Mistral New AI Crushes GPT 4o and Claude 3.7 and Cost Less Than DeepSeek!
Mistral Medium 3: A Frontier-Class AI Challenging GPT-4 at a Fraction of the Cost
Mistral just launched Medium 3, a frontier-class AI model that outperforms GPT-4.0 and Clod 3.7 Sonnet in coding, languages and even multimodal tasks, while costing a fraction to run. It hits over 90% of Clod’s performance for just $0.40 per million input tokens, and runs smoothly on only four GPUs. And now that it powers LayChat Enterprise with deep integrations, privacy-first architecture and no-code AI agents, OpenAI finally has a serious competitor coming straight out of Europe.
The catchphrase Mistral’s own research blog led with is, Medium is the new large, and the company is leaning into that pretty hard. Medium 3 sits between their featherweight small and whatever large surprise they’re teasing for later, but don’t let the name fool you. Internally, it’s what they call a frontier-class model.
It delivers performance that lands in the same neighborhood as Anthropic’s Clod 3.7 Sonnet, Cohere’s Command-A, Meta’s Llama 4 Maverick, and even OpenAI’s freshly announced GPT-4.0, while pulling that off on a much skinnier compute diet. In plain English, you can wedge this thing into a four-GPU on-prem rig, or spin it up in a cloud VPC, and still crank out results that would usually demand far chunkier hardware. Now, the headline number that made everyone raise an eyebrow is the cost.
Benchmarks show Medium 3 reaches more than 90% of Clod Sonnet’s overall benchmark score, yet Mistral quotes just $0.40 per million input tokens and $20.80 per million output tokens when you hit their API. For comparison, Sonnet is listed at $3 per million input and $15 per million output. Some of Mistral’s own research material even shows an alternative rate card of $2 per million output.
Medium 3 Delivers Elite Performance, Multilingual Mastery, and STEM Power at One-Eighth the Cost
So the exact figure depends on which SKU or deployment path you pick, but either way you’re staring at something in the neighborhood of an eight-times price cut. That’s wild in a market where model bills blow up faster than GPU stock on launch day. Performance claims always need receipts, and Mistral came armed.
On Human Eval and Multi-PLE, the two coding benchmarks everyone loves to quote Medium 3 matches or beats Clod Sonnet and GPT-4.0. Third-party human evaluations from Surge show it winning 82% of coding scenarios against Llama 4 Maverick and nearly 70% against Cohere Command A. It’s not just about code, either. Toss multilingual tasks at it and you get higher win rates over Llama 4 Maverick in English 67%, French 71%, Spanish 73%, and Arabic 65%. On multimodal reasoning, numbers like 0.953 on DOCVQA, 0.937 on AI2D, and 0.826 on ChartQA pop up impressive because multimodal is still where many mid-sized models are.
All that horsepower turns out to be especially handy for STEM workloads. Medium 3 doesn’t lock up while chewing on giant math proofs or engineering docs, and it compiles code fast enough that dev teams in finance, energy, and healthcare have already plugged data versions into production pipelines. A couple of those early testers are reportedly letting the model pre-train continuously on proprietary data, then fine-tuning in quick bursts when requirements shift, effectively running an in-house feedback loop without the headache of starting from scratch every time.
That adapt-as-you-go angle is part of Mistral’s pitch. Don’t pick between Blackbox, SAS, fine-tuning, or a DIY deployment, just blend both. Money matters, too.
Medium 3 Powers Scalable, GDPR-Ready AI with Hybrid Flexibility for Regulated Industries
And Medium 3 isn’t only cheap compared to Anthropic’s lineup, it also beats DeepSeek v3, which until now enjoyed the reputation of being the cost-efficiency champ. That allows small teams to kick the tires through the API, then graduate to a self-hosted image when the CFO starts breathing down their neck about data residency or vendor lock-in. Mistral even calls Medium 3 a proprietary model, so no MIT-style license, but they’ve kept everything flexible.
Throw it in Mistrala Platefimum, light it up on Amazon SageMaker, or wait a few weeks for IBM Watson X, NVIDIA NIMH, Azure AI Foundry, and Google Cloud Vertex integrations to go live. Whichever route you choose, the company insists, you’ll still be able to slide their weights onto your own GPU stack if you want total control. The under-the-hood strategy is all about hybrid deployments.
You can keep inference in a private subnet, slam a low-latency tenant in a public region for burst traffic, or fork the entire thing and run it fully on-prem. Because we’re talking about a French firm operating under GDPR and soon the EU AI Act, data governance boxes get ticked pretty aggressively. Audit logs, fine-grained ACLs, memory-based personalization, and the ability to unplug from the cloud completely all come baked into the architecture.
That’s gold for banks, hospitals, and utilities who live and die by regulations. And that credibility boost lands at the perfect moment because Medium 3 is already the motor under LeChat Enterprise, the customer-facing layer that Mistral hopes will move it from cool research shop to everyday fixture inside big company workflows. Social media platform delivered exactly what employees wanted, with driving underpass to laser-cut your immersive public speaking programs down to minutes you don’t have to, but bolts on everything a CIO circles in red when they read an AI RFP.
LeChat Enterprise Showcases Medium 3’s Power with Unified Search, No-Code Agents, and Bulletproof Compliance
When you wire it up to Google Drive, Gmail, Calendar, Microsoft SharePoint, OneDrive, or whatever connector they ship next, it does a single-path search across all those silos, then snapshots its sources so compliance knows exactly where each sentence came from. If the file is a 60-page PDF, AutoSummary skims the cruft and the model drops links you can audit. The same Medium 3 stack powers a no-code agent builder.
Drag a few blocks together and suddenly the assistant can pull a contract, update the CRM, and ping legal without anyone writing a cron job. Because everything sits on Medium 3’s cheaper token pricing, finance teams finally get a clean-line item instead of three overlapping SKUs. Security is the non-negotiable.
LeChat Enterprise will run as SaaS in Mistral’s own cloud, but you can flip the switch to a single-tenant region, a private VPC, or an on-prem rack, and keep the data inside your firewall. Access controls are inherited from the source apps, so a board deck locked to the CFO stays locked. Full audit logs ship out for SOC2 and ISO paperwork, which matters if you’re, say, a French bank living under GDPR and the incoming EU AI app.
That EU angle quietly gives Mistral an edge with customers who are wary of routing sensitive traffic through U.S. clouds or Chinese open-weight models. Medium 3 and LeChat Enterprise grab the headlines, but Mistral’s catalog is getting crowded. There’s Mistral Large 2, its GPT-4-class flagship, Pixtral Large for images and docs, Codistral for pure code generation, the LizMinistral edge models that squeeze onto phones, and the Arabic-focused Mistral Saba.
Mistral Scales Fast with €1B Backing, Strategic Deals, and National Support on the Road to IPO
Alright. In March, they even shipped Mistral OCR, an API that turns any PDF into plain text so Medium 3 can actually read the stuff. Legal still prints.
Some models are wide open under Apache 2.0. The newest higher-end weights, including Medium 3, stay proprietary so Mistral can lock down licensed content and offer paid SLAs. That two-track approach is how they square their original openness slogan with the realities of enterprise contracts. If the roadmap feels impatient, look at the cap table.
Since June 2023, the company has raised about 1 billion euros, including a $112 million seed that was Europe’s largest on record, a $415 million Series A led by Andreessen Horowitz, and a 600 million euro mix of equity and debt last summer that parked the valuation at roughly $6 billion. Microsoft chipped in 15 million euros and hosts the weights on Azure, while Nvidia, Cisco, Samsung, and IBM all took smaller slices. On the revenue side, paid API usage and the 14.99 per month LeChat Pro plan are growing, but insiders still peg annual sales in the low eight digits.
So scaling fast is existential. Partnerships help. The French army press agency AFP letting LeChat query every story since 1983.
Shipping giant CMA, CGM, and defense startup Helsing all signed up. Even President Macron pitched LeChat on TV last week, telling viewers to download the French app instead of importing ChatGPT. That kind of home-field backing doesn’t guarantee market share, but it keeps the spotlight bright, while Mistral chases the numbers needed for the IPO that CEO Arthur Mensch keeps hinting at.
Medium 3 Positions Mistral for Global Enterprise Play—with Cost Wins, EU Compliance, and IPO Pressure Mounting
All that context explains why Medium 3 is more than a mid-sized curiosity. It hits the sweet spot between small enough to run on four GPUs and smart enough to finish real work, and it does it at roughly â…› the cost of Anthropic’s Claude Sonnet for the same token count. For dev teams watching cloud bills spike, a 60% benchmark tie with GPT-4 class models for pennies on the dollar is a conversation starter.
For risk officers, the EU jurisdiction and on-prem option tick political and regulatory boxes OpenAI can’t check yet. Looking ahead, the company is openly teasing a large release. If Medium is already closing the gap with open-weight flagships like Llama 4 Maverick, a true Mistral Large 3 could yank the high-end leaderboard again.
But Mistral’s bigger challenge is commercial, not technical. It has to turn brand buzz and government endorsements into sustainable post-GAP revenue before acquisition rumors start looking more attractive than the Nasdaq Bell. Mench told reporters at Davos that the startup is not for sale and a public listing is the plan, but those words only hold if revenue catches up to that $6 billion price tag.
For now, Medium 3 plus LayChat Enterprise give them a real shot. If you’re tracking inference cost, on-prem compliance, or just need an LLM that speaks French and Arabic as well as English, keep an eye on the Google Cloud Marketplace listing that went live yesterday, with Azure AI and AWS Bedrock slots coming next. And if you’ve already tried the PubliclyChat web app, remember, the model behind the curtain now writes code, summarizes PDFs, and cross-references your SharePoint without launching a dozen plugins or draining your GPU budget.
Whether that’s enough to vault Mistral into the same usage tier as OpenAI is the billionaire question, but at least now they’re swinging with heavyweight gloves. Thanks for readings. Catch you in the next one.
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