-
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Authors:
Gheorghe Comanici,
Eric Bieber,
Mike Schaekermann,
Ice Pasupat,
Noveen Sachdeva,
Inderjit Dhillon,
Marcel Blistein,
Ori Ram,
Dan Zhang,
Evan Rosen,
Luke Marris,
Sam Petulla,
Colin Gaffney,
Asaf Aharoni,
Nathan Lintz,
Tiago Cardal Pais,
Henrik Jacobsson,
Idan Szpektor,
Nan-Jiang Jiang,
Krishna Haridasan,
Ahmed Omran,
Nikunj Saunshi,
Dara Bahri,
Gaurav Mishra,
Eric Chu
, et al. (3284 additional authors not shown)
Abstract:
In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our most capable model yet, achieving SoTA performance on frontier coding and reasoning benchmarks. In addition to its incredible coding and reasoning skills, Gemini 2.5 Pro is a thinking model that excels at multimodal unde…
▽ More
In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our most capable model yet, achieving SoTA performance on frontier coding and reasoning benchmarks. In addition to its incredible coding and reasoning skills, Gemini 2.5 Pro is a thinking model that excels at multimodal understanding and it is now able to process up to 3 hours of video content. Its unique combination of long context, multimodal and reasoning capabilities can be combined to unlock new agentic workflows. Gemini 2.5 Flash provides excellent reasoning abilities at a fraction of the compute and latency requirements and Gemini 2.0 Flash and Flash-Lite provide high performance at low latency and cost. Taken together, the Gemini 2.X model generation spans the full Pareto frontier of model capability vs cost, allowing users to explore the boundaries of what is possible with complex agentic problem solving.
△ Less
Submitted 22 July, 2025; v1 submitted 7 July, 2025;
originally announced July 2025.
-
Gemma 3 Technical Report
Authors:
Gemma Team,
Aishwarya Kamath,
Johan Ferret,
Shreya Pathak,
Nino Vieillard,
Ramona Merhej,
Sarah Perrin,
Tatiana Matejovicova,
Alexandre Ramé,
Morgane Rivière,
Louis Rouillard,
Thomas Mesnard,
Geoffrey Cideron,
Jean-bastien Grill,
Sabela Ramos,
Edouard Yvinec,
Michelle Casbon,
Etienne Pot,
Ivo Penchev,
Gaël Liu,
Francesco Visin,
Kathleen Kenealy,
Lucas Beyer,
Xiaohai Zhai,
Anton Tsitsulin
, et al. (191 additional authors not shown)
Abstract:
We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision understanding abilities, a wider coverage of languages and longer context - at least 128K tokens. We also change the architecture of the model to reduce the KV-cache memory that tends to explode with long context. This is achie…
▽ More
We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision understanding abilities, a wider coverage of languages and longer context - at least 128K tokens. We also change the architecture of the model to reduce the KV-cache memory that tends to explode with long context. This is achieved by increasing the ratio of local to global attention layers, and keeping the span on local attention short. The Gemma 3 models are trained with distillation and achieve superior performance to Gemma 2 for both pre-trained and instruction finetuned versions. In particular, our novel post-training recipe significantly improves the math, chat, instruction-following and multilingual abilities, making Gemma3-4B-IT competitive with Gemma2-27B-IT and Gemma3-27B-IT comparable to Gemini-1.5-Pro across benchmarks. We release all our models to the community.
△ Less
Submitted 25 March, 2025;
originally announced March 2025.
-
ECLeKTic: a Novel Challenge Set for Evaluation of Cross-Lingual Knowledge Transfer
Authors:
Omer Goldman,
Uri Shaham,
Dan Malkin,
Sivan Eiger,
Avinatan Hassidim,
Yossi Matias,
Joshua Maynez,
Adi Mayrav Gilady,
Jason Riesa,
Shruti Rijhwani,
Laura Rimell,
Idan Szpektor,
Reut Tsarfaty,
Matan Eyal
Abstract:
To achieve equitable performance across languages, multilingual large language models (LLMs) must be able to abstract knowledge beyond the language in which it was acquired. However, the current literature lacks reliable ways to measure LLMs' capability of cross-lingual knowledge transfer. To that end, we present ECLeKTic, a multilingual closed-book QA (CBQA) dataset that Evaluates Cross-Lingual K…
▽ More
To achieve equitable performance across languages, multilingual large language models (LLMs) must be able to abstract knowledge beyond the language in which it was acquired. However, the current literature lacks reliable ways to measure LLMs' capability of cross-lingual knowledge transfer. To that end, we present ECLeKTic, a multilingual closed-book QA (CBQA) dataset that Evaluates Cross-Lingual Knowledge Transfer in a simple, black-box manner. We detected information with uneven coverage across languages by controlling for presence and absence of Wikipedia articles in 12 languages. We generated knowledge-seeking questions in a source language, for which the answer appears in a relevant Wikipedia article and translated them to all other 11 languages, for which the respective Wikipedias lack equivalent articles. Assuming that Wikipedia reflects the prominent knowledge in the LLM's training data, to solve ECLeKTic's CBQA task the model is required to transfer knowledge between languages. Experimenting with 8 LLMs, we show that SOTA models struggle to effectively share knowledge across, languages even if they can predict the answer well for queries in the same language the knowledge was acquired in.
△ Less
Submitted 3 March, 2025; v1 submitted 28 February, 2025;
originally announced February 2025.