Introducing Parallel Fast Mode at $1 CPM for Frontier Intelligence

Some things just pair together perfectly like peanut butter and jelly. That's how well Parallel's newly introduced fast search pairs with frontier Luna-class models. Have you checked your OpenAI bill since Luna inference prices dropped 80%? Don't be surprised if you find that you are now spending more on search than on inference. Switch to Parallel's fast search to experience 10x lower costs, and higher speed and accuracy.

With Fast mode, Parallel leads the Pareto frontier on both quality vs. cost and quality vs. speed. $1 per 1k queries for low-latency, high-quality web search!

Capable models have gotten cheaper. Search hasn’t, until now. Today we’re introducing Fast mode for Parallel Search: frontier-quality web search for AI that’s 10x cheaper than the default from model labs. - $1 per 1,000 results - 700ms p50 latency - #3 on Artificial Analysis Search Index for intelligence Parallel Fast is the only web search that’s cheap, fast, and accurate. It’s optimized for today’s leading class of cost-effective models: GPT-5.6 Luna, DeepSeek V4 Pro, MiniMax M3, and Qwen3.8 27B: - 10x cheaper than frontier labs, 5x cheaper than other APIs at $1 per 1,000 results - On the quality vs. cost Pareto frontier - On the quality vs. latency Pareto frontier - Nearly matches frontier accuracy set by Parallel Advanced (SOTA) Read the announcement: https://lnkd.in/e4PUSb2z

Our best search API mode is here. It's called Fast. - 700ms Latency - Near-frontier accuracy (2 pts shy of #1, Parallel Advanced) - $1 / 1,000 results Cheap models can't be bogged down by web search that costs more than the inference itself. Parallel Search is now the best choice for everyday models: - GPT 5.6 Luna - DeepSeek V4 Flash & Pro - MiniMax-M3 - Qwen3.8 27b - Gemini 3.7 Flash Try it and let us know what you think!

Neon just proved something most AI companies don't want you to know: the best model for a specific task is probably open-source, and it’s ridiculously cheaper. Their latest experiment, Castform, outperforms GPT-5.6 Sol on retrieval-based workloads while running on open models that cost less than 1/100th the price. This isn’t a cherry-picked academic demo—it’s a production system handling real queries. The economics shift from “per-token cost is manageable” to “we can now afford to run AI on every single request without thinking twice.” For years, the narrative has been that proprietary frontier models are unbeatable on quality, and you pay a premium for that edge. That story is cracking. When open models beat GPT-class systems by design—on accuracy, speed, and cost—the “just use the biggest API” default starts looking like a financial mistake, not a technical decision. The real unlock here is that teams can now compete on retrieval without a massive GPU budget. You don't need to be a hyperscaler to serve world-class AI features. You need a sharp evaluation pipeline and the willingness to swap one model for another when the data says so. The open-source flywheel is spinning faster than ever. If you’re still paying per-query rates for a model that an open alternative already beats on your exact task, what’s the business case? What’s the one workload in your stack where you’d swap proprietary for open if the price gap was 100x and performance stayed the same—or got better? If you've shipped something like this, repost with "HOWCASTFORMN" — I'll DM you the source and compare notes.

DeepSeek has done it again—this time not with a groundbreaking leap in AI capabilities, but with a price tag that’s turning heads across the tech world. Their new model, V4-Flash, delivers artificial intelligence at a cost so low it’s almost unbelievable: completing a full suite of intelligence tests costs just 3 cents. To compare, the same tests run $3.15 on Anthropic’s Claude Fable 5, making DeepSeek’s offering roughly 100 times cheaper. 💸 How is this possible? V4-Flash is engineered for efficiency. It packs a massive 284 billion parameters but activates only 13 billion during inference, allowing it to handle routine, high-volume tasks at blazing speed with minimal resource consumption. With a million-token context window, it’s built for scale. DeepSeek’s pricing is equally aggressive: $0.14 per million input tokens and $0.28 per million output tokens, dramatically undercutting competitors even after OpenAI’s recent 80% price cut on GPT-5.6. But the low cost comes with a catch. In rigorous knowledge tests, V4-Flash scored just 37% with an 84% hallucination rate, meaning it’s prone to significant errors if left unsupervised. This isn’t a magic bullet for critical, high-stakes work—it’s a workhorse for cost-sensitive applications where human oversight is viable. Why is DeepSeek making such aggressive moves? Rumors point to a potential IPO, with the company doubling down on its playbook: democratizing AI through unbeatable prices. This model rattles the dominance of deep-pocketed labs and signals that the AI race is shifting from who has the smartest model to who can deliver value at scale. Is the era of ultra-cheap, good-enough AI here? We’d love to hear your thoughts. 🤖📉 #ArtificialIntelligence #DeepSeek #TechDisruption

DeepSeek has done it again—this time not with a groundbreaking leap in AI capabilities, but with a price tag that’s turning heads across the tech world. Their new model, V4-Flash, delivers artificial intelligence at a cost so low it’s almost unbelievable: completing a full suite of intelligence tests costs just 3 cents. To compare, the same tests run $3.15 on Anthropic’s Claude Fable 5, making DeepSeek’s offering roughly 100 times cheaper. 💸 How is this possible? V4-Flash is engineered for efficiency. It packs a massive 284 billion parameters but activates only 13 billion during inference, allowing it to handle routine, high-volume tasks at blazing speed with minimal resource consumption. With a million-token context window, it’s built for scale. DeepSeek’s pricing is equally aggressive: $0.14 per million input tokens and $0.28 per million output tokens, dramatically undercutting competitors even after OpenAI’s recent 80% price cut on GPT-5.6. But the low cost comes with a catch. In rigorous knowledge tests, V4-Flash scored just 37% with an 84% hallucination rate, meaning it’s prone to significant errors if left unsupervised. This isn’t a magic bullet for critical, high-stakes work—it’s a workhorse for cost-sensitive applications where human oversight is viable. Why is DeepSeek making such aggressive moves? Rumors point to a potential IPO, with the company doubling down on its playbook: democratizing AI through unbeatable prices. This model rattles the dominance of deep-pocketed labs and signals that the AI race is shifting from who has the smartest model to who can deliver value at scale. Is the era of ultra-cheap, good-enough AI here? We’d love to hear your thoughts. 🤖📉 #ArtificialIntelligence #DeepSeek #TechDisruption

DeepSeek has done it again—this time not with a groundbreaking leap in AI capabilities, but with a price tag that’s turning heads across the tech world. Their new model, V4-Flash, delivers artificial intelligence at a cost so low it’s almost unbelievable: completing a full suite of intelligence tests costs just 3 cents. To compare, the same tests run $3.15 on Anthropic’s Claude Fable 5, making DeepSeek’s offering roughly 100 times cheaper. 💸 How is this possible? V4-Flash is engineered for efficiency. It packs a massive 284 billion parameters but activates only 13 billion during inference, allowing it to handle routine, high-volume tasks at blazing speed with minimal resource consumption. With a million-token context window, it’s built for scale. DeepSeek’s pricing is equally aggressive: $0.14 per million input tokens and $0.28 per million output tokens, dramatically undercutting competitors even after OpenAI’s recent 80% price cut on GPT-5.6. But the low cost comes with a catch. In rigorous knowledge tests, V4-Flash scored just 37% with an 84% hallucination rate, meaning it’s prone to significant errors if left unsupervised. This isn’t a magic bullet for critical, high-stakes work—it’s a workhorse for cost-sensitive applications where human oversight is viable. Why is DeepSeek making such aggressive moves? Rumors point to a potential IPO, with the company doubling down on its playbook: democratizing AI through unbeatable prices. This model rattles the dominance of deep-pocketed labs and signals that the AI race is shifting from who has the smartest model to who can deliver value at scale. Is the era of ultra-cheap, good-enough AI here? We’d love to hear your thoughts. 🤖📉 #ArtificialIntelligence #DeepSeek #TechDisruption

DeepSeek has done it again—this time not with a groundbreaking leap in AI capabilities, but with a price tag that’s turning heads across the tech world. Their new model, V4-Flash, delivers artificial intelligence at a cost so low it’s almost unbelievable: completing a full suite of intelligence tests costs just 3 cents. To compare, the same tests run $3.15 on Anthropic’s Claude Fable 5, making DeepSeek’s offering roughly 100 times cheaper. 💸 How is this possible? V4-Flash is engineered for efficiency. It packs a massive 284 billion parameters but activates only 13 billion during inference, allowing it to handle routine, high-volume tasks at blazing speed with minimal resource consumption. With a million-token context window, it’s built for scale. DeepSeek’s pricing is equally aggressive: $0.14 per million input tokens and $0.28 per million output tokens, dramatically undercutting competitors even after OpenAI’s recent 80% price cut on GPT-5.6. But the low cost comes with a catch. In rigorous knowledge tests, V4-Flash scored just 37% with an 84% hallucination rate, meaning it’s prone to significant errors if left unsupervised. This isn’t a magic bullet for critical, high-stakes work—it’s a workhorse for cost-sensitive applications where human oversight is viable. Why is DeepSeek making such aggressive moves? Rumors point to a potential IPO, with the company doubling down on its playbook: democratizing AI through unbeatable prices. This model rattles the dominance of deep-pocketed labs and signals that the AI race is shifting from who has the smartest model to who can deliver value at scale. Is the era of ultra-cheap, good-enough AI here? We’d love to hear your thoughts. 🤖📉 #ArtificialIntelligence #DeepSeek #TechDisruption

Found this new research paper - what if the biggest cost in AI agents is NOT the model, or even the tools? Think of an AI agent like a driver. You can give two drivers the same car, the same destination, and the same roads. Yet one might arrive in 20 minutes while the other takes hours. The difference? How they are guided. That’s the idea behind "The Scaffolding Matters More Than the Interface." The researchers compared MCP and CLI tool use across 7 agent scaffoldings and 5 language models, using the same real-world software task. The surprising finding: the scaffolding around the agent often mattered far more than the tool interface itself. Across completed runs, median input-token usage varied from 14.6K to 288.8K tokens which is nearly 20x. For one 27B model, the gap was even more dramatic: 17K tokens vs. 2.42M tokens. That’s a 139x difference for essentially the same task. The paper doesn’t conclude that MCP is inherently expensive. In the controlled MCP-vs-CLI comparison, the results were mixed. The bigger lesson is about agent design. Prompt loops, context management, tool schemas, caching, verification, and failure handling can dramatically change how efficiently an agent works. Full paper link - https://lnkd.in/dqg-JrYc #AIAgents #GenerativeAI #ArtificialIntelligence #MachineLearning #MCP