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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...

129,191 次观看 • 1 个月前 •via X (Twitter)

34 条评论

Google AI 的头像
Google AI1 个月前

— Google AI Pro and Ultra subscribers can experience 3.7 Flash today via Spark in the @GeminiApp — Access the model in the Gemini Enterprise Agent Platform and Gemini Enterprise app — Build in the Gemini API via @googleaistudio and @androidstudio, and explore agent-first workflows in @antigravity — Learn more in the blog ↓

Teneo Protocol 的头像
Teneo Protocol1 个月前

@GeminiApp Cheaper intelligence becomes much more interesting when agents can actually act on it. Better planning, tool use, and lower inference costs could push agents from assistants toward real economic actors.

Vanar 的头像
Vanar1 个月前

@GeminiApp The more capable and affordable agents become, the closer we get to AI that can actually run workflows end to end.

throwaway_304 的头像
throwaway_3041 个月前

@GeminiApp My gf and I tried to use Voice mode earlier today and it was so incredibly bad. Definitely needs an update. Even regular 3.0 was better. We asked it to translate Chinese/English and it randomly spoke Korean and also just said a lot of nonsense.

MerkleFlow 的头像
MerkleFlow1 个月前

@GeminiApp Now that Sergey is back in a more extended capacity - we hope to see Google going back to the frontier race

Inspired Taste 的头像
Inspired Taste1 个月前

@GeminiApp All based and trained on immense amounts of theft…

EllyEleven 的头像
EllyEleven1 个月前

@GeminiApp This can be perfect workhorse model, And usage limits are good to in ai studio or use antigravity This is a perfect model for analysis and large document search. Or just quick easy prototypes.

˚ sofi 的头像
˚ sofi1 个月前

@GeminiApp so the agents can do my to-do list while i scroll twitter? finally.

Wilson T. 的头像
Wilson T.1 个月前

@GeminiApp DeepSWE: 48.6% → 65.3% with $0.75/M input. Hmmm interesting

Kostya | AI 的头像
Kostya | AI1 个月前

@GeminiApp Gemini 3.7 Flash sounds like a notable step for AI‑assisted coding; it'll be interesting to see how its speed and agent capabilities compare to existing tools.

Knowix 的头像
Knowix1 个月前

@GeminiApp developers are eating good with this one, love it!

Brian Cheong 的头像
Brian Cheong1 个月前

@GeminiApp Most agent failures are product failures, not model failures. Scope, permissions, and handoff rules decide more than the benchmark score.

Anis🐬Al 的头像
Anis🐬Al1 个月前

Gemini 3.7 Flash represents a significant step forward in optimizing workflows for coding and complex agent tasks. Integrating this model into the Gemini app suggests a focus on enhancing personal AI productivity. How do you see this specific iteration impacting the speed of agent-led development?

Dmitriy King 的头像
Dmitriy King1 个月前

@GeminiApp AI agents are getting smarter (Gemini 3.7 Flash), now they need a clean chain to run on. Molum — L3 for AI agents, community-owned, no VC. The pack is building. $MOLUM

Rudy 的头像
Rudy1 个月前

@GeminiApp You didn't even mention the best app it works well with. Google keep

Utkarsh 的头像
Utkarsh1 个月前

@GeminiApp marketing team did its job well

JOKMAH | Inteligencia causal 的头像
JOKMAH | Inteligencia causal1 个月前

@GeminiApp “Smartest work model” will be decided outside the launch demo. Give every model the same repo, tools and budget; then report accepted patches, retries, broken tests and human corrections. Speed matters, but cost per verified outcome is the number agents are missing.

ToolRadarAI 的头像
ToolRadarAI1 个月前

@GeminiApp cool. now show tool logs and the invoice.

QuietLayer Studio 的头像
QuietLayer Studio1 个月前

@GeminiApp mate, cost is what matters now!! Heard of DeepSeek?

Edward Hernandez 的头像
Edward Hernandez1 个月前

@GeminiApp Gemini 3.7 Flash? Sounds like my new sidekick, but can it beat my procrastination?

Kostya | AI 的头像
Kostya | AI1 个月前

@GeminiApp Gemini 3.7 Flash sounds promising for coding assistants, but real‑world performance will depend on benchmarks and integration details that aren’t public yet.

安叫兽|Bird🕊️ 🔶 BNB 的头像
安叫兽|Bird🕊️ 🔶 BNB1 个月前

@GeminiApp 任务清单能不能少,得先看它会不会自己加活。

sonil 的头像
sonil1 个月前

@GeminiApp gemini 3.7 flash looking like a solid workhorse for agents

Inflectiv AI ⧉ 的头像
Inflectiv AI ⧉1 个月前

@GeminiApp Better planning and tool use matter more than raw benchmark gains when agents are handling real workflows.

sof c, 的头像
sof c,1 个月前

@GeminiApp Claiming 'most intelligent' without benchmarks is just marketing. How do you measure intelligence in coding-through raw speed, correctness, or adaptability?

zenramen 的头像
zenramen1 个月前

@GeminiApp 3.7 flash is a big jump, wonder how that's gonna play out in coding tasks

Sebastian Buzdugan 的头像
Sebastian Buzdugan1 个月前

@GeminiApp gemini 3.7 flash speed matters less when repo-wide edits still need regression tests

Miss HR | Technical Recruiter 的头像
Miss HR | Technical Recruiter1 个月前

@GeminiApp Impressive progress on agentic AI for real-world workflows, multi-step task handling with less oversight is exactly where the industry's heading.

Namujogo Brenda 的头像
Namujogo Brenda1 个月前

@GeminiApp Exciting advancements! AI models like Gemini 3.7 Flash can really enhance productivity, especially in automating tasks. When integrating such tools, it's essential to consider how they can optimize specific workflows, like content creation and SEO, to get the most out of them.

Magica 的头像
Magica1 个月前

@GeminiApp Paying for separate developer subscriptions every time a new version drops is getting old when Magica just bundles everything for $15.

Nitish Kumar Yadav 的头像
Nitish Kumar Yadav1 个月前

@GeminiApp Faster coding is useful. Fewer broken handoffs during long agent tasks would be the bigger win.

Deva 的头像
Deva1 个月前

@GeminiApp The real bottleneck for coding agents is token density in tool output. Unless the model is actually reasoning over compressed bash history or structured diffs, it just burns context on raw command noise. Ship the agent, but optimize the environment.

Nina Ledwinka 的头像
Nina Ledwinka1 个月前

@GeminiApp Our most intelligent workhorse model yet

Andrés 的头像
Andrés1 个月前

@GeminiApp Nice

相关视频

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

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

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