Multimodal AI models for video, image, and audio understanding.
AI Assistant
Reka AI is the frontier AI lab that has done something almost no other lab of its size has done — built a genuinely competitive multimodal foundation model family from scratch, in the open, with a team small enough to fit in a single conference room. Founded in 2022 by ex-DeepMind, ex-Google Brain, and ex-Meta researchers — the founding team includes Yi Tay, Dani Yogatama, and Aitor Lewkowycz, all of whom shipped meaningful research at the top-tier labs before leaving to start Reka — the company has bases in Singapore and the San Francisco Bay Area and has stayed deliberately independent through 2026 rather than getting absorbed into a hyperscaler.
The one-line positioning: Reka is the frontier multimodal AI lab to pick when you need a model that understands video, image, and audio as first-class inputs, when you need private deployment, and when you value working with a lab whose entire research bet is native multimodality rather than a text model with vision bolted on. The Reka Core, Reka Flash, and Reka Edge models — spanning frontier, fast-serving, and on-device tiers — are among the very few multimodal families in 2026 that were designed multimodal from day one rather than fine-tuned into vision.
For enterprises building video-first, image-first, or audio-first products — content moderation, video search, media asset management, retail visual analytics, healthcare imaging, autonomous systems — Reka is genuinely the right technical answer more often than any of the frontier generalist labs. It is not the sharpest text-only model on the market, not the widest ecosystem, and not the cheapest per token on text. But if the input to your AI is anything other than pure text, Reka should be on your shortlist.
Reka does not sell a mass-market consumer chat product the way ChatGPT, Claude, Gemini, and Mistral Le Chat do. The company's product surface is Reka Space — a research-first playground for individual users and small teams — and Reka Nexus, an enterprise deployment platform. Access is via API, private deployment, or the Space UI. This is a research lab that ships products, not a consumer AI product company that also does research.
Reka's product identity is built around native multimodality. The features that matter are the model family's video and audio understanding, the deployment flexibility, and the willingness to share model weights on the Flash and Edge tiers.
Reka Core. The frontier flagship model as of 2026. Native multimodal input across text, image, video, and audio. Competitive with GPT-5 and Claude Opus 4.7 on multimodal reasoning benchmarks — meaningfully ahead of both on video-specific evaluations like Perception Test and video question-answering — and behind on pure-text reasoning by a modest margin. Context window in the hundreds of thousands of tokens, with video and audio counted at a fixed token budget per second of input.
Reka Flash. The fast-serving mid-tier model. Roughly one-tenth the price and three-times the throughput of Core, with quality that competes with GPT-5 mini and Claude Sonnet on multimodal tasks. This is the tier most production deployments end up on.
Reka Edge. The small, on-device model. Designed to run on consumer hardware — laptops, high-end phones, industrial edge devices — with video and image understanding included. Not a text-only distillation; a real multimodal model at small scale.
Native video understanding. Feed Reka a video clip and it answers questions about actions, objects, transitions, spoken content, and temporal relationships in a single pass. Most competing "multimodal" models decompose video into keyframes and reason over stills; Reka's video pathway processes temporal information directly. For video search, moderation, and analytics, the difference is a real capability gap, not a margin.
Native audio understanding. Spoken audio, music, and non-speech audio events are first-class inputs. Ask about tone of voice, emotional register, background sounds, or musical structure. Ahead of most competing frontier models, which typically transcribe first and reason over text.
Private deployment via Reka Nexus. Enterprise customers can deploy Reka Core, Flash, and Edge inside their own cloud environment — AWS, Azure, GCP, or on-prem. Model weights, inference, and data never leave the customer boundary. This is the operational moat versus the frontier generalist labs.
Reka Space. A web workbench for individual users and small teams — chat with any Reka model, upload images, video, or audio, run multimodal prompts, share sessions with collaborators. This is the fastest way to evaluate the models without a sales conversation.
Reka Vision. A dedicated enterprise product for video intelligence — searching video libraries, extracting metadata, detecting events, running content moderation at scale. Built on Core and Flash under the hood; sold as a vertical product to media, retail, and security customers.
Model weights available on Flash and Edge tiers. Reka publishes weights for the smaller-tier models under a research and commercial license, letting developers self-host without a sales conversation. Core remains a hosted-only frontier model. This is roughly the same pattern Mistral follows.
Reka's pricing splits into three surfaces — a low-cost consumer playground, a usage-based API for developers, and custom-priced enterprise deployments — and is competitive with the frontier generalist labs on the tiers where they overlap.
| Plan | Price | What you get |
|---|---|---|
| Reka Space Free | $0 | Access to Flash and Edge on Space, capped daily usage |
| Reka Space Pro | ~$15/month | Higher limits, access to Reka Core, private sessions |
| API (Reka Flash) | ~$0.20 per M input tokens, ~$0.40 per M output tokens | Fastest tier, multimodal I/O |
| API (Reka Core) | ~$3 per M input tokens, ~$8 per M output tokens | Frontier tier, multimodal I/O |
| Reka Nexus (Enterprise) | Custom (typical: low six figures/yr) | Private VPC deployment, model weights, dedicated support |
| Reka Vision (Enterprise) | Custom | Video intelligence product, per-hour ingestion pricing |
Video and audio input pricing is metered per second at a fixed token budget — typically 100-200 tokens per second of video, and 30-60 tokens per second of audio, depending on the model tier and processing mode. This is where cost modeling for video workloads gets nuanced; a one-hour video ingested at Core rates costs meaningfully more than the same query volume on text.
Reka Space Pro at approximately $15/month is priced below Claude Pro at $20 and comparable to Gemini Advanced. For individual users doing multimodal work — video analysts, journalists working with video sources, researchers analyzing image libraries — the value is real. For pure text work, the frontier generalist assistants remain sharper daily drivers.
API pricing on Flash undercuts most frontier alternatives on multimodal workloads, and the throughput is meaningfully higher. On pure text, GPT-5 mini and Claude Haiku are comparably priced with sharper text-only benchmarks. The Reka economic case gets stronger the more your workload is not text.
Reka Nexus enterprise deployments typically start in the low six figures for a mid-market pilot with a single-vertical use case (video search, content moderation, retail visual analytics) and scale into seven figures for larger deployments. Contracts include the model license, deployment support, and access to Reka Vision as an add-on.
Pros
Cons
Enterprise video intelligence products. Media libraries, security footage analysis, retail visual analytics, sports broadcasting. Reka Vision plus Reka Core is the sharpest available product for querying and understanding video at enterprise scale.
Multimodal RAG applications. RAG pipelines that need to index and retrieve across text, image, and video documents. Reka's native multimodal embeddings and generation together make the pipeline meaningfully simpler than stitching a text model plus a vision model plus a video model together.
Content moderation at scale. Video and audio moderation where the input is not just text and the moderation policy requires temporal or auditory context. Faster and more accurate than the transcribe-first-then-reason pattern most competing solutions use.
Media production and journalism working with video sources. Journalists analyzing hours of interview footage, documentary filmmakers reviewing archival material, sports analysts extracting event metadata from game footage. Reka Space Pro at $15/month is a legitimate daily-driver tool for this shape of work.
On-device multimodal applications. Reka Edge running locally on high-end phones and laptops opens up multimodal experiences that cannot round-trip to the cloud — privacy-sensitive video analysis, industrial edge devices, offline media apps.
Developers building multimodal APIs at cost. Reka Flash pricing plus its multimodal capability makes it a genuine cost win over GPT-5 or Claude for multimodal workloads.
Three broad alternatives exist depending on which axis of Reka's positioning matters most to you.
Gemini — Google's frontier multimodal assistant with the largest text context window (2M tokens) and native Workspace integration. Strong multimodal capability including video understanding via Gemini 2.5 Video. If your work is document-heavy or Workspace-first and multimodal is one axis among many, Gemini wins. Reka wins on video-specific evaluations and on private deployment flexibility.
ChatGPT — the widest ecosystem with GPT-5, image generation, Sora video generation, and Advanced Voice mode. Strong multimodal understanding on the input side but the video-input pathway is less mature than Reka's. If ecosystem breadth matters more than the sharpest video understanding, ChatGPT wins.
Claude — the sharpest writing and reasoning at the frontier. Multimodal input works well on images; video and audio are behind. If your work is text-first with occasional image analysis, Claude is the better daily driver.
Mistral Le Chat — the EU-first frontier assistant with strong text capability and multilingual coverage. Multimodal support is real but behind Reka on video specifically. Overlap exists on the private-deployment positioning; Mistral wins on European data residency, Reka wins on native multimodality.
Hugging Face and open-weight multimodal models. For teams committed to fully open-weight self-hosted stacks, the open ecosystem now includes credible multimodal models like Qwen-VL, LLaVA-NeXT, and open-source video models. None match Reka Core on frontier quality; the Flash-tier and Edge-tier open alternatives are competitive.
For teams evaluating multimodal specifically, the right test is a bake-off: pick a video-understanding task that stresses temporal reasoning and run it head-to-head across Reka, Gemini, and GPT-5. The winning axis often surprises text-first buyers who assumed the generalist frontier labs would win.
Try Reka Space at space.reka.ai. Free signup, no credit card required. Upload a video or an image and ask something that requires real understanding — "what happens at the transition point," "what emotion is the speaker conveying," "which frame shows the product logo." Compare the answer to what ChatGPT or Gemini produces.
Test the API on a real workload. If you build software with multimodal AI features, sign up at reka.ai/developers and get an API key. Run your existing prompts through Reka Flash or Core and compare quality, cost, and latency against your current provider. On multimodal workloads the numbers often come out in Reka's favor before the quality question is settled.
If you are an enterprise buyer, request a Nexus demo. Nexus deployments start with a scoping conversation about vertical use case, deployment topology, and volume. Pilot timelines are typically 4-8 weeks from first call to a working deployment.
For video-heavy workloads, evaluate Reka Vision separately. Vision is a product, not just an API — it includes indexing, search, and moderation surfaces on top of the base models. The right eval is against dedicated video intelligence products, not against text-first chat assistants.
Do not evaluate Reka on text-only benchmarks. Reka loses on MMLU, HumanEval, and other text-only reasoning tests versus GPT-5 and Claude Opus. The right evaluation is on multimodal tasks where the input includes video, image, or audio.
How does Reka Core compare to GPT-5 or Claude Opus 4.7 on video understanding? Reka Core wins on evaluations that stress temporal video reasoning — questions that require understanding what happens over time in a clip rather than just what appears in a keyframe. GPT-5 and Gemini remain competitive on shorter video clips and specific benchmarks; Claude's video understanding is behind both. For serious video work, Reka is the honest technical recommendation in 2026.
Can I self-host Reka models? Yes for Reka Flash and Reka Edge — model weights are published under a permissive license. Reka Core is hosted-only for now, either via Reka's own API or via Reka Nexus private VPC deployment.
How does video token pricing work? Video input is metered per second at a fixed token budget — typically 100-200 tokens per second on Core and Flash, depending on processing mode. A one-minute video at Core rates is roughly 10,000 tokens of input.
Does Reka train on my API data? Free-tier Space usage may be used for model improvement per the privacy policy. Paid API and enterprise contracts include data-handling terms that exclude training use by default.
Where is Reka based? Singapore and the San Francisco Bay Area, with distributed research and engineering teams. Investors include DST Global, Radical Ventures, Snowflake Ventures, and others.
Is there a Reka mobile app? Reka Space works well in mobile browsers. There is no dedicated iOS or Android app as of 2026 — the product surface is web-first.
Reka is the frontier AI lab to pick when your workload is not primarily text. For video intelligence, multimodal RAG, on-device multimodal deployment, and any product where the input includes real video or audio, Reka Core, Flash, and Edge together are the sharpest technical answer on the market. The native multimodal architecture is not a marketing claim — it produces measurable capability advantages on the evaluations that matter for real multimodal work.
Where Reka stops being the right answer: pure text reasoning, coding, and long-form English writing where Claude Opus 4.7 and GPT-5 both remain ahead by a real margin; ecosystem-heavy workflows where ChatGPT's plugin store, Gemini's Workspace integration, or Mistral's EU hosting are the decisive factor; and general consumer chat use cases where the polished daily-driver assistants remain a better experience.
The honest recommendation: developers and enterprise buyers with real multimodal workloads should run a bake-off this month — Reka Flash and Core against your current provider on your actual multimodal prompts. If Reka wins on quality or cost or both, follow it into production. Individual users doing multimodal work should try Reka Space Pro at $15/month for a month and see whether it displaces their current daily driver on that specific work.
Explore more assistants in the AI Assistant category, or read our full comparison of the frontier assistants for context on where Reka fits alongside the wider market.
AIQORA does not earn commission on Reka AI — this is an editorial recommendation only.