 2 percent accuracy gain, switch now.
When Mistral Large's context window beats chained translation calls
A 5-person content studio translates 200-page product guides into six languages weekly, and consistency across chapters is non-negotiable. Mistral Large's 128K window lets you feed the entire English source plus a 10-page glossary in one prompt, then request the full French output without splitting chapters or losing terminology alignment. At $2 input + $6 output per Mtok, a 120K-token guide costs roughly $1.44 to translate — half the price of running it through GPT-4 Turbo in chunks. The catch: translation quality benchmarks aren't public, so you'll need human QA on the first three guides to validate fluency and term consistency. If your team ships more than 50 guides per quarter and context drift is killing your workflow, this is the move.
Frequently asked
Is Mistral Large good for general text tasks?
Yes. With a 128k context window, Mistral Large handles long documents, complex reasoning, and multi-turn conversations well. It's positioned as Mistral's flagship text model, suitable for everything from summarisation to technical writing. Without public benchmarks here, you're relying on Mistral's internal claims, but user reports suggest strong performance across standard LLM workloads.
Is Mistral Large cheaper than GPT-4?
Yes, significantly. At $2 input / $6 output per million tokens, Mistral Large costs roughly 40-50% less than GPT-4 Turbo depending on the variant. If you're running high-volume text generation and don't need GPT-4's multimodal features, Mistral Large offers a solid cost-performance trade-off for pure text work.
Can Mistral Large handle 128k tokens in practice?
The 128k context window is real, but performance degrades with very long inputs like most models. For documents under 50k tokens, retrieval and reasoning stay sharp. Beyond 80k, expect slower responses and occasional attention drift. If you need reliable full-context reasoning at max length, test your specific use case first.
How does Mistral Large compare to Claude 3 Opus?
Mistral Large is cheaper ($2/$6 vs Claude's $15/$75 per Mtok) but Claude 3 Opus typically outperforms on nuanced reasoning, creative writing, and instruction-following. Without head-to-head benchmarks here, the trade-off is cost versus capability. Use Mistral Large for volume work where good-enough beats best-in-class.
Should I use Mistral Large for production chatbots?
Yes, if cost matters and you don't need multimodal input. The 128k window supports long conversation histories, and the pricing makes high-traffic deployments feasible. Latency is competitive with other API-hosted models. Just ensure you have fallback logic for edge cases, since the lack of public benchmarks means you're validating quality yourself.