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Gemini 3.5 Flash is a step forward for Google on speed and agentic capabilities but comes at a trade-off of being higher cost than prior models We have measured up to ~280 output tokens/sec, placing it on the speed/intelligence Pareto frontier and well ahead of Gemini 3 Flash. It... show more

Artificial Analysis

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16,055 次观看 • 4 个月前 •via X (Twitter)

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Our new Gemini 3.5 Flash is our strongest agentic and coding model yet, delivering frontier-level performance at 4x the speed of comparable frontier models at less than half the cost. We had Tulsee Doshi break down what you need to know about it.

Our new Gemini 3.5 Flash is our strongest agentic and coding model yet, delivering frontier-level performance at 4x the speed of comparable frontier models at less than half the cost. We had Tulsee Doshi break down what you need to know about it.

Google

77,799 次观看 • 4 个月前

Antigravity's computer use has also been massively upgraded with Gemini 3 Flash. It is both faster and better at doing long agentic tasks using the browser. Here's Antigravity doing deep research on the Pareto frontier of models and writing code to visualize the result.

Antigravity's computer use has also been massively upgraded with Gemini 3 Flash. It is both faster and better at doing long agentic tasks using the browser. Here's Antigravity doing deep research on the Pareto frontier of models and writing code to visualize the result.

Varun Mohan

157,515 次观看 • 9 个月前

We’re expanding the Gemini 3 family with the launch of Gemini 3 Flash. This model: — Combines Gemini 3’s Pro-grade reasoning with Flash-level latency, efficiency, and cost — Delivers frontier-level performance on PHD-level reasoning and knowledge benchmarks — Is our most impressive model for agentic workflows Watch Gemini 3 Flash handle hundreds of function calling options at low latency, compiling global recipes from 100 ingredients and 100 kitchen tools.

We’re expanding the Gemini 3 family with the launch of Gemini 3 Flash. This model: — Combines Gemini 3’s Pro-grade reasoning with Flash-level latency, efficiency, and cost — Delivers frontier-level performance on PHD-level reasoning and knowledge benchmarks — Is our most impressive model for agentic workflows Watch Gemini 3 Flash handle hundreds of function calling options at low latency, compiling global recipes from 100 ingredients and 100 kitchen tools.

Google AI

229,848 次观看 • 10 个月前

Gemini 3 Flash is here, rolling out now in AI Mode in Search! ⚡️⚡️⚡️ 3 Flash brings frontier model intelligence to everyone...with the speed and accuracy of Google Search. Excited to ship it globally! Try it now at

Gemini 3 Flash is here, rolling out now in AI Mode in Search! ⚡️⚡️⚡️ 3 Flash brings frontier model intelligence to everyone...with the speed and accuracy of Google Search. Excited to ship it globally! Try it now at

Nick Fox

27,504 次观看 • 10 个月前

A nice aspect of our new Gemini 3.6 Flash model is that it is much more token efficient than our 3.5 Flash model. Here's a side-by-side demonstration of that. Nice work by everyone who worked on this model and release!

A nice aspect of our new Gemini 3.6 Flash model is that it is much more token efficient than our 3.5 Flash model. Here's a side-by-side demonstration of that. Nice work by everyone who worked on this model and release!

Jeff Dean

211,927 次观看 • 2 个月前

🆕Google DeepMind's Gemini 3.7 Flash dropped this morning, and our evals showed it scored higher and ran about 37% faster than Gemini 3.6 Flash, with the largest gains on the most challenging analytical tasks. Here's how it performs on one of those tasks: private credit work. In this Box Agent demo, Gemini 3.7 Flash reviews 15 documents in a borrower package, catches an outdated leverage limit, applies the amended 5.50x threshold, corrects for $4M in restricted cash and a $1.5M unsupported add-back, flags a potential covenant exception at 5.58x, and saves a cited review back to Box for the portfolio team. Customers can use Gemini 3.7 Flash in Box AI Studio to build custom agents grounded in their enterprise content.

🆕Google DeepMind's Gemini 3.7 Flash dropped this morning, and our evals showed it scored higher and ran about 37% faster than Gemini 3.6 Flash, with the largest gains on the most challenging analytical tasks. Here's how it performs on one of those tasks: private credit work. In this Box Agent demo, Gemini 3.7 Flash reviews 15 documents in a borrower package, catches an outdated leverage limit, applies the amended 5.50x threshold, corrects for $4M in restricted cash and a $1.5M unsupported add-back, flags a potential covenant exception at 5.58x, and saves a cited review back to Box for the portfolio team. Customers can use Gemini 3.7 Flash in Box AI Studio to build custom agents grounded in their enterprise content.

Box

3,817,973 次观看 • 2 个月前

Today, we’re launching Agentic Video Understanding in Gemini across 3.7 Flash, 3.6 Flash, and 3.5 Flash-Lite. 📉 ~88% fewer tokens 💰 ~66% lower cost 📈 ~7% higher accuracy The result: a new Pareto frontier for video understanding across accuracy and cost. Traditional video understanding processes video statically at a fixed sampling rate. With Agentic Video Understanding, Gemini can actively decide what to watch, at what speed, and which signals to use (frames, audio, or transcripts) by using native video tools. This enables split-second retrieval, rapid-motion understanding, and needle-in-a-haystack search across hours of video. Available now via the Gemini API - and coming soon to billions of users through the Gemini App and YouTube’s “Ask YouTube” experience. Learn more (details, demos, and docs): PMing this 0→1 has been a career highlight. Taking it from an idea and helping shape it into a frontier-defining capability we’re now shipping broadly has been incredibly rewarding. Huge thanks to Mario Lucic , Sergi Caelles Ahmet Iscen , Filip Pavetić, Suhas Yogin , JB Alayrac, Ben Caine, Nicole Brichtova, Bibo Xu , Tulsee Doshi, David Thacker, koray kavukcuoglu and the multimodal team at Google DeepMind.

Today, we’re launching Agentic Video Understanding in Gemini across 3.7 Flash, 3.6 Flash, and 3.5 Flash-Lite. 📉 ~88% fewer tokens 💰 ~66% lower cost 📈 ~7% higher accuracy The result: a new Pareto frontier for video understanding across accuracy and cost. Traditional video understanding processes video statically at a fixed sampling rate. With Agentic Video Understanding, Gemini can actively decide what to watch, at what speed, and which signals to use (frames, audio, or transcripts) by using native video tools. This enables split-second retrieval, rapid-motion understanding, and needle-in-a-haystack search across hours of video. Available now via the Gemini API - and coming soon to billions of users through the Gemini App and YouTube’s “Ask YouTube” experience. Learn more (details, demos, and docs): PMing this 0→1 has been a career highlight. Taking it from an idea and helping shape it into a frontier-defining capability we’re now shipping broadly has been incredibly rewarding. Huge thanks to Mario Lucic , Sergi Caelles Ahmet Iscen , Filip Pavetić, Suhas Yogin , JB Alayrac, Ben Caine, Nicole Brichtova, Bibo Xu , Tulsee Doshi, David Thacker, koray kavukcuoglu and the multimodal team at Google DeepMind.

Rohan Doshi

20,825 次观看 • 1 个月前

Try Google Gemini 3 Pro now on Verdent! Enjoy up to 85% OFF usage cost to build more. - 70% off for 30 days: Gemini 3 Pro usage cost in Verdent is 70% OFF. - 2x credits on Verdent subscription: With our limited-time double credits bonus, Gemini 3 Pro costs is 85% OFF of the official price. - Gemini 3 Pro keeps full-project context for real development tasks. Its agentic execution ensures results are correct and shippable. Come build with Google's latest powerful model for coding! Enjoy the Black Friday Sales now.

Try Google Gemini 3 Pro now on Verdent! Enjoy up to 85% OFF usage cost to build more. - 70% off for 30 days: Gemini 3 Pro usage cost in Verdent is 70% OFF. - 2x credits on Verdent subscription: With our limited-time double credits bonus, Gemini 3 Pro costs is 85% OFF of the official price. - Gemini 3 Pro keeps full-project context for real development tasks. Its agentic execution ensures results are correct and shippable. Come build with Google's latest powerful model for coding! Enjoy the Black Friday Sales now.

Verdent

154,352 次观看 • 10 个月前

Following our Gemini 3.5 Flash launch at #GoogleIO, check out this demo of what it can do right inside the Gemini app. Watch 3.5 Flash build an interactive circuit helper, outputting a step-by-step physical build guide alongside a working simulation. It’s a great example of how the Gemini app can help students learn visually, breaking down new or complex concepts and guiding them through hands-on subjects interactively. 3.5 Flash is available globally today!

Following our Gemini 3.5 Flash launch at #GoogleIO, check out this demo of what it can do right inside the Gemini app. Watch 3.5 Flash build an interactive circuit helper, outputting a step-by-step physical build guide alongside a working simulation. It’s a great example of how the Gemini app can help students learn visually, breaking down new or complex concepts and guiding them through hands-on subjects interactively. 3.5 Flash is available globally today!

koray kavukcuoglu

59,837 次观看 • 4 个月前

glm 5.3 vs qwen 3.8 vs gemini 3.7 vs deepseek v4 flash four models designed and built three structures each on a physics-backed site, with no dimensions anywhere in the brief the setup: our own agent loop on OpenRouter, a construction site as the tool set – footings, walls, arches, roofs, scaffold, a lamp. the site enforces physics and nothing else: unsupported brick falls, a roof needs walls under it, a worker reaches 3.2 m above whatever he stands on, an arch needs centring until the keystone is set, concrete cures before it carries. no budget ceiling – material cost is tallied and reported, never blocked. tasks: 1. house – a plot and a palette, no plan. shape, height and material are the model's call 2. lighthouse – a headland cut by a gully, with a rock stack standing 30 m offshore. the lamp must burn, it must be the highest thing built, and the keeper must be able to walk to it 3. bridge – a river with one islet and banks at different heights. cross it however you want models: Z.ai glm 5.3 flash, Qwen qwen 3.8 flash, Google DeepMind gemini 3.7 flash, DeepSeek v4 flash vision all twelve objects were finished and signed off by the models themselves. tallest lighthouse is qwen's at 38.4 m, planted on the offshore stack with a bridge run out to it – the only model that read the site that way. deepseek signed off its bridge on an empty riverbed: 0 bricks, 107 minutes, $1.16m of material tallied - total cost, three builds #1 glm 5.3 flash – $0.201 #2 gemini 3.7 flash – $0.871 #3 qwen 3.8 flash – $1.058 #4 deepseek v4 flash – $1.567 - wall clock, three builds #1 gemini 3.7 flash – 91m #2 glm 5.3 flash – 228m #3 deepseek v4 flash – 502m #4 qwen 3.8 flash – 912m - total tokens #1 gemini 3.7 flash – 3,567,052 #2 glm 5.3 flash – 4,732,748 #3 qwen 3.8 flash – 13,469,333 #4 deepseek v4 flash – 18,230,076 - defects logged by the site #1 deepseek v4 flash – 59 #2 gemini 3.7 flash – 132 #3 glm 5.3 flash – 221 #4 qwen 3.8 flash – 350 - material tallied across three builds #1 gemini 3.7 flash – $359,884 #2 glm 5.3 flash – $583,358 #3 deepseek v4 flash – $1,327,484 #4 qwen 3.8 flash – $2,188,625 observations: • glm is the cheap one and nothing here is close – $0.201 for three buildings, $0.042 per million tokens, 6x under gemini's rate • what glm spends it on is bulk, not care: 166,228 bricks in one house and 156 defect weight, the worst single object in the set • gemini is the efficiency line – 91 minutes and 3.57m tokens for all three and an eighth of qwen's clock • gemini also builds the smallest of everything. its lighthouse is 22.5 m against qwen's 38.4, its house 6.9 m against 19.3 • qwen is the maximalist: 1.18m bricks, $2.19m of material, tallest on all three tasks, and 912 minutes – 15 hours – to get there conclusion: twelve finished objects for $3.80 all in, and a 7.8x price spread between the cheapest model and the priciest! follow thehype. for 24/7 ai news, analysis and breakdowns

glm 5.3 vs qwen 3.8 vs gemini 3.7 vs deepseek v4 flash four models designed and built three structures each on a physics-backed site, with no dimensions anywhere in the brief the setup: our own agent loop on OpenRouter, a construction site as the tool set – footings, walls, arches, roofs, scaffold, a lamp. the site enforces physics and nothing else: unsupported brick falls, a roof needs walls under it, a worker reaches 3.2 m above whatever he stands on, an arch needs centring until the keystone is set, concrete cures before it carries. no budget ceiling – material cost is tallied and reported, never blocked. tasks: 1. house – a plot and a palette, no plan. shape, height and material are the model's call 2. lighthouse – a headland cut by a gully, with a rock stack standing 30 m offshore. the lamp must burn, it must be the highest thing built, and the keeper must be able to walk to it 3. bridge – a river with one islet and banks at different heights. cross it however you want models: Z.ai glm 5.3 flash, Qwen qwen 3.8 flash, Google DeepMind gemini 3.7 flash, DeepSeek v4 flash vision all twelve objects were finished and signed off by the models themselves. tallest lighthouse is qwen's at 38.4 m, planted on the offshore stack with a bridge run out to it – the only model that read the site that way. deepseek signed off its bridge on an empty riverbed: 0 bricks, 107 minutes, $1.16m of material tallied - total cost, three builds #1 glm 5.3 flash – $0.201 #2 gemini 3.7 flash – $0.871 #3 qwen 3.8 flash – $1.058 #4 deepseek v4 flash – $1.567 - wall clock, three builds #1 gemini 3.7 flash – 91m #2 glm 5.3 flash – 228m #3 deepseek v4 flash – 502m #4 qwen 3.8 flash – 912m - total tokens #1 gemini 3.7 flash – 3,567,052 #2 glm 5.3 flash – 4,732,748 #3 qwen 3.8 flash – 13,469,333 #4 deepseek v4 flash – 18,230,076 - defects logged by the site #1 deepseek v4 flash – 59 #2 gemini 3.7 flash – 132 #3 glm 5.3 flash – 221 #4 qwen 3.8 flash – 350 - material tallied across three builds #1 gemini 3.7 flash – $359,884 #2 glm 5.3 flash – $583,358 #3 deepseek v4 flash – $1,327,484 #4 qwen 3.8 flash – $2,188,625 observations: • glm is the cheap one and nothing here is close – $0.201 for three buildings, $0.042 per million tokens, 6x under gemini's rate • what glm spends it on is bulk, not care: 166,228 bricks in one house and 156 defect weight, the worst single object in the set • gemini is the efficiency line – 91 minutes and 3.57m tokens for all three and an eighth of qwen's clock • gemini also builds the smallest of everything. its lighthouse is 22.5 m against qwen's 38.4, its house 6.9 m against 19.3 • qwen is the maximalist: 1.18m bricks, $2.19m of material, tallest on all three tasks, and 912 minutes – 15 hours – to get there conclusion: twelve finished objects for $3.80 all in, and a 7.8x price spread between the cheapest model and the priciest! follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

26,360 次观看 • 1 个月前

DeepSeek V4.1 Flash is a very interesting model. I just tested it on the BridgeBench lava lamp test and it took LONGER to complete than Fable 5.1 and GPT 6 Astra. It ran at 344 toks/sec, spent 23.5M tokens with a cache hit rate of 99.7%, and cost $0.33. Even though it runs so fast it ends up completing tasks very slowly. The lava lamp that it produced was better than Muse Spark 1.3 and Gemini 3.8 Flash.

DeepSeek V4.1 Flash is a very interesting model. I just tested it on the BridgeBench lava lamp test and it took LONGER to complete than Fable 5.1 and GPT 6 Astra. It ran at 344 toks/sec, spent 23.5M tokens with a cache hit rate of 99.7%, and cost $0.33. Even though it runs so fast it ends up completing tasks very slowly. The lava lamp that it produced was better than Muse Spark 1.3 and Gemini 3.8 Flash.

BridgeMind

128,202 次观看 • 1 个月前

glm 5.3 flash is 7.5x cheaper, but 3.4x slower than gemini 3.7 flash Z.ai glm 5.3 flash – shipped aug 26, $0.07/$0.25 per 1m Google DeepMind gemini 3.7 flash – shipped aug 13, $0.38/$1.88 per 1m we put the two models on one job: write one html file that draws an animated 3d scene in the browser. no images, no downloads, and it has to look the same on every load. the setup: three scenes – a glass aquarium in a lit room, the solar system, a night city under a thunderstorm. identical brief word for word, reasoning effort high, 64k output cap. the numbers below are not the whole run. they cover the three scenes we kept – the best one per task from each model, the ones in the video. - total generation time for the three scenes #1 gemini 3.7 flash – 10m 36s #2 glm 5.3 flash – 36m 30s - tokens spent on those three scenes #1 glm 5.3 flash – 110k #2 gemini 3.7 flash – 111k - cost of those three scenes #1 glm 5.3 flash – $0.027 #2 gemini 3.7 flash – $0.202 observations: • glm's first 10 attempts: 7 blank pages. it kept inventing short random helpers and forgetting to define one of them. the fix was one line in the brief: use exactly one random helper, named rand(), and don't invent shorthands next to it. next 12 attempts: 11 alive, 0 crashes. • glm spends 66% of its output on reasoning, gemini 57%. that is the whole speed gap. • gemini's storm came back as a black rectangle in 4 of 6 runs. glm's best storm has a branching bolt, lit rain and wet asphalt – for $0.01. conclusion: same three scenes, same token spend – glm 5.3 flash billed $0.027 and took 36m 30s, gemini 3.7 flash billed $0.202 and took 10m 36s. glm wins gemini on price and made the best storm of the whole run follow thehype. for 24/7 ai news, analysis and breakdowns

glm 5.3 flash is 7.5x cheaper, but 3.4x slower than gemini 3.7 flash Z.ai glm 5.3 flash – shipped aug 26, $0.07/$0.25 per 1m Google DeepMind gemini 3.7 flash – shipped aug 13, $0.38/$1.88 per 1m we put the two models on one job: write one html file that draws an animated 3d scene in the browser. no images, no downloads, and it has to look the same on every load. the setup: three scenes – a glass aquarium in a lit room, the solar system, a night city under a thunderstorm. identical brief word for word, reasoning effort high, 64k output cap. the numbers below are not the whole run. they cover the three scenes we kept – the best one per task from each model, the ones in the video. - total generation time for the three scenes #1 gemini 3.7 flash – 10m 36s #2 glm 5.3 flash – 36m 30s - tokens spent on those three scenes #1 glm 5.3 flash – 110k #2 gemini 3.7 flash – 111k - cost of those three scenes #1 glm 5.3 flash – $0.027 #2 gemini 3.7 flash – $0.202 observations: • glm's first 10 attempts: 7 blank pages. it kept inventing short random helpers and forgetting to define one of them. the fix was one line in the brief: use exactly one random helper, named rand(), and don't invent shorthands next to it. next 12 attempts: 11 alive, 0 crashes. • glm spends 66% of its output on reasoning, gemini 57%. that is the whole speed gap. • gemini's storm came back as a black rectangle in 4 of 6 runs. glm's best storm has a branching bolt, lit rain and wet asphalt – for $0.01. conclusion: same three scenes, same token spend – glm 5.3 flash billed $0.027 and took 36m 30s, gemini 3.7 flash billed $0.202 and took 10m 36s. glm wins gemini on price and made the best storm of the whole run follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

16,041 次观看 • 1 个月前

Introducing Gemini 3.5 Flash ⚡️ Normally, Gemini 3.5 Flash is 4x faster than other models with frontier performance. For a limited time, Antigravity is serving it 12x faster thanks to custom inference tricks, delivering incredible speed for your workflows. 🚀 See the performance in this demo: generating pixel art from photos, orchestrating multi-agent workflows to write and register sprites, and spawning browser subagents to auto-test rendering: 👇

Introducing Gemini 3.5 Flash ⚡️ Normally, Gemini 3.5 Flash is 4x faster than other models with frontier performance. For a limited time, Antigravity is serving it 12x faster thanks to custom inference tricks, delivering incredible speed for your workflows. 🚀 See the performance in this demo: generating pixel art from photos, orchestrating multi-agent workflows to write and register sprites, and spawning browser subagents to auto-test rendering: 👇

Google Antigravity

132,207 次观看 • 4 个月前

deepseek v4.1 flash ran all four three.js scenes for $0.15. gemini 3.8 flash needed $1.25 we put the two models on one job: a self-contained html page that renders an animated 3d scene with three.js – a storm at sea, a spiral galaxy, a tornado over farmland, a tesla coil on a dark table. same prompt - calls until all four scenes were usable #1 deepseek v4.1 flash – 6 #2 gemini 3.8 flash – 12 - time #1 deepseek v4.1 flash – 13m 57s #2 gemini 3.8 flash – 32m 02s - cost #1 deepseek v4.1 flash – $0.15 #2 gemini 3.8 flash – $1.25 observations: • gemini blew out the exposure on 4 of 5 first attempts – a white blob where the tesla coil should be. the prompt had a paragraph on exposure. it did not help. • both models overcorrect in the fix loop: "too bright" becomes "almost black". round two is where it lands. same four scenes, 6 calls and $0.15 against 12 calls and $1.25!

deepseek v4.1 flash ran all four three.js scenes for $0.15. gemini 3.8 flash needed $1.25 we put the two models on one job: a self-contained html page that renders an animated 3d scene with three.js – a storm at sea, a spiral galaxy, a tornado over farmland, a tesla coil on a dark table. same prompt - calls until all four scenes were usable #1 deepseek v4.1 flash – 6 #2 gemini 3.8 flash – 12 - time #1 deepseek v4.1 flash – 13m 57s #2 gemini 3.8 flash – 32m 02s - cost #1 deepseek v4.1 flash – $0.15 #2 gemini 3.8 flash – $1.25 observations: • gemini blew out the exposure on 4 of 5 first attempts – a white blob where the tesla coil should be. the prompt had a paragraph on exposure. it did not help. • both models overcorrect in the fix loop: "too bright" becomes "almost black". round two is where it lands. same four scenes, 6 calls and $0.15 against 12 calls and $1.25!

thehype.

62,603 次观看 • 1 个月前

Today we're shipping Nemotron 3 Ultra. A 550B MoE frontier-intelligence open model built for long-running agents. It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models.

Today we're shipping Nemotron 3 Ultra. A 550B MoE frontier-intelligence open model built for long-running agents. It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models.

NVIDIA AI

1,330,779 次观看 • 4 个月前

Tired of high token costs for video workflows? Agentic video understanding gives Gemini the tools to dynamically search, scan, and inspect target video segments to achieve its goal. Building on agentic vision, Gemini now actively scans visual frames, audio, and transcripts instead of ingesting video at a static 1 FPS. This reduces token consumption by up to 88%, improves accuracy, and unlocks new ways to process long-form video. Tackle complex video workflows like: ▸ Sub-second retrieval: Catch split-second cuts missed at 1 FPS ▸ Long-form search: Query multi-hour videos without overflowing context ▸ Anomaly detection: Resample interesting windows at higher FPS ▸ Counting: Accurately track repeated actions and objects over time See how agentic video understanding transforms token efficiency in Gemini 3.7 Flash when compared with static video processing:

Tired of high token costs for video workflows? Agentic video understanding gives Gemini the tools to dynamically search, scan, and inspect target video segments to achieve its goal. Building on agentic vision, Gemini now actively scans visual frames, audio, and transcripts instead of ingesting video at a static 1 FPS. This reduces token consumption by up to 88%, improves accuracy, and unlocks new ways to process long-form video. Tackle complex video workflows like: ▸ Sub-second retrieval: Catch split-second cuts missed at 1 FPS ▸ Long-form search: Query multi-hour videos without overflowing context ▸ Anomaly detection: Resample interesting windows at higher FPS ▸ Counting: Accurately track repeated actions and objects over time See how agentic video understanding transforms token efficiency in Gemini 3.7 Flash when compared with static video processing:

Google for Developers

38,369 次观看 • 1 个月前

ox alpha vs deepseek v4 flash vision vs grok 4.6 vs gemini 3.7 flash vs – on photo-to-3d four vision models got one photograph each and had to rebuild the place inside it as a Three.js scene. twelve scenes, twelve first-try runs, zero console errors the setup: one reference photo per scene, sent as an image on OpenRouter. the prompt never says what is in the picture – no "motel", no "bar", no "gas station". the model has to read the photo and rebuild it: layout, materials, hour of the day, and whatever is around the corner that the frame does not show tasks – three photographs of early-2000s america: 1. a motel at night, neon pylon lit, snow on the ground 2. an old new york tavern interior, tin ceiling, tiled floor 3. an abandoned service station in the california desert, midday sun each scene ships as one self-contained html file, procedural geometry and canvas textures only, no downloads. three timed camera shots, and shot 1 has to reproduce the framing of the reference photo models: xAI grok 4.6, Google DeepMind gemini 3.7 flash, DeepSeek deepseek v4 flash vision exp, and ox alpha – a stealth model on openrouter, free, no lab attached to it yet results: - wall clock, three scenes #1 gemini 3.7 flash – 11m 12s #2 deepseek v4 flash – 15m 20s #3 grok 4.6 – 28m 11s #4 ox alpha – 38m 54s - output tokens #1 gemini 3.7 flash – 77,396 #2 ox alpha – 87,613 #3 grok 4.6 – 105,687 #4 deepseek v4 flash – 127,884 - lines of code shipped #1 ox alpha – 2,090 #2 deepseek v4 flash – 2,291 #3 grok 4.6 – 3,529 #4 gemini 3.7 flash – 3,989 - total price #1 ox alpha – $0.000 #2 deepseek v4 flash – $0.091 #3 gemini 3.7 flash – $0.136 #4 grok 4.6 – $0.697 observations: • grok is 7.7x the price of deepseek. it is the only model that read the light – low sun, real shadows on the station, a cold night on the motel • gemini is the fastest and the least deliberate. 17,158 reasoning tokens against deepseek's 99,172, and it still shipped the most code – 3,989 lines • deepseek thought hardest and rendered plainest. 99,172 reasoning tokens, 5.8x gemini's, spent on layout rather than on light. its motel is the second best in the set for $0.030 • ox alpha is free and reads a photo as well as anything here – it lifted "family units / kitchenettes" off the pylon and redrew it in canvas conclusion: twelve scenes, four models, zero fixes, and the whole run cost $0.924! follow thehype. for 24/7 ai news, analysis and breakdowns

ox alpha vs deepseek v4 flash vision vs grok 4.6 vs gemini 3.7 flash vs – on photo-to-3d four vision models got one photograph each and had to rebuild the place inside it as a Three.js scene. twelve scenes, twelve first-try runs, zero console errors the setup: one reference photo per scene, sent as an image on OpenRouter. the prompt never says what is in the picture – no "motel", no "bar", no "gas station". the model has to read the photo and rebuild it: layout, materials, hour of the day, and whatever is around the corner that the frame does not show tasks – three photographs of early-2000s america: 1. a motel at night, neon pylon lit, snow on the ground 2. an old new york tavern interior, tin ceiling, tiled floor 3. an abandoned service station in the california desert, midday sun each scene ships as one self-contained html file, procedural geometry and canvas textures only, no downloads. three timed camera shots, and shot 1 has to reproduce the framing of the reference photo models: xAI grok 4.6, Google DeepMind gemini 3.7 flash, DeepSeek deepseek v4 flash vision exp, and ox alpha – a stealth model on openrouter, free, no lab attached to it yet results: - wall clock, three scenes #1 gemini 3.7 flash – 11m 12s #2 deepseek v4 flash – 15m 20s #3 grok 4.6 – 28m 11s #4 ox alpha – 38m 54s - output tokens #1 gemini 3.7 flash – 77,396 #2 ox alpha – 87,613 #3 grok 4.6 – 105,687 #4 deepseek v4 flash – 127,884 - lines of code shipped #1 ox alpha – 2,090 #2 deepseek v4 flash – 2,291 #3 grok 4.6 – 3,529 #4 gemini 3.7 flash – 3,989 - total price #1 ox alpha – $0.000 #2 deepseek v4 flash – $0.091 #3 gemini 3.7 flash – $0.136 #4 grok 4.6 – $0.697 observations: • grok is 7.7x the price of deepseek. it is the only model that read the light – low sun, real shadows on the station, a cold night on the motel • gemini is the fastest and the least deliberate. 17,158 reasoning tokens against deepseek's 99,172, and it still shipped the most code – 3,989 lines • deepseek thought hardest and rendered plainest. 99,172 reasoning tokens, 5.8x gemini's, spent on layout rather than on light. its motel is the second best in the set for $0.030 • ox alpha is free and reads a photo as well as anything here – it lifted "family units / kitchenettes" off the pylon and redrew it in canvas conclusion: twelve scenes, four models, zero fixes, and the whole run cost $0.924! follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

24,470 次观看 • 1 个月前

DeepSeek V4.1 Flash vs Gemini 3.8 Flash at 3D geometry test: six self-contained three.js stereometry scenes — a (3,8) torus knot, a Klein bottle, a great stellated dodecahedron, a hyperboloid ruled by 120 straight rods, a level-3 Menger sponge, a thick Möbius strip. one shot each, no libraries beyond three.js, every surface painted with a live iridescent rainbow that has to crawl across the geometry. cost: Gemini 3.8 Flash — $0.11 DeepSeek V4.1 Flash — $0.27 • gemini delivers all six figures for 2.4× less — 21k output tokens against deepseek's 168k • gemini is 5× faster — every scene under 25 seconds, deepseek took up to 3 minutes on the Möbius strip • deepseek wins the torus knot: its rope is vivid and glossy, while gemini rendered the same knot nearly black • neither model can build a real Klein bottle. both hand back a twisted tube and call it done try both on aimlapi[.]com

DeepSeek V4.1 Flash vs Gemini 3.8 Flash at 3D geometry test: six self-contained three.js stereometry scenes — a (3,8) torus knot, a Klein bottle, a great stellated dodecahedron, a hyperboloid ruled by 120 straight rods, a level-3 Menger sponge, a thick Möbius strip. one shot each, no libraries beyond three.js, every surface painted with a live iridescent rainbow that has to crawl across the geometry. cost: Gemini 3.8 Flash — $0.11 DeepSeek V4.1 Flash — $0.27 • gemini delivers all six figures for 2.4× less — 21k output tokens against deepseek's 168k • gemini is 5× faster — every scene under 25 seconds, deepseek took up to 3 minutes on the Möbius strip • deepseek wins the torus knot: its rope is vivid and glossy, while gemini rendered the same knot nearly black • neither model can build a real Klein bottle. both hand back a twisted tube and call it done try both on aimlapi[.]com

AI/ML API

39,932 次观看 • 1 个月前

Fast enough to play games ⚡️ We challenged Gemini 3 Flash to a quick drawing game in the G3mini. Because this model is optimized for speed, it can see and guess what you’re drawing while you’re still sketching it.

Fast enough to play games ⚡️ We challenged Gemini 3 Flash to a quick drawing game in the G3mini. Because this model is optimized for speed, it can see and guess what you’re drawing while you’re still sketching it.

Google

119,117 次观看 • 9 个月前

We’re introducing Gemini 3.8 Flash ⚡️ built to tackle complex agentic and multi-step tasks with even greater diligence. Our most intelligent workhorse model yet delivers significant improvements in reasoning, evolving to an AI partner that doesn’t just write code, but can also navigate complex projects. While solving ambiguous and high-friction tasks is immensely helpful, it can also be expensive. Fortunately, 3.8 Flash features the usual effort controls, ensuring that the amount of thinking required to accomplish the task at hand is proportional to the tokens spent. Watch Gemini 3.8 Flash combine native video understanding with advanced coding to autonomously build this 3D game, play it to find errors, and execute code changes in a seamless agentic loop in Google Antigravity.

We’re introducing Gemini 3.8 Flash ⚡️ built to tackle complex agentic and multi-step tasks with even greater diligence. Our most intelligent workhorse model yet delivers significant improvements in reasoning, evolving to an AI partner that doesn’t just write code, but can also navigate complex projects. While solving ambiguous and high-friction tasks is immensely helpful, it can also be expensive. Fortunately, 3.8 Flash features the usual effort controls, ensuring that the amount of thinking required to accomplish the task at hand is proportional to the tokens spent. Watch Gemini 3.8 Flash combine native video understanding with advanced coding to autonomously build this 3D game, play it to find errors, and execute code changes in a seamless agentic loop in Google Antigravity.

Google AI

600,201 次观看 • 1 个月前