Multimodal models from ex-DeepMind/Meta researchers — Reka Flash, Core and Edge
Reka AI is a research lab founded by scientists from DeepMind, Meta and Google, building multimodal models that understand text, images, video and audio. Its Reka Flash, Core and Edge models are available through an API and partner platforms, with a research chat demo to try them. Pricing is usage-based with a free entry tier for developers.
| Pricing | Freemium |
|---|---|
| Category | Coding & Development |
| Platforms | web |
| Company | Reka AI |
| Best for | Developers building multimodal applications via API |
| Official website | www.reka.ai |
The right coding tool depends on where you write code and how much of your codebase it can understand. IDE integration is the first filter — a tool that lives in your editor, sees your project files and respects your stack will help far more than a general chatbot you paste snippets into. Look for accurate completions, clear explanations of suggested changes, and strong refactoring and debugging support for your main languages. Privacy deserves real attention: check whether your code is used for training, and whether a self-hosted or enterprise option exists for sensitive projects. Price against the hours it actually saves — most developers feel the difference within the first week of real use.
Reka AI is a research lab founded by scientists from DeepMind, Meta and Google, building multimodal models that understand text, images, video and audio. Its Reka Flash, Core and Edge models are available through an API and partner platforms, with a research chat demo to try them. Pricing is usage-based with a free entry tier for developers.
Reka offers Native multimodal understanding (text, image, video, audio), Model sizes from Edge to Core, Developer API with free entry and Strong research pedigree.
Reka follows a freemium model: a free tier is available, and paid plans unlock higher limits and advanced features. Check its official website for current plan prices.
Strengths of Reka include True multimodal in one model; Efficient smaller models; Published research and evals. On the downside, No mainstream consumer app; Smaller ecosystem than big labs; Brand still emerging.
by Flatbread
Turns a coding-agent session into one shareable link: the prompt, every step, what broke, what fixed it, and the diff — readable by people and resumable by agents.
Best for: Developers running coding agents who want shareable, resumable records of what their agents actually did
Limitation: No pricing published on the official homepage
by MAO
Multi-agent orchestration canvas: Claude Code, Codex, Gemini and Grok work as a team on one canvas, handing work to each other through drawn wires.
Best for: Developers running several coding agents at once who want them coordinated instead of colliding
Pricing: Paid
Limitation: Each agent still needs its own login and subscription
by Arco
MIT-licensed Codex plugin (local MCP server plus panel UI) for browsing and editing GitHub and Linear issues and pulling issue context into your conversation.
Best for: Codex users who triage GitHub and Linear issues without leaving the conversation
Pricing: Open Source
Limitation: Requires a Codex desktop environment plus the codex CLI (and the gh CLI for GitHub) — not a standalone tool
by Polylane
Production-issue agents that learn what you run, detect issues from metrics, logs and traces, and fix what they can by opening pull requests.
Best for: Engineering teams tired of on-call pages who want production fixes to arrive as pull requests
Limitation: No pricing shown in the official site text fetched
by Skyhook
Open-source (Apache 2.0) Kubernetes UI with a built-in MCP server for AI agents: topology, events, Helm, GitOps and audits — on your laptop or in your cluster.
Best for: Kubernetes operators who want one interface for humans and agents over live clusters
Pricing: Open Source
Limitation: AI investigations live in the paid Radar Cloud tiers, not the free open-source build
by Entire
Git forge built for agent-human collaboration — pull requests are replaced by 'Trails', agent-scored evals on every push, with agent sessions and prompts searchable in the repo as Continuous Memory.
Best for: Teams building software with AI agents who want evals and agent context inside the repo
Pricing: Paid
Limitation: Pay-as-you-go — costs scale with model-provider rates
