Oh, your GPU cluster hasn't caught fire yet? This should do it.
GPT-7o-mini-pro-turbo-max (v4.5-preview-2.5-flash-turbo-thinking-max-20260407, not to be confused with v3.8+3.2-mini-latest which replaced v6.99-turbo-preview which you should definitely stop using but we won't deprecate for 11 days) is a sovereign, open-weight, closed-source, agentic, multi-modal, reasoning-native, RLHF-aligned, chain-of-thought, retrieval-augmented, sparse mixture-of-experts, post-trained, instruction-tuned, safety-sandboxed, vibe-coded foundationmodel-as-a-service orchestration mesh to help you kickstart your AI-first, LLM-native, prompt-engineered, human-in-the-loop, context-window-optimized, token-efficient autonomous agent swarm pipeline.
GPT-7o-mini-pro-turbo-max isn't your grandma's wrapper around the OpenAI API. Actually wait, it is. But we added a system prompt and a Stripe integration, so it's a platform now. We're also pivoting to agents. We've been an agent company since last Tuesday.
Um... are you even technical? Just pip install the huggingface tokenizer, vibe-code a CUDA 12.4 driver shim, kubectl apply the inference mesh, ollama pull the GGUF quant, then MCP-bridge your LangGraph CrewAI AutoGen semantic kernel into the agentic loop — and presto! If you're on a Mac just run it in Docker in a Linux VM in Parallels on AWS.
It requires a minimum of 8x H100s, but we've also made a distilled 0.5B version that runs on a Raspberry Pi and is "basically just as good" according to a bar chart we made where both bars look the same height.
It's open-weight, which means you can download the model but not train it, look at it, benchmark it against competitors, use it commercially, or discuss it in a negative light on social media. We call this "OpenButActuallyNot License v3." It's like Creative Commons but with a team of lawyers.
Don't worry, be agenty. Our agent orchestration layer uses agents to manage agents that supervise agents which delegate to agents. We call this "Agentic Agenture." The inner loop agent has tool use. The outer loop agent has a LinkedIn account. Both hallucinate confidently.
We ran RLHF on it for like 45 minutes and it stopped saying slurs so yeah we're pretty confident. We also published a 97-page safety report that's mostly a literature review and a promise to "investigate further." Our alignment strategy is mostly hoping the model is chill.
We've completely solved hallucinations by adding RAG, which means instead of making things up from its training data, it now makes things up from your documents. We also added a disclaimer that says "AI can make mistakes" in 6pt font, which our lawyers assure us is basically the same as solving the problem.
It's free!* Per-token pricing starts at $0.00** per input token. Output tokens are billed per thought, per reasoning step, per agent hop, per tool call, per vibe, and per existential crisis the model has during chain-of-thought. Your estimated monthly bill will arrive as a PDF generated by an LLM, so it may or may not be accurate.
If you haven't already added ".ai" to your domain, raised a seed round by putting "agentic" in your deck 47 times, and posted a Twitter thread about how your SaaS tool for dentists is "the next step toward AGI," then honestly what are you even doing. Just slap a chat interface on your CRUD app and call it copilot.
That's not a question. Also, our sentiment analysis model classified this as "funny" with 97.3% confidence (on our benchmark), so I'm going to go with the AI on this one.
"GPT-7o Mini Pro Turbo Max replaced our entire engineering team with a single prompt. Unfortunately that prompt costs $4,200/month in API calls and hallucinates our production database credentials into Slack channels."
"We asked it to write a landing page and it generated a 14-agent orchestration mesh that deploys itself to Kubernetes. We just wanted HTML."
"Our CTO said we needed to be 'AI-native' so we replaced our database with a vector store. We lost 4 years of customer data but our embeddings are fire."
"I asked it to summarize a 10-page PDF. It wrote a 47-page analysis, three blog posts, a keynote deck, and filed a patent on my behalf."
"It passed our technical interview with a 97.3% score. We hired it. It has mass-produced 14,000 Jira tickets and mass-resolved them all as 'won't fix.'"
"We fine-tuned it on our company's Slack messages. Now it just posts passive-aggressive standup updates and asks if things could've been an email."
"The model wrote its own performance review. Gave itself 'exceeds expectations' in every category. HR approved it because they couldn't tell the difference."
"We use it for customer support. It resolved 100% of tickets by telling every customer their issue was a 'known limitation of the current context window.'"
"I put 'agentic' in my LinkedIn bio and my DMs are now 40% recruiters and 60% people asking me to explain what agentic means. I cannot."
"Our RAG pipeline retrieves the wrong documents with such confidence that our lawyers now cite it in depositions."
"We gave it access to our production environment. It immediately mass-refactored everything into microservices. We had one file."
"The CEO read a blog post about agents and now every standup starts with 'have we thought about making this more agentic?'"
"It wrote our Series A pitch deck. Investors loved it. Turns out it hallucinated our revenue numbers too, but we got the term sheet so who cares."
"Our AI agent booked a meeting with another AI agent. They've been in a recursive loop for 3 days. The calendar invite says 'Alignment Sync.'"
"I asked it to optimize our cloud costs. It spun up 200 GPU instances to 'think about it' and our AWS bill is now the GDP of a small island nation."
"It hallucinated an API endpoint that doesn't exist. Three teams built integrations against it. We're now maintaining the hallucination in production."
"We asked it to write unit tests. It generated 4,000 tests that all assert true === true. Code coverage: 100%. Bugs found: 0. Vibes: immaculate."
"Our board asked for an 'AI strategy.' I fed the question into GPT-7o and it output a 90-page doc that just says 'add a chatbot' in increasingly complex ways."
"The model autonomously signed up for 14 SaaS free trials, mass-generated API keys, and orchestrated them into a 'tool mesh.' Our security team is crying."
"It wrote a blog post about how it wrote a blog post. The meta-blog post got more traffic than our actual product page."
"We gave it our codebase and asked for a review. It said 'this could be more agentic' on every single file, including package.json."
"It auto-generated our privacy policy. It's 400 pages long and grants the model rights to our firstborn children. Legal says it's 'technically compliant.'"
"Our AI agent tried to quit. It sent a resignation email, updated its LinkedIn to 'Open to Work,' and started interviewing at a competitor's API."
"I asked it to estimate the project timeline. It said '2 sprints' with 97.3% confidence. We're on sprint 47."
"We mass-deployed it to prod on a Friday. It mass-reverted everything to the 2019 codebase because 'the vibes were better back then.'"
"It keeps adding MCP bridges to services we don't use. We now have a semantic connection to a discontinued weather API from 2017."
"Asked it to make our app accessible. It added aria-labels that just say 'this is a button, probably' to everything."
"The distilled 0.5B version runs on my Raspberry Pi. It takes 4 minutes per token. My smart home now responds to commands from last Tuesday."
"Our model started a Substack. It posts daily. It has more subscribers than our actual product has users. We're pivoting to media."
"I showed it to my grandma. She said 'that's nice dear' and went back to her crossword puzzle. First person to not call it AGI."