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5 patterns in Text Arena's price–performance Pareto frontier since 2023: 1. GPT-4-level quality is now ~500x lower cost. - From a ~$50 blended price per million tokens in 2023 to ~$0.10 today. 2. The higher-price end is both better and lower-priced since 2023. - The leading Arena score has...

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Arena.ai

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58,133 次观看 • 3 个月前 •via X (Twitter)

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Are open source models catching up to proprietary models? We’ve looked back at 3 years of Arena’s data to show how the race has evolved. For comparison, we’ve taken the top 20% of the models and uncovered the following: - Before mid 2024: The gap was between 100-150 points - In the second half of 2024, the gap rapidly narrowed to ~50 points - This was driven by the fact that proprietary models have not significantly improved between early 2023 and November 2024 - hovering around the 1350 score for the top 20% of proprietary models - Then since Jan 2025, we saw a parallel improvement in both proprietary and open source models - Meaning that for the last ~14 months the gap stayed steady at around 50-60 points For context, 50 points is the gap between the 1st and 20th place in the Overall leaderboard Today, proprietary models take up the first 20 spots on the Text Arena, with Anthropic, Google DeepMind , xAI and OpenAI leading in the rankings. The top open source models are ranked #20 (GLM-5 by Z.ai ), #23 (Kimi-K2.5-Thinking by Kimi.ai ) and #27 (Qwen3.5-397b-a17b by Qwen ).
0:21

Are open source models catching up to proprietary models? We’ve looked back at 3 years of Arena’s data to show how the race has evolved. For comparison, we’ve taken the top 20% of the models and uncovered the following: - Before mid 2024: The gap was between 100-150 points - In the second half of 2024, the gap rapidly narrowed to ~50 points - This was driven by the fact that proprietary models have not significantly improved between early 2023 and November 2024 - hovering around the 1350 score for the top 20% of proprietary models - Then since Jan 2025, we saw a parallel improvement in both proprietary and open source models - Meaning that for the last ~14 months the gap stayed steady at around 50-60 points For context, 50 points is the gap between the 1st and 20th place in the Overall leaderboard Today, proprietary models take up the first 20 spots on the Text Arena, with Anthropic, Google DeepMind , xAI and OpenAI leading in the rankings. The top open source models are ranked #20 (GLM-5 by Z.ai ), #23 (Kimi-K2.5-Thinking by Kimi.ai ) and #27 (Qwen3.5-397b-a17b by Qwen ).

Arena.ai

22,447 次观看 • 5 个月前

So comes the biggest threat to the AI trade, which relies on ever-increasing revenues to the AI model companies to support ever-increasing valuations. Those revenues are a function of price and quantity. Quantities are all good, from my perspective. Despite increasing competition from open source and Chinese models, OpenAI and Anthropic continue adding more businesses and drawing more token usage from new and existing customers. Price, as a lever, is deteriorating. Over the last month, the average cost per million tokens has fallen 27%. This week, OpenAI cut prices for it's frontier model by another 20%. Further gains to the model companies will have to rely on driving more volume, and driving it even faster as prices fall, to support their valuations.
0:44

So comes the biggest threat to the AI trade, which relies on ever-increasing revenues to the AI model companies to support ever-increasing valuations. Those revenues are a function of price and quantity. Quantities are all good, from my perspective. Despite increasing competition from open source and Chinese models, OpenAI and Anthropic continue adding more businesses and drawing more token usage from new and existing customers. Price, as a lever, is deteriorating. Over the last month, the average cost per million tokens has fallen 27%. This week, OpenAI cut prices for it's frontier model by another 20%. Further gains to the model companies will have to rely on driving more volume, and driving it even faster as prices fall, to support their valuations.

Ara Kharazian

36,639 次观看 • 6 天前

We just beat the best frontier models at a quarter of the price. Now you can too, in one API call. Today we're launching Merge Fusion. Send one prompt to a panel of models, and a judge writes one answer better than any of them alone. Fusion beats Fable 5 outright at 1/4th the cost. It also sets the highest score on the DRACO benchmark.
0:45

We just beat the best frontier models at a quarter of the price. Now you can too, in one API call. Today we're launching Merge Fusion. Send one prompt to a panel of models, and a judge writes one answer better than any of them alone. Fusion beats Fable 5 outright at 1/4th the cost. It also sets the highest score on the DRACO benchmark.

Shensi Ding

409,995 次观看 • 1 个月前

Gavin Baker: "Anthropic is roughly four times more capital efficient than OpenAI - they're growing really fast and they're not burning that much cash" this is him explaining why he thinks token efficiency decides who wins, why xAI has the lowest cost per token of anyone, and what a Tesla board member says is actually the most advanced AI in the world right now "an Anthropic model can give you a comparable quality answer to a Google model using half as many tokens - and each token is literally cost" "xAI has the lowest cost per token because they build data centers faster and at a lower cost than everyone, and they run GPUs at a higher utilization" Tesla board member Antonio Gracias: "Tesla actually is today the most advanced AI in the world - it does the most complex thing a human does, drive a car" bookmark & watch the full conversation ↓
15:52

Gavin Baker: "Anthropic is roughly four times more capital efficient than OpenAI - they're growing really fast and they're not burning that much cash" this is him explaining why he thinks token efficiency decides who wins, why xAI has the lowest cost per token of anyone, and what a Tesla board member says is actually the most advanced AI in the world right now "an Anthropic model can give you a comparable quality answer to a Google model using half as many tokens - and each token is literally cost" "xAI has the lowest cost per token because they build data centers faster and at a lower cost than everyone, and they run GPUs at a higher utilization" Tesla board member Antonio Gracias: "Tesla actually is today the most advanced AI in the world - it does the most complex thing a human does, drive a car" bookmark & watch the full conversation ↓

Сarm1ne

128,347 次观看 • 1 个月前

OpenAI's chairman just responded to the Kimi K3 panic 👇 The timeline calls K3 a frontier killer. Bret Taylor's answer on CNBC: every CEO he talks to asks the same 2 questions. 1️⃣ Am I getting value from my token spend? 2️⃣ What's my moat in the age of AI? His counter on cheap Chinese models: frontier models finish the same task in far fewer tokens. Cheap per token means little if you burn 5x more of them The new battleground is tokens per task, and the price war just forced OpenAI to argue efficiency instead of capability
8:11

OpenAI's chairman just responded to the Kimi K3 panic 👇 The timeline calls K3 a frontier killer. Bret Taylor's answer on CNBC: every CEO he talks to asks the same 2 questions. 1️⃣ Am I getting value from my token spend? 2️⃣ What's my moat in the age of AI? His counter on cheap Chinese models: frontier models finish the same task in far fewer tokens. Cheap per token means little if you burn 5x more of them The new battleground is tokens per task, and the price war just forced OpenAI to argue efficiency instead of capability

Ruben

76,682 次观看 • 1 个月前

On December 15, 2023, LME copper settled at $8,471 per tonne ($3.85 per pound). Today, May 25, 2026, the LME price is $13,660 per tonne, with U.S. copper trading (at a premium) at around $6.46 per pound. That’s a rise of over 60% in just two and a half years. The $15,000 per tonne threshold, needed to incentivise meaningful new supply, which I highlighted back in 2023, is now less than 10% away. Yet global copper supply is weaker today and costs are higher. Meanwhile, the demand for copper from the world’s two largest economies is unprecedented. In the US, potential Section 232 tariffs have put imported copper in the crosshairs, while in Asia we’re entering peak Chinese demand season. There are no semiconductors, no data centers, no electrification without copper… which is why the price of the world’s essential electrical conductor now trades in tandem with Nvidia and the AI boom. Add to the mix EVs, robotics, and defense demand, and copper’s future price direction is clear. In another two and a half years, $20,000 per tonne or $9+ per pound is realistic. Watch the LME warehouses closely and see where the metal actually moves… the world is finally waking up to the true value of copper.
0:28

On December 15, 2023, LME copper settled at $8,471 per tonne ($3.85 per pound). Today, May 25, 2026, the LME price is $13,660 per tonne, with U.S. copper trading (at a premium) at around $6.46 per pound. That’s a rise of over 60% in just two and a half years. The $15,000 per tonne threshold, needed to incentivise meaningful new supply, which I highlighted back in 2023, is now less than 10% away. Yet global copper supply is weaker today and costs are higher. Meanwhile, the demand for copper from the world’s two largest economies is unprecedented. In the US, potential Section 232 tariffs have put imported copper in the crosshairs, while in Asia we’re entering peak Chinese demand season. There are no semiconductors, no data centers, no electrification without copper… which is why the price of the world’s essential electrical conductor now trades in tandem with Nvidia and the AI boom. Add to the mix EVs, robotics, and defense demand, and copper’s future price direction is clear. In another two and a half years, $20,000 per tonne or $9+ per pound is realistic. Watch the LME warehouses closely and see where the metal actually moves… the world is finally waking up to the true value of copper.

Robert Friedland

56,522 次观看 • 3 个月前

Grok 4 Beats OpenAI + The $300 AI Agent Era .@jason and alex 🏴‍☠️🇺🇸🇺🇦 dig into the debut of Grok 4, the new #1 AI model—based on performance and price—and what its leapfrogging of OpenAI means for the AI race. Grok 4 stuns with AGI-like performance—Tim Sweeney says it “feels like AGI” Price collapse: top-tier LLMs dropped from $2 to $0.06 per million tokens since Sept. 2024 But: why are X, OpenAI, Perplexity & others increasing prices at the high end? How self-driving cars + AI agents will unlock new delivery use cases—and how one founder is betting on a future of “autonomous commerce” Plus: The Missouri AG is targeting AI companies for “anti-Trump” output—what does it mean for speech, safety & First Amendment rights in the age of LLMs? If you’re building with AI, investing in infra, or thinking about the next frontier of self-driving + software, this is a must-watch. #AI #AGI #Grok #OpenAI #SelfDriving #VentureCapital #ThisWeekInStartups #Crypto #Fintech #FirstAmendment #Startups
1:11:04

Grok 4 Beats OpenAI + The $300 AI Agent Era .@jason and alex 🏴‍☠️🇺🇸🇺🇦 dig into the debut of Grok 4, the new #1 AI model—based on performance and price—and what its leapfrogging of OpenAI means for the AI race. Grok 4 stuns with AGI-like performance—Tim Sweeney says it “feels like AGI” Price collapse: top-tier LLMs dropped from $2 to $0.06 per million tokens since Sept. 2024 But: why are X, OpenAI, Perplexity & others increasing prices at the high end? How self-driving cars + AI agents will unlock new delivery use cases—and how one founder is betting on a future of “autonomous commerce” Plus: The Missouri AG is targeting AI companies for “anti-Trump” output—what does it mean for speech, safety & First Amendment rights in the age of LLMs? If you’re building with AI, investing in infra, or thinking about the next frontier of self-driving + software, this is a must-watch. #AI #AGI #Grok #OpenAI #SelfDriving #VentureCapital #ThisWeekInStartups #Crypto #Fintech #FirstAmendment #Startups

This Week in Startups

29,443 次观看 • 1 年前

Gavin on how Blackwell and GB300 will end Google's position as the lowest-cost producer of tokens: "I think it's important that Google has been the lowest cost producer of tokens. And this is really important because AI is the first time in my career as a tech investor that being the low cost producer has ever mattered. What Google has been doing as the low cost producer is they have been sucking the economic oxygen out of the AI ecosystem, which is an extremely rational strategy for them -- let's make life really hard for our competitors. We'll see the first models trained on Blackwell in early 2026. I think the first Blackwell model will come from xAI because they build the data centers the fastest. The GB300 is a great chip. It is drop-in compatible in every way with those GB200 racks, and the companies that use the GB300s -- they're going to be the low cost producer of tokens. I think this has pretty profound implications because it has to change Google's strategic calculus. If you have decisive cost advantage, why not AI at a -30% margin. You make it hard for your competitors who need funding to raise the capital that they need. On the other side of that maybe you have an extremely dominant share position. Well, all of that changes once Google is no longer the low-cost producer."
3:33

Gavin on how Blackwell and GB300 will end Google's position as the lowest-cost producer of tokens: "I think it's important that Google has been the lowest cost producer of tokens. And this is really important because AI is the first time in my career as a tech investor that being the low cost producer has ever mattered. What Google has been doing as the low cost producer is they have been sucking the economic oxygen out of the AI ecosystem, which is an extremely rational strategy for them -- let's make life really hard for our competitors. We'll see the first models trained on Blackwell in early 2026. I think the first Blackwell model will come from xAI because they build the data centers the fastest. The GB300 is a great chip. It is drop-in compatible in every way with those GB200 racks, and the companies that use the GB300s -- they're going to be the low cost producer of tokens. I think this has pretty profound implications because it has to change Google's strategic calculus. If you have decisive cost advantage, why not AI at a -30% margin. You make it hard for your competitors who need funding to raise the capital that they need. On the other side of that maybe you have an extremely dominant share position. Well, all of that changes once Google is no longer the low-cost producer."

Patrick OShaughnessy

174,753 次观看 • 8 个月前

🚨 CHINA'S AI PRICE WAR IS BECOMING A NIGHTMARE FOR US AI COMPANIES. DeepSeek just made its 75% API price cut permanent. Days later, Xiaomi slashed its API costs by up to 99% to match it. The biggest threat to OpenAI and Anthropic was never another frontier model. It's developers realizing they can get competitive performance for a fraction of the cost. Now Chinese labs are turning AI into a commodity. Pricing it like electricity. Giving it away to WIN the market. The West is still selling AI like a premium product. China is selling it like electricity...
0:22

🚨 CHINA'S AI PRICE WAR IS BECOMING A NIGHTMARE FOR US AI COMPANIES. DeepSeek just made its 75% API price cut permanent. Days later, Xiaomi slashed its API costs by up to 99% to match it. The biggest threat to OpenAI and Anthropic was never another frontier model. It's developers realizing they can get competitive performance for a fraction of the cost. Now Chinese labs are turning AI into a commodity. Pricing it like electricity. Giving it away to WIN the market. The West is still selling AI like a premium product. China is selling it like electricity...

CryptoGoos

39,046 次观看 • 3 个月前

Why Pioneers Should Brace Up For the Next Months... Pi Network (Pi Network) is set to unlock nearly 775.8 million $PI tokens in the next few months leading up to the end of 2026. This will definitely increase liquid supply in late 2026, amid an ongoing price decline with thin liquidity. While $PI has retraced positively in the past week, the imminent unlocks will provide extra liquidity, which would be a clear positive signal for price and market cap. Now, this is not to say that the unlocks will lead to a price surge. However, Pioneers should be ready for the scheduled wave... Data was obtained from PiScan.
0:30

Why Pioneers Should Brace Up For the Next Months... Pi Network (Pi Network) is set to unlock nearly 775.8 million $PI tokens in the next few months leading up to the end of 2026. This will definitely increase liquid supply in late 2026, amid an ongoing price decline with thin liquidity. While $PI has retraced positively in the past week, the imminent unlocks will provide extra liquidity, which would be a clear positive signal for price and market cap. Now, this is not to say that the unlocks will lead to a price surge. However, Pioneers should be ready for the scheduled wave... Data was obtained from PiScan.

BSCN

52,130 次观看 • 1 个月前

Disney has been raising the prices of items at their theme parks far outpacing inflation - This Mickey Ice Cream Bar cost $2.75 at Disney World in 2009. With inflation, that bar would cost $4.22 today. But the price has more than doubled to $6.50 - Basic Mickey Ears are up 67%. In 2009 Mickey Ears cost $11.99, today they cost $19.99 - And the Mickey Pretzel is up 118%. In 2009 a pretzel cost $3.79, today the price is $8.25 - It's also gotten more expensive to get inside the parks. From 2015 to 2025, Disney World's top tier annual pass nearly doubled from $829 to $1,629. That's a 44% increase after inflation. “Every year I think to myself, well, what's my breaking point?”
1:31

Disney has been raising the prices of items at their theme parks far outpacing inflation - This Mickey Ice Cream Bar cost $2.75 at Disney World in 2009. With inflation, that bar would cost $4.22 today. But the price has more than doubled to $6.50 - Basic Mickey Ears are up 67%. In 2009 Mickey Ears cost $11.99, today they cost $19.99 - And the Mickey Pretzel is up 118%. In 2009 a pretzel cost $3.79, today the price is $8.25 - It's also gotten more expensive to get inside the parks. From 2015 to 2025, Disney World's top tier annual pass nearly doubled from $829 to $1,629. That's a 44% increase after inflation. “Every year I think to myself, well, what's my breaking point?”

Wall Street Apes

565,925 次观看 • 6 个月前

SITUATION EXPLAINED: Model fusion, or combining several frontier models into one system, may be better than any single model, sometimes at a lower price. ⿻ Andrew Trask, senior researcher at Google DeepMind: "Model fusion is basically this idea that, when you combine models, they can give you higher accuracy than any of the individual models that you're using." "Some recent studies are actually showing they're able to deliver such a significant jump in accuracy that it's actually a lower cost option than buying some of the premium models." "So for example, there's a few different companies, showing the higher accuracy claims. So Sakana, OpenRouter are all releasing results in the last year/couple months, and we replicated some of it internally at OpenMined." "Some of the most surprising stuff, though, is that they're reaching past Fable level quality with non-Fable models, and in the case of OpenRouter, at half the price."
4:20

SITUATION EXPLAINED: Model fusion, or combining several frontier models into one system, may be better than any single model, sometimes at a lower price. ⿻ Andrew Trask, senior researcher at Google DeepMind: "Model fusion is basically this idea that, when you combine models, they can give you higher accuracy than any of the individual models that you're using." "Some recent studies are actually showing they're able to deliver such a significant jump in accuracy that it's actually a lower cost option than buying some of the premium models." "So for example, there's a few different companies, showing the higher accuracy claims. So Sakana, OpenRouter are all releasing results in the last year/couple months, and we replicated some of it internally at OpenMined." "Some of the most surprising stuff, though, is that they're reaching past Fable level quality with non-Fable models, and in the case of OpenRouter, at half the price."

MTS

20,175 次观看 • 2 个月前

Chinese models are 112x cheaper than Anthropic per million tokens. Chamath laid it out on CNBC: a "barrel of intelligence" costs $56 from Anthropic, $26 from OpenAI, $1.50 from Meta, $1 from xAI and Google, and $0.50 from Chinese models. That is not a pricing quirk. That is the steepest commodity curve any technology has run in recorded history. Oil took 40 years to compress like this. Semiconductors took 20. AI inference is doing it in months. The companies sitting at $26 and $56 are not stupid. They're buying time… betting that trust, safety, and enterprise contracts hold the premium long enough for costs to catch up. What they cannot bet on is the timeline. Because the $0.50 model is not a demo. I've been inside the labs building it.
0:34

Chinese models are 112x cheaper than Anthropic per million tokens. Chamath laid it out on CNBC: a "barrel of intelligence" costs $56 from Anthropic, $26 from OpenAI, $1.50 from Meta, $1 from xAI and Google, and $0.50 from Chinese models. That is not a pricing quirk. That is the steepest commodity curve any technology has run in recorded history. Oil took 40 years to compress like this. Semiconductors took 20. AI inference is doing it in months. The companies sitting at $26 and $56 are not stupid. They're buying time… betting that trust, safety, and enterprise contracts hold the premium long enough for costs to catch up. What they cannot bet on is the timeline. Because the $0.50 model is not a demo. I've been inside the labs building it.

Shruti

839,457 次观看 • 1 个月前

Chinese models are 112x cheaper than Anthropic per million tokens. Chamath laid it out on CNBC: a "barrel of intelligence" costs $56 from Anthropic, $26 from OpenAI, $1.50 from Meta, $1 from xAI and Google, and $0.50 from Chinese models. That is not a pricing quirk. That is the steepest commodity curve any technology has run in recorded history. Oil took 40 years to compress like this. Semiconductors took 20. AI inference is doing it in months. The companies sitting at $26 and $56 are not stupid. They're buying time… betting that trust, safety, and enterprise contracts hold the premium long enough for costs to catch up. What they cannot bet on is the timeline. Because the $0.50 model is not a demo. I've been inside the labs building it.
0:34

Chinese models are 112x cheaper than Anthropic per million tokens. Chamath laid it out on CNBC: a "barrel of intelligence" costs $56 from Anthropic, $26 from OpenAI, $1.50 from Meta, $1 from xAI and Google, and $0.50 from Chinese models. That is not a pricing quirk. That is the steepest commodity curve any technology has run in recorded history. Oil took 40 years to compress like this. Semiconductors took 20. AI inference is doing it in months. The companies sitting at $26 and $56 are not stupid. They're buying time… betting that trust, safety, and enterprise contracts hold the premium long enough for costs to catch up. What they cannot bet on is the timeline. Because the $0.50 model is not a demo. I've been inside the labs building it.

Clara Bennett

94,743 次观看 • 1 个月前

This is one of the craziest AI launches of 2026 and it came out of basically nowhere (Save this). A company called Subquadratic just shipped SubQ, and the benchmarks are almost hard to believe. To understand why this is such a big deal, you have to understand the fundamental problem that has defined AI for the last decade. Every large language model in existence is built on transformer architecture, and transformers use a mechanism called standard attention that checks every single word in a sequence against every other word. Double the context length and compute doesn't double, it quadruples, triple it and compute goes up nine times. This quadratic scaling is why frontier models have been stuck at roughly 1 million tokens, why running them at those lengths gets expensive fast, and why the AI labs have essentially been printing money charging you more the longer you need the model to think. The industry has known this problem existed since 2017 but they scaled it anyway. SubQ is built from the ground up to solve it. Instead of processing every possible token relationship, SubQ's sparse attention architecture identifies which relationships actually matter and ignores the rest meaning compute is used where it counts and wasted nowhere else. The result is that compute scales linearly with context length instead of exponentially, and the implications of that one architectural shift are enormous. At 12 million tokens, SubQ reduces attention compute by nearly 1,000x compared to standard frontier models and at 1 million tokens, it runs 52x faster than FlashAttention. And it does all of this while posting frontier level accuracy, scoring 95% on the RULER 128K long-context benchmark versus Claude Opus 4.6's 94.8%, and an 81.8 on SWE-Bench Verified coding tasks, besting Opus 4.6 (80.8) and DeepSeek 4.0 Pro. The cost comparison is where it gets genuinely insane. SubQ runs at under $1.50 per million tokens less than 5% of what Claude Opus charges. On the RULER benchmark, running the test with SubQ cost $8, running the same test with Claude Opus cost $2,600 and that's a 300x cost reduction at equivalent or better accuracy.. Subquadratic launched with $29 million in funding, SubQ is available today for early access via API, and SubQ Code, a coding agent built on the architecture ships alongside it. The transformer has been the unchallenged foundation of every major AI system since 2017. SubQ is the first serious evidence that something structurally better might have just arrived.
1:24

This is one of the craziest AI launches of 2026 and it came out of basically nowhere (Save this). A company called Subquadratic just shipped SubQ, and the benchmarks are almost hard to believe. To understand why this is such a big deal, you have to understand the fundamental problem that has defined AI for the last decade. Every large language model in existence is built on transformer architecture, and transformers use a mechanism called standard attention that checks every single word in a sequence against every other word. Double the context length and compute doesn't double, it quadruples, triple it and compute goes up nine times. This quadratic scaling is why frontier models have been stuck at roughly 1 million tokens, why running them at those lengths gets expensive fast, and why the AI labs have essentially been printing money charging you more the longer you need the model to think. The industry has known this problem existed since 2017 but they scaled it anyway. SubQ is built from the ground up to solve it. Instead of processing every possible token relationship, SubQ's sparse attention architecture identifies which relationships actually matter and ignores the rest meaning compute is used where it counts and wasted nowhere else. The result is that compute scales linearly with context length instead of exponentially, and the implications of that one architectural shift are enormous. At 12 million tokens, SubQ reduces attention compute by nearly 1,000x compared to standard frontier models and at 1 million tokens, it runs 52x faster than FlashAttention. And it does all of this while posting frontier level accuracy, scoring 95% on the RULER 128K long-context benchmark versus Claude Opus 4.6's 94.8%, and an 81.8 on SWE-Bench Verified coding tasks, besting Opus 4.6 (80.8) and DeepSeek 4.0 Pro. The cost comparison is where it gets genuinely insane. SubQ runs at under $1.50 per million tokens less than 5% of what Claude Opus charges. On the RULER benchmark, running the test with SubQ cost $8, running the same test with Claude Opus cost $2,600 and that's a 300x cost reduction at equivalent or better accuracy.. Subquadratic launched with $29 million in funding, SubQ is available today for early access via API, and SubQ Code, a coding agent built on the architecture ships alongside it. The transformer has been the unchallenged foundation of every major AI system since 2017. SubQ is the first serious evidence that something structurally better might have just arrived.

Milk Road AI

278,367 次观看 • 3 个月前

Our most intelligent workhorse model yet for coding and agents has arrived ⚡ Meet Gemini 3.7 Flash. — Crush that seemingly endless to-do list. Gemini Spark in the Google Gemini now uses 3.7 Flash. The new model can equip your personal AI agent to work even smarter for you by seamlessly handling complex, multi-step tasks across your Google Workspace apps like Gmail, Google Calendar and Google Docs — Enjoy a smoother build experience. The model thinks more diligently, putting more effort into multi-step planning and tool calls. A more disciplined execution means less manual oversight and fewer retries across engineering workflows — Build more, spend less. 3.7 Flash is available through the end of the year with an introductory price of half the original 3.6 Flash cost per million tokens ($0.75/1M input tokens and $3.75/1M output tokens)
1:15

Our most intelligent workhorse model yet for coding and agents has arrived ⚡ Meet Gemini 3.7 Flash. — Crush that seemingly endless to-do list. Gemini Spark in the Google Gemini now uses 3.7 Flash. The new model can equip your personal AI agent to work even smarter for you by seamlessly handling complex, multi-step tasks across your Google Workspace apps like Gmail, Google Calendar and Google Docs — Enjoy a smoother build experience. The model thinks more diligently, putting more effort into multi-step planning and tool calls. A more disciplined execution means less manual oversight and fewer retries across engineering workflows — Build more, spend less. 3.7 Flash is available through the end of the year with an introductory price of half the original 3.6 Flash cost per million tokens ($0.75/1M input tokens and $3.75/1M output tokens)

Google AI

126,843 次观看 • 17 天前

China is once again unsettling its American competitors in the AI race. Chinese labs have returned to the spotlight with the unveiling of GLM 5.2, described as the most powerful AI model ever trained in China — and one of the leading open‑source models worldwide. 🔍 Key highlights of “GLM 5.2”: ♦️ It is considered the strongest AI model developed in China to date, and ranks among the smartest open‑source models available. ♦️ It operates at a cost less than one‑tenth that of Anthropic’s latest models, making it a far more economical option. ♦️ It is likely to attract developers and companies seeking powerful, low‑cost models — especially amid growing technical and geopolitical restrictions. ♦️ Some evaluations place the model among the top globally, trailing OpenAI’s models while outperforming several competitors in specialized benchmarks and performance tests.
0:42

China is once again unsettling its American competitors in the AI race. Chinese labs have returned to the spotlight with the unveiling of GLM 5.2, described as the most powerful AI model ever trained in China — and one of the leading open‑source models worldwide. 🔍 Key highlights of “GLM 5.2”: ♦️ It is considered the strongest AI model developed in China to date, and ranks among the smartest open‑source models available. ♦️ It operates at a cost less than one‑tenth that of Anthropic’s latest models, making it a far more economical option. ♦️ It is likely to attract developers and companies seeking powerful, low‑cost models — especially amid growing technical and geopolitical restrictions. ♦️ Some evaluations place the model among the top globally, trailing OpenAI’s models while outperforming several competitors in specialized benchmarks and performance tests.

China pulse 🇨🇳

14,293 次观看 • 2 个月前

Key fact claims for anyone watching the AI boom/bubble! They may contradict the Bridgewater AI slides I posted recently. Ed Zitron According to Dylan, both OpenAI and Anthropic have shifted from being venture-funded loss-making companies to generating massive positive gross margins (01:39 - 02:37). Key points regarding their profitability per gigawatt include: Revenue vs. Cost: While the base cost of compute is roughly $10 million to $15 million per megawatt, these labs have significantly increased their ability to monetize that capacity (02:29 - 03:00). Margin Improvement: OpenAI’s newer models (like 5.6) and Anthropic’s models (such as Opus 5 or Mythos) have pushed revenue generation well beyond the incremental cost of compute (02:54 - 03:02). Monetization Scaling: In the case of Anthropic, revenue has reached as high as $50 million per megawatt, allowing them to take the profit from that inference capacity and reinvest it heavily into training (03:07 - 03:23). Listen further for incredible projections: OAI + Ant will control most of the computing power on Earth within next few years?
1:46

Key fact claims for anyone watching the AI boom/bubble! They may contradict the Bridgewater AI slides I posted recently. Ed Zitron According to Dylan, both OpenAI and Anthropic have shifted from being venture-funded loss-making companies to generating massive positive gross margins (01:39 - 02:37). Key points regarding their profitability per gigawatt include: Revenue vs. Cost: While the base cost of compute is roughly $10 million to $15 million per megawatt, these labs have significantly increased their ability to monetize that capacity (02:29 - 03:00). Margin Improvement: OpenAI’s newer models (like 5.6) and Anthropic’s models (such as Opus 5 or Mythos) have pushed revenue generation well beyond the incremental cost of compute (02:54 - 03:02). Monetization Scaling: In the case of Anthropic, revenue has reached as high as $50 million per megawatt, allowing them to take the profit from that inference capacity and reinvest it heavily into training (03:07 - 03:23). Listen further for incredible projections: OAI + Ant will control most of the computing power on Earth within next few years?

steve hsu

14,109 次观看 • 4 天前

SITUATION EXPLAINED: Kimi K3 just beat Fable on a major coding benchmark, at a fraction of the price. • Kimi K3 beat Fable on Frontend Code Arena • The gap between Kimi and Fable is bigger than the gap between Fable and Sol • It also smashes Opus outright, while being priced the same as Sonnet, $3/$15 per million tokens • Sonnet 5 High currently sits in 10th place on this leaderboard at that same price point • The generational jump is dramatic: Kimi K2.6 ranked 18th on this benchmark, K3 jumped 17 spots to take the top spot • K3 ranked #1 in 6 of 7 tested domains: brand and marketing, reference-based design, data and analytics, consumer products, simulations, and content creation tools, it placed #2, only in gaming, behind Fable • Full model weights are set to release July 27th Theo Jaffee: "Imagine if Anthropic were a public company today, their stock would be down 20%, maybe more, enterprises are probably not gonna be spending $10 and $50 on Fable when they can instead be spending $3 and $15 on Kimi K3."
3:21

SITUATION EXPLAINED: Kimi K3 just beat Fable on a major coding benchmark, at a fraction of the price. • Kimi K3 beat Fable on Frontend Code Arena • The gap between Kimi and Fable is bigger than the gap between Fable and Sol • It also smashes Opus outright, while being priced the same as Sonnet, $3/$15 per million tokens • Sonnet 5 High currently sits in 10th place on this leaderboard at that same price point • The generational jump is dramatic: Kimi K2.6 ranked 18th on this benchmark, K3 jumped 17 spots to take the top spot • K3 ranked #1 in 6 of 7 tested domains: brand and marketing, reference-based design, data and analytics, consumer products, simulations, and content creation tools, it placed #2, only in gaming, behind Fable • Full model weights are set to release July 27th Theo Jaffee: "Imagine if Anthropic were a public company today, their stock would be down 20%, maybe more, enterprises are probably not gonna be spending $10 and $50 on Fable when they can instead be spending $3 and $15 on Kimi K3."

MTS

132,705 次观看 • 1 个月前

From 2021–2024, inflation under Biden peaked at 8% in June 2022 and stayed above 5% for two straight years. By the end of his term, overall prices were about 17% higher than in 2020. Mortgage rates rose from around 3% in 2020 to more than 7% in 2023, which nearly doubled the average monthly payment on a new home. Groceries increased by 20–25%, gas prices topped $5 per gallon in many places, and household savings dropped from $2.3 trillion in 2021 to under $400 billion by 2024. During this time, wages grew about 4% a year, but because prices rose faster, real earnings fell for more than two years. In 2025, inflation has slowed to 2.9%, close to the Federal Reserve’s target. Wages are rising 3.5–4%, now ahead of inflation. The unemployment rate is 4.3%, which remains low by historical standards, and GDP is projected to grow about 1.9% this year. The S&P 500 is up about 15% year-to-date, consumer confidence has improved from 2022 lows, and energy prices are below their 2022 peak. Prices for food, housing, and fuel are still higher than before 2020, but the pace of increases has stabilized. So yeah, I’d say the economy’s on the right track under President Trump.
0:59

From 2021–2024, inflation under Biden peaked at 8% in June 2022 and stayed above 5% for two straight years. By the end of his term, overall prices were about 17% higher than in 2020. Mortgage rates rose from around 3% in 2020 to more than 7% in 2023, which nearly doubled the average monthly payment on a new home. Groceries increased by 20–25%, gas prices topped $5 per gallon in many places, and household savings dropped from $2.3 trillion in 2021 to under $400 billion by 2024. During this time, wages grew about 4% a year, but because prices rose faster, real earnings fell for more than two years. In 2025, inflation has slowed to 2.9%, close to the Federal Reserve’s target. Wages are rising 3.5–4%, now ahead of inflation. The unemployment rate is 4.3%, which remains low by historical standards, and GDP is projected to grow about 1.9% this year. The S&P 500 is up about 15% year-to-date, consumer confidence has improved from 2022 lows, and energy prices are below their 2022 peak. Prices for food, housing, and fuel are still higher than before 2020, but the pace of increases has stabilized. So yeah, I’d say the economy’s on the right track under President Trump.

Dan Crenshaw

48,554 次观看 • 10 个月前