Gemini 2.0 Flash
Gemini 2.0 Flash is a model from Google, made generally available as gemini-2.0-flash-001 on February 5, 2025, with multimodal input and text output.
Retired. Google’s Gemini API changelog records gemini-2.0-flash as shut down on June 1, 2026.
Released
February 5, 2025
Type
multimodal
License
proprietary
Context
1,000,000 tokens
Google gave it a 1 million-token context window.
Capabilities
Sources
Every figure on this page was checked against these pages on . A figure we could not source is left out.
- Google blog: Gemini model updates, February 5, 2025 (opens in a new tab)
- Gemini API changelog (opens in a new tab)
- Gemini API deprecations (opens in a new tab)
- Gemini API docs: Models (opens in a new tab)
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Links
In the news: Gemini 2.0 Flash and Google DeepMind
MarkTechPost
Google DeepMind Introduces Gemini 4 Carbon, Its Top Model for Cyber Defense and Coding
Google DeepMind has introduced Gemini 4 Carbon, its strongest model to date, designed for long-horizon agentic coding and cybersecurity defense. The model ranks #1 on the updated v5.0 Artificial Analysis Intelligence Index with a score of 59. Access to Gemini 4 Carbon is currently limited to a vetted group of cyber defenders, with no public API availability. Parameter count and context window details have not been disclosed. Unlike its predecessor, Gemini 4 Argon, which has a 1,048,576-token context, Carbon's specifics remain unannounced. Gemini 4 Carbon runs exclusively on Google-hosted infrastructure, with no open weights, self-hosting options, or quantized variants available. It outperforms its predecessor, Gemini 4 Argon, on 15 out of 17 published benchmarks, including GPT-6 Astra, Opus 5.5, and Fable 5.1. The model achieved a 96.2% BFS F1 score on GraphWalks (256K to 1M tokens), significantly higher than Argon's 84.2%. However, it scored 81.5% on OSWorld-2.1, trailing Claude Opus 5.5 at 87.2%. Its key strengths lie in state-of-the-art vulnerability finding, exploitation, and patching. Gemini 4 Carbon is the second model in the Gemini 4 series, following Gemini 4 Argon. Google states the model is engineered for challenging, long-duration tasks, with internal teams observing it running for days on single tasks. These tasks include root-causing and patching subtle bugs in large codebases and low-level systems. Google has also tuned Carbon for full-stack web development and multimodal understanding. On the WebDev Arena, Carbon gained 89 Elo points, reaching #4 with a score of 1769. On Terminal-Bench 4.0 (AA), Carbon scored 65.2% in agentic coding, surpassing Opus 5.5's 59.6%. It achieved 9.6% on SWE-sweep, narrowly ahead of GPT-6 Astra's 9.5%, though GPT-6 Astra leads on FrontierSWE at 65.5% compared to Carbon's 61.2%.
MarkTechPost
Meet Google DeepMind’s Genie 4: Real-Time World Model With Native Audio
Google DeepMind has released Genie 4 as a research preview for trusted testers, a world model capable of generating explorable environments from text and image prompts in real time. Genie 4 also generates a soundscape, including ambient noise, music, and character dialogue, making it DeepMind's first real-time world model to natively produce audio. Users interact with the generated worlds using keyboard controls and steer events with plain-language prompts, with the model's architectural change featuring a Gemini model running inside the simulation loop as a planner. This architectural pattern is noted as important for those building training environments for agents or robots. Genie 4 offers photorealistic worlds with native audio and live text steering, maintaining scene consistency for up to 2 minutes, which is double that of Genie 3. However, sessions typically last only a few minutes, and objects may drift after repeated prompt edits. The model is natively multimodal, generating audio, music, and dialogue concurrently with visuals, succeeding Genie 3, which was announced in August 2025 and ran at 720p and 24 frames per second with visual memory reaching back 1 minute. Genie 4 integrates two parts of the Gemini family: Gemini Omni anchors the world's look and behavior, while other Gemini models manage the reasoning to keep the simulation coherent. The runtime loop involves Gemini interpreting prompts, planning a sequence of steps, verifying frame success, and adapting to world shifts or user interventions, with Gemini also steering the scene between user inputs to ensure progression.
MarkTechPost
Google Releases Gemini 4 Argon API With 1M Token Output Limit
Google DeepMind has made its Gemini 4 Argon API generally available to developers, enterprises, and paid Gemini subscribers. Gemini 4 Argon is a sparse mixture-of-experts frontier model built for long, multi-step work, with target domains including coding, finance, legal, science, and cybersecurity. The model was first announced on September 30 for a select group of cyber defenders and is now accessible via the paid Gemini API at introductory pricing of $2 per input million tokens and $10 per output million tokens. A key enhancement for developers is the increased output length, with Argon capable of generating up to 1 million tokens in a single response, a significant increase from the 64K limit on earlier Gemini models. The model card indicates a combined input and output window of up to 1 million tokens, meaning a lengthy prompt will reduce available output space. Gemini 4 Argon is described as a new, large-scale sparse MoE transformer with native multimodal support, accepting text, images, audio, and video inputs, and producing text outputs. It was trained on TPUs using JAX and ML Pathways, with a knowledge cutoff of March 2026. Google has not disclosed parameter counts. Performance benchmarks show Argon leading Google's internal tables for knowledge work, long context, and video understanding, achieving 91.7% on LVBench. However, it performed less strongly on agentic coding tasks, scoring 55.0% on FrontierSWE v2, placing it last among four models in Google's comparison.
Hugging Face
One Model Family, Two Gold-Level Results: Fine-Tuning Nemotron for IOI and IMO
Google DeepMind's Nemotron model family has achieved gold medal-level performance in two prestigious international competitions: the International Olympiad in Informatics (IOI) and the International Mathematical Olympiad (IMO). This achievement marks a significant milestone for AI in competitive problem-solving, demonstrating advanced capabilities in algorithmic reasoning and mathematical problem-solving. The Nemotron models, specifically fine-tuned for these challenging tasks, were able to tackle complex problems that typically require human-level intellect and strategic thinking. The IOI, which focuses on computer science and algorithmic problem-solving, saw Nemotron excel in areas such as data structures, algorithm design, and computational complexity. The IMO, on the other hand, tested Nemotron's prowess in abstract reasoning, number theory, geometry, and combinatorics. The specific details of the fine-tuning process and the exact Nemotron model variants used were not disclosed in the initial announcement, but the success indicates a sophisticated approach to adapting large language models for specialized domains. This dual success highlights the growing potential of AI systems to not only assist humans but also to compete and excel in fields previously considered exclusive to human cognitive abilities. The development of Nemotron by Google DeepMind, a leading AI research laboratory known for its work on models like AlphaFold and AlphaGo, underscores the company's commitment to pushing the boundaries of artificial intelligence. The ability of these models to achieve top-tier results in academic competitions suggests a deeper understanding and application of logic and reasoning than previously demonstrated by AI. The implications of this development are far-reaching. In education, AI models like Nemotron could serve as powerful learning tools, helping students understand complex concepts and develop problem-solving skills. In research, they could accelerate discovery by assisting scientists and mathematicians in exploring new theories and solutions. However, it also raises important questions about the future of human competition, the definition of intelligence, and the ethical considerations surrounding AI's increasing capabilities in academic and professional arenas. Further analysis of the specific problem sets and Nemotron's solutions will be crucial to fully understand the extent of this AI achievement.
MarkTechPost
Google DeepMind Releases EmbeddingGemma 2, a 740M Open Multimodal Embedding Model Built on Gemma 4
Google DeepMind has released EmbeddingGemma 2, an open-source multimodal embedding model designed to convert text, code, images, video, and audio into a unified 768-dimensional vector space. This model, built upon the Gemma 4 architecture, features 740 million parameters and boasts an 8K token context window, representing a significant upgrade from its predecessor. The release is accompanied by an Apache 2.0 license, making it freely available for use and modification. EmbeddingGemma 2 is engineered for applications such as on-device search, classification tasks, and privacy-first Retrieval Augmented Generation (RAG) pipelines. The model's ability to generate embeddings locally ensures that sensitive data remains on the user's device, thereby reducing latency and enabling offline functionality. The core innovation of EmbeddingGemma 2 lies in its capacity to create a single vector space that encompasses multiple modalities. This means a text query can retrieve relevant images, or a voice memo can be used to find a corresponding video clip. The model is modular, comprising a text and code backbone with 270 million parameters (further divided into a 130M transformer and a 140M embedder), a 170M parameter vision encoder, and an optional 300M parameter audio encoder. Developers can choose to load only the components they require, leading to a flexible parameter count: 270M for text-only, 440M for text and vision, 570M for text and audio, or the full 740M for all modalities. Crucially, all configurations share a single vector space, allowing embeddings generated by a text-only setup to match documents embedded by the complete model. The context window has been expanded to 8,192 tokens, which is four times larger than that of EmbeddingGemma 1. This increased capacity can accommodate approximately 29 images, 58 video frames, or 5.5 minutes of audio, enhancing its ability to process richer and longer inputs. Google's research team has reported that EmbeddingGemma 2 achieves leading scores among multimodal embedders with fewer than 1 billion parameters on the MTEB Code and MAEB benchmarks. Specifically, it scored 78.68 on MTEB Code v1, an improvement from EmbeddingGemma 1's score of 68.76, and 61.36 on MTEB multilingual v2, slightly surpassing EmbeddingGemma 1's 61.15. The model's weights are now accessible on platforms like Hugging Face and Kaggle, with builds available for Ollama, llama.cpp GGUF, and LiteRT, facilitating immediate deployment by developers.