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Reka AI (commonly referred to as Reka) is an artificial intelligence research and product company that builds multimodal large language models capable of processing text, images, video, and audio inputs. Founded in 2022 by a team of researchers from Google DeepMind, Meta FAIR, and Google, the company has positioned itself as one of a small number of organizations building frontier-class multimodal AI models from scratch. Reka is headquartered in Sunnyvale, California, and maintains operations in Singapore.
The company emerged from stealth in June 2023 with $58 million in funding and has since released a family of models spanning different size classes: Reka Core, Reka Flash, and Reka Edge. In July 2025, Reka raised $110 million in a funding round backed by Nvidia and Snowflake, reaching a valuation of over $1 billion and achieving unicorn status.
Reka was founded in 2022 by five AI researchers who had previously worked at some of the world's leading AI laboratories. The founders, Dani Yogatama, Yi Tay, Cyprien de Masson d'Autume, Qi Liu, and Mikel Artetxe, shared a conviction that it was impractical to expect a single large language model to serve all possible enterprise use cases. While working on projects like AlphaCode and Google's Bard at DeepMind and Google, the founders observed that general-purpose models often fell short when organizations needed AI tailored to specific requirements, such as generating marketing copy in a particular brand voice or processing domain-specific documents.
The company operated in stealth mode through the first half of 2023, assembling a small research team and beginning work on its first models.
On June 28, 2023, Reka publicly emerged from stealth, announcing it had raised $58 million in a funding round led by DST Global Partners and Radical Ventures, with participation from Snowflake Ventures and several angel investors including former GitHub CEO Nat Friedman. At the time of this announcement, the company was valued at approximately $300 million.
Reka's pitch to investors centered on its ability to build efficient, customizable AI models for enterprise deployment. Unlike larger competitors that focused primarily on consumer-facing chatbots, Reka emphasized on-premise and virtual private cloud (VPC) deployment options that would allow enterprises to keep sensitive data within their own infrastructure.
In October 2023, Reka launched Yasa-1, its first publicly available multimodal AI assistant. Yasa-1 was built on a unified model trained from scratch and could process text, images, short videos, and audio snippets. The assistant supported 20 languages and offered a context window of up to 24,000 tokens by default, with the ability to handle documents as long as 100,000 tokens.
Yasa-1 was made available through APIs and as Docker containers for on-premise or VPC deployment. The system could also be customized on private datasets of any modality, allowing enterprises to fine-tune the model for specific use cases.
Following the Yasa-1 launch, Reka released Reka Flash, a 21 billion parameter multimodal language model that served as the company's mid-tier "turbo-class" offering. Flash was designed to deliver strong performance at a fraction of the computational cost required by larger models. It could process text, images, and video inputs and was positioned as a practical choice for everyday enterprise applications.
On April 15, 2024, Reka published a comprehensive technical report detailing its full model lineup: Reka Core, Reka Flash, and Reka Edge. The report, titled "Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models," was released as a preprint on arXiv and demonstrated that a small team could build frontier-competitive models.
Reka Core, the company's most capable model, approached the performance of GPT-4V on image question-answering benchmarks and ranked as the second most preferred model (behind only GPT-4V) in blind third-party human evaluations of multimodal chat, outperforming Claude 3 Opus. On video question answering, Core surpassed Gemini Ultra on the Perception-Test benchmark.
The smaller models also performed well above their weight class. Reka Flash (21B parameters) outperformed much larger models including Gemini Pro 1.0 and Llama 2 70B on multiple benchmarks, while Reka Edge (7B parameters) beat other models in its size category, including Mistral 7B and Gemma 7B.
In May 2024, Bloomberg reported that Snowflake was in talks to acquire Reka for more than $1 billion. However, by May 22, 2024, those talks had broken down without a deal, with both companies deciding it made more sense to move forward independently.
Despite the failed acquisition, Reka and Snowflake deepened their partnership. Snowflake integrated Reka Flash and Reka Core into its Cortex AI service, making them available to over 400 enterprises using Snowflake's data cloud. This integration enabled customers to build generative AI applications that could work with text, images, and video inputs directly within Snowflake's platform.
In May 2024, Reka released Vibe-Eval, an open evaluation benchmark for multimodal language models. The benchmark consists of 269 visual understanding prompts, including 100 prompts rated as hard difficulty, with gold-standard responses authored by human experts. Over 50% of the questions in the hard set were answered incorrectly by all frontier models at the time of release, making it a useful tool for measuring real progress in multimodal AI.
On November 25, 2024, co-founder and Chief Scientist Yi Tay announced his departure from Reka to return to Google DeepMind as a Senior Staff Research Scientist. Tay had spent approximately 1.5 years at Reka and cited his identity as a researcher and scientist as the primary reason for his return. At DeepMind, he went on to lead a new GenAI research lab in Singapore, contributing to work on the Gemini Deep Think model.
On March 11, 2025, Reka open-sourced Reka Flash 3, a 21 billion parameter general-purpose reasoning model trained from scratch. The model was released under the Apache 2.0 license, making it freely available for commercial and research use. Flash 3 was trained on synthetic and public datasets using supervised fine-tuning, followed by RLOO (Reinforced Language Learning from Observations) with model-based and rule-based rewards.
Reka Flash 3 performed competitively with proprietary models such as OpenAI o1-mini, which was notable given its relatively small parameter count. An updated version, Reka Flash 3.1, followed shortly after, improving by 10 points on LiveCodeBench v5 and showing particular strength on coding tasks and as a base model for agentic fine-tuning.
In July 2025, Reka raised $110 million in a funding round backed by Nvidia and Snowflake. This round more than tripled the company's valuation from approximately $300 million to over $1 billion, making Reka a unicorn. The investment reflected confidence in Reka's ability to develop market-leading models at a fraction of the cost incurred by larger competitors.
Reka was founded by five researchers with complementary backgrounds in natural language processing, machine learning, and large-scale systems engineering.
| Founder | Role at Reka | Previous Affiliation | Background |
|---|---|---|---|
| Dani Yogatama | CEO and Co-founder | DeepMind (2016-2022), Baidu Silicon Valley AI Lab (2015-2016) | PhD in Computer Science from Carnegie Mellon University. Senior Staff Research Scientist at DeepMind. Associate Professor at the University of Southern California. |
| Yi Tay | Co-founder, Chief Scientist (until Nov 2024) | Google Brain, DeepMind | Co-lead of PaLM 2 at Google. Inventor of UL2 and Differentiable Search Indexes. Returned to Google DeepMind in November 2024. |
| Cyprien de Masson d'Autume | CTO and Co-founder | DeepMind (2016-2022) | Staff Research Engineer at DeepMind. Worked on Gopher and AlphaCode. |
| Qi Liu | Co-founder | DeepMind, Meta FAIR, Microsoft Research | PhD from the University of Oxford. Assistant Professor at the University of Hong Kong. |
| Mikel Artetxe | Co-founder | Meta FAIR | PhD from the University of the Basque Country. Research focus on multilingual NLP, unsupervised machine translation, and cross-lingual representation learning. Honorary Researcher at the University of the Basque Country. |
Reka has developed models across multiple size tiers, all trained from scratch with native multimodal capabilities.
Reka Core is the company's largest and most capable model. While Reka has not officially disclosed the exact parameter count, reports suggest it has approximately 67 billion parameters. Core features a 128K context window and can process text, images, video, and audio inputs simultaneously.
Core was designed as a frontier-class model and was benchmarked against GPT-4V, Claude 3 Opus, and Gemini Ultra at the time of its release. On language tasks, it achieved an MMLU score of 83.2, a GSM8K score of 92.2, and a HumanEval score of 76.8. On multimodal tasks, it scored 56.3 on MMMU, 78.1 on VQAv2, and 59.3 on the Perception-Test video benchmark.
Reka Flash is a 21 billion parameter dense model trained on approximately 5 trillion text tokens with an 8K default context window that can extend to 128K. Flash was positioned as a "turbo-class" model, offering fast inference and strong performance at a lower computational cost than Core.
Flash outperformed several larger models on key benchmarks: it scored 75.9 on MMLU, 85.8 on GSM8K, 72.0 on HumanEval, and 53.3 on MMMU. These scores surpassed Gemini Pro 1.0 and Llama 2 70B on multiple measures.
Reka Edge is a 7 billion parameter dense model trained on 4.5 trillion text tokens. It was designed for resource-constrained environments such as on-device deployment and local inference scenarios. Edge features an 8K default context window that can extend to 64K.
Despite its compact size, Edge outperformed other models in the 7B class, including Mistral 7B, Gemma 7B, and Llama 2 7B. It scored 65.7 on MMLU, 66.2 on GSM8K, and 54.3 on HumanEval.
Released in March 2025 under the Apache 2.0 license, Reka Flash 3 is a 21 billion parameter general-purpose reasoning model trained from scratch. Unlike the earlier Flash model, Flash 3 was specifically optimized for reasoning tasks through reinforcement learning (RLOO). It uses a Llama-compatible architecture and the cl100k_base tokenizer.
Flash 3.1, an updated version released shortly after, incorporated advances in Reka's reinforcement learning stack and improved by 10 points on LiveCodeBench v5. Flash 3.1 showed particular strength on coding benchmarks and as a base model for fine-tuning on agentic tasks.
Reka Spark is an ultra-compact model with approximately 2 billion parameters, designed for lightweight tasks and edge deployment on smaller devices. Spark serves as the most affordable entry point in Reka's model lineup.
The following table summarizes Reka's model performance on key benchmarks alongside leading competitors, as reported in the April 2024 technical report.
| Benchmark | Reka Edge (7B) | Reka Flash (21B) | Reka Core | GPT-4 (0613) | Claude 3 Opus | Gemini Ultra |
|---|---|---|---|---|---|---|
| MMLU | 65.7 | 75.9 | 83.2 | 86.4 | 86.8 | 83.7 |
| GSM8K | 66.2 | 85.8 | 92.2 | 92.0 | 95.0 | 94.4 |
| HumanEval | 54.3 | 72.0 | 76.8 | 76.5 | 84.9 | 74.4 |
| GPQA | - | 34.0 | 38.2 | 38.1 | 50.2 | 35.7 |
| Benchmark | Reka Flash (21B) | Reka Core | GPT-4V | Claude 3 Opus | Gemini Ultra |
|---|---|---|---|---|---|
| MMMU | 53.3 | 56.3 | 56.8 | 59.1 | 59.4 |
| VQAv2 | 78.4 | 78.1 | 77.2 | - | 77.8 |
| Perception-Test (Video) | 56.4 | 59.3 | - | - | 54.7 |
| Benchmark | Reka Edge | Reka Flash | Reka Core | GPT-4 |
|---|---|---|---|---|
| MedMCQA | 52.6 | 71.3 | 80.6 | 72.4 |
| PubMedQA | 71.6 | 69.0 | 74.6 | 80.4 |
| MMLU Medical | 65.7 | 79.5 | 88.3 | 90.3 |
| Model | ELO Score | Win Rate |
|---|---|---|
| GPT-4V | 1201 | 79.4% |
| Reka Core | 1130 | 72.2% |
| Reka Flash | 1082 | 66.8% |
| Claude 3 Opus | 1073 | 66.2% |
These results were notable because Reka built these models with a team of roughly 20 core researchers, while competitors employed teams that were orders of magnitude larger.
Reka Chat is the company's consumer-facing chat interface, available at chat.reka.ai. It allows users to interact with Reka's models for text conversations, document analysis, and multimodal queries involving images and files.
Reka Research is an agentic AI product that can browse the web and search through private documents to answer complex questions. The tool performs multiple web searches, visits dozens of websites in one to three minutes, and synthesizes information from multiple sources in a multi-hop manner, reasoning in natural language before taking each step. Every answer includes a traceable path of the steps taken.
The Reka Research API is OpenAI-compatible and supports customizable execution, including domain restrictions and structured JSON output. Enterprises can deploy Reka Research on-premise, in a private cloud, or through the API.
Reka offers flexible deployment options for enterprises, including API access, on-premise deployment, and private cloud (VPC) deployment via Docker containers. This flexibility is a key differentiator, as many competing AI providers only offer cloud-based API access. Enterprises can also fine-tune Reka's models on their own proprietary datasets.
Snowflake has been one of Reka's most significant partners. Snowflake Ventures participated in Reka's initial $58 million funding round in 2023 and later backed the $110 million round in 2025. Through the partnership, Reka Flash and Reka Core have been integrated into Snowflake's Cortex AI service, making Reka's multimodal capabilities available to over 400 enterprises using Snowflake's data cloud.
The integration allows Snowflake customers to build generative AI applications that can process text, images, and video inputs without moving data outside of Snowflake's platform. Reka's models brought the total number of LLMs available in Snowflake Cortex to approximately a dozen.
Nvidia has been both an investor and a technology partner for Reka. Beyond participating in the $110 million funding round in 2025, Nvidia's GPU infrastructure serves as the primary compute platform for training and serving Reka's models. The partnership reflects the close relationship between frontier AI model developers and Nvidia's hardware ecosystem.
Unlike many competitors that built multimodal capabilities as extensions to existing text-only models, Reka trained its models as natively multimodal from the beginning. This approach means the models can process and reason across text, images, video, and audio within a single unified architecture, rather than relying on separate encoders bolted onto a text model after the fact.
One of Reka's defining characteristics is its emphasis on training efficiency. The company has consistently achieved competitive results with significantly fewer resources than larger competitors. The original Reka Core model was developed by a team of roughly 20 researchers, compared to the hundreds or thousands of engineers at organizations like OpenAI, Google, and Anthropic. Yi Tay noted during his time at Reka that the team managed to build a GPT-4-class multimodal model from scratch in under a year with this small team.
Reka has contributed to the open-source AI ecosystem through several releases. Reka Flash 3 and Flash 3.1 were released under the Apache 2.0 license, making them freely available for commercial and research use. The company also released the Vibe-Eval benchmark as an open resource for the research community, providing a standardized way to evaluate multimodal language models with expert-annotated gold-standard responses.
| Date | Round | Amount | Lead Investors | Valuation |
|---|---|---|---|---|
| June 2023 | Series A | $58 million | DST Global Partners, Radical Ventures | ~$300 million |
| July 2025 | Growth Round | $110 million | Nvidia, Snowflake | ~$1 billion |
Total disclosed funding: approximately $168 million.
Notable investors across rounds include DST Global Partners, Radical Ventures, Snowflake Ventures, Nvidia, and angel investor Nat Friedman (former GitHub CEO).
| Feature | Reka AI | OpenAI | Google DeepMind | Anthropic |
|---|---|---|---|---|
| Founded | 2022 | 2015 | 2010 (DeepMind) | 2021 |
| Headquarters | Sunnyvale, CA | San Francisco, CA | London, UK | San Francisco, CA |
| Team Size (approx.) | ~50 | ~3,000+ | ~2,500+ | ~1,500+ |
| Flagship Model (2024) | Reka Core | GPT-4 | Gemini Ultra | Claude 3 Opus |
| Native Multimodal | Yes (text, image, video, audio) | Yes (GPT-4V/o) | Yes (Gemini) | Partial (text, image) |
| Video Understanding | Yes | Limited | Yes | No |
| Audio Input | Yes | Yes (Whisper integration) | Yes | No |
| On-Premise Deployment | Yes | No (API only) | Limited | No (API only) |
| Open-Source Models | Yes (Flash 3, Flash 3.1) | Limited (Whisper, CLIP) | Yes (Gemma) | No |
| Estimated Valuation | ~$1B (2025) | ~$157B (2025) | Part of Alphabet | ~$60B (2025) |