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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 views • 1 month ago •via X (Twitter)

34 Comments

Google AI's profile picture
Google AI1 month ago

— 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's profile picture
Teneo Protocol1 month ago

@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's profile picture
Vanar1 month ago

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

throwaway_304's profile picture
throwaway_3041 month ago

@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's profile picture
MerkleFlow1 month ago

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

Inspired Taste's profile picture
Inspired Taste1 month ago

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

EllyEleven's profile picture
EllyEleven1 month ago

@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's profile picture
˚ sofi1 month ago

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

Wilson T.'s profile picture
Wilson T.1 month ago

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

Kostya | AI's profile picture
Kostya | AI1 month ago

@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's profile picture
Knowix1 month ago

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

Brian Cheong's profile picture
Brian Cheong1 month ago

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

Anis🐬Al's profile picture
Anis🐬Al1 month ago

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's profile picture
Dmitriy King1 month ago

@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's profile picture
Rudy1 month ago

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

Utkarsh's profile picture
Utkarsh1 month ago

@GeminiApp marketing team did its job well

JOKMAH | Inteligencia causal's profile picture
JOKMAH | Inteligencia causal1 month ago

@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's profile picture
ToolRadarAI1 month ago

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

QuietLayer Studio's profile picture
QuietLayer Studio1 month ago

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

Edward Hernandez's profile picture
Edward Hernandez1 month ago

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

Kostya | AI's profile picture
Kostya | AI1 month ago

@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's profile picture
安叫兽|Bird🕊️ 🔶 BNB1 month ago

@GeminiApp 任务清单能不能少,得先看它会不会自己加活。

sonil's profile picture
sonil1 month ago

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

Inflectiv AI ⧉'s profile picture
Inflectiv AI ⧉1 month ago

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

sof c,'s profile picture
sof c,1 month ago

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

zenramen's profile picture
zenramen1 month ago

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

Sebastian Buzdugan's profile picture
Sebastian Buzdugan1 month ago

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

Miss HR | Technical Recruiter's profile picture
Miss HR | Technical Recruiter1 month ago

@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's profile picture
Namujogo Brenda1 month ago

@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's profile picture
Magica1 month ago

@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's profile picture
Nitish Kumar Yadav1 month ago

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

Deva's profile picture
Deva1 month ago

@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's profile picture
Nina Ledwinka1 month ago

@GeminiApp Our most intelligent workhorse model yet

Andrés's profile picture
Andrés1 month ago

@GeminiApp Nice

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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

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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

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