जेमिनी 3.6 फ्लॅश
Previous Flash generation; strong general-purpose multimodal model.
लाँच दिनांक: 21 जुलै, 2026
₹396.00
प्रति 10 लाख आउटपुट Tokens
इनपुट: ₹79.20 प्रति 10 लाख Tokens
प्रोव्हायडरच्या मूळ दरानुसार 0% मार्कअपसह प्रति US डॉलर ₹96 दराने रूपांतरित।
वैशिष्ट्ये
- Context विंडो
- 10,48,576 Tokens
- कमाल आउटपुट
- 65,536 Tokens
- स्वीकारतो
- टेक्स्ट, इमेजेस, PDF फाइल्स, ऑडिओ, व्हिडिओ
- Reasoning
- बाय डीफॉल्ट चालू
- Effort लेव्हल्स
- minimal, low, medium, high
- टूल वापर
- होय
- स्ट्रक्चर्ड आउटपुट
- होय
- Code एक्झिक्यूशन
- होय
- इंटेलिजन्स रँक
- 54 पैकी #28
- व्हॅल्यू रँक
- 54 पैकी #17
दरपत्रक
बेंचमार्क
रेटिंग म्हणून नमूद केलेले नसल्यास स्कोअर टक्केवारीत आहेत. सर्व बेंचमार्क स्वतंत्रपणे तपासले जातात.
92.8%
GPQA Diamond
GPQA Diamond - graduate-level science Q&A
40.8%
HLE
Humanity's Last Exam
53.4%
SciCode
SciCode - scientific code generation
80.0%
Long Context
Long Context Reasoning - reasoning over long inputs
77.5%
Terminal-Bench 2
Terminal-Bench 2.1 - agentic terminal tasks, second edition
34.4%
FrontierCode
FrontierCode - long-horizon production coding tasks
60.4%
ARC-AGI-2
ARC-AGI-2 - abstract reasoning on novel puzzles
1539
WebDev Arena
WebDev Arena - head-to-head web-app builds, Elo rating
जेमिनी 3.6 फ्लॅश विषयी
A high-efficiency Gemini providing sustained frontier-level intelligence for real-world tasks at higher speed and lower cost. Google built it for the agentic era, and it excels at code generation, agentic execution and spatial reasoning while using about 17 percent fewer output tokens than the previous Flash generation at better quality. Same four input modalities and million-token context as 3.7 Flash.
Google released 3.6 Flash as the workhorse of a three-model announcement: the Flash tier for general production work, a cheaper 3.5 Flash-Lite underneath it for throughput, and a restricted 3.5 Flash Cyber for vulnerability work that Google keeps to governments and trusted partners. Its stated design goal was not a higher peak score but a lower cost per completed agentic task, which is a different thing from a lower price per token.
That is why the efficiency claim leads the post. Google reports the model spending fewer output tokens than 3.5 Flash for better answers, and taking fewer reasoning steps and tool calls to finish a multi-step workflow, while charging less per token than the model it replaced. A cheaper token that is spent twice as often is not cheaper, and the lab measures the difference at the task level.
On capability the lab reports higher precision with fewer unwanted code edits and fewer execution loops on DeepSWE, at 49% against 37% for 3.5 Flash; a large jump in machine learning research work on MLE Bench, at 63.9% against 49.7%; better computer use on OSWorld-Verified, at 83.0% against 78.4%; and better knowledge work on the GDPval-AA v2 Elo, at 1421 against 1349. Computer use also became a built-in client-side tool with this release rather than something the caller has to assemble.
The multimodal side is where Google points customers with document workloads: parsing documents, reading charts and data, and drafting reports off them. It ships with strengthened safeguards in the chemical, biological, radiological, nuclear and cyber-offence domains, which Google says make it substantially harder to jailbreak while being trained to refuse less on legitimate requests.
The honest caveat is the calendar. Gemini 3.7 Flash arrived three weeks later at an introductory price Google set at half of 3.6 Flash's original rate, and the lab's own comparison puts it ahead of 3.6 Flash on every coding, document and workflow row it publishes. 3.6 Flash remains a reasonable choice where a workload has already been tuned against it, not where a new one is being started.
लाँचवेळी Google ने काय सांगितले
- Cost per task, not per token
- Google reports fewer output tokens than 3.5 Flash for better quality, and fewer reasoning steps and tool calls per multi-step workflow, at a lower price per token than the model it replaced.
- Cleaner code edits
- On DeepSWE the lab reports 49% against 37% for 3.5 Flash, attributing the gain to higher precision, fewer unwanted edits and fewer execution loops rather than to more attempts.
- Research and computer use
- Google reports 63.9% against 49.7% on MLE Bench for machine learning research work, and 83.0% against 78.4% on OSWorld-Verified, with computer use now a built-in client-side tool.
- Document and chart work
- The lab points customers with document workloads here: parsing documents, analysing charts and data, and drafting reports from them, with knowledge work measured at 1421 Elo against 1349.
- Jailbreak resistance
- It ships with strengthened CBRN and cyber-offence safeguards that Google says make it substantially more resistant to jailbreaks, while being trained to refuse fewer beneficial requests.
- What it is not for
- Gemini 3.7 Flash followed three weeks later at the same introductory rate, and Google's own figures put it ahead on every coding, document and workflow row. New work belongs on the newer model.
भारतीय भाषा
जेमिनी 3.6 फ्लॅश हे 15 भारतीय भाषांमध्ये उत्तरे देते। मेसेज बॉक्ससमोरील मेनूमधून भाषा निवडा आणि त्याच भाषेत उत्तर मिळवा।
Frequently Asked Questions
जेमिनी 3.6 फ्लॅश बद्दल वारंवार विचारले जाणारे प्रश्न।
जेमिनी 3.6 फ्लॅश कधी लाँच झाले?
Google ने जेमिनी 3.6 फ्लॅश मॉडेल 21 जुलै, 2026 रोजी लाँच केले.
जेमिनी 3.6 फ्लॅश ची निर्मिती कोणी केली?
जेमिनी 3.6 फ्लॅश ची निर्मिती Google ने केली आहे। 99Models प्रोव्हायडरच्या मूळ दरात थेट तिच्याशी जोडते।
जेमिनी 3.6 फ्लॅश किती कार्यक्षम आहे?
स्वतंत्र बेंचमार्क गुणांवर आधारित आमच्या बुद्धिमत्ता रँकिंगमध्ये 54 पैकी या Model चा क्रमांक 28 आहे। तिचे सर्व गुण वरील Benchmarks तक्त्यामध्ये पाहू शकता।
जेमिनी 3.6 फ्लॅश चे दर किती आहेत?
0% मार्कअपसह दर प्रति 10 लाख इनपुट Tokens साठी ₹79.20 आणि प्रति 10 लाख आउटपुट Tokens साठी ₹396.00 आहे। कोणतेही सबस्क्रिप्शन नाही; तुम्ही वापरानुसार पेमेंट करता।
जेमिनी 3.6 फ्लॅश चे अमेरिकन डॉलरमधील दर काय आहेत?
प्रोव्हायडर प्रति 10 लाख इनपुट Tokens साठी $0.83 आणि प्रति 10 लाख आउटपुट Tokens साठी $4.13 आकारतो। रुपयांचे दर प्रति अमेरिकन डॉलर ₹96 या दराने रूपांतरित केले आहेत।
जेमिनी 3.6 फ्लॅश किती मोठे संभाषण लक्षात ठेवू शकते?
याची Context विंडो 10.5 lakh Tokens आहे। एकाच विनंतीमध्ये हे Model संभाषण आणि जोडलेल्या फाइल्स मिळून एवढा एकूण मजकूर वाचू शकते।
मूल्याच्या (Value) बाबतीत जेमिनी 3.6 फ्लॅश चा क्रमांक कितवा आहे?
मूल्य रँकिंगमध्ये 54 मॉडेलपैकी हिचा क्रमांक 17 आहे। हे रँकिंग मॉडेलची बुद्धिमत्ता आणि Tokens च्या किमतीची तुलना करून ठरवले जाते।
जेमिनी 3.6 फ्लॅश उत्तर देण्यापूर्वी विचार (Reasoning) करते का?
बाय डीफॉल्ट चालू। Reasoning उपलब्ध असल्यास, तुम्ही मेसेज कंपोजरमध्ये विचार करण्याची पातळी (effort level) निवडू शकता।
जेमिनी 3.6 फ्लॅश कोणत्या भारतीय भाषांमध्ये उत्तरे देते?
हे 15 भारतीय भाषांमध्ये उत्तरे देते। मेसेज बॉक्ससमोरील मेनूमधून भाषा निवडा आणि त्याच भाषेत उत्तर मिळवा।
Google कडील इतर मॉडेल्स
कॅटलॉग अपडेट: 9 सप्टें, 2026