The multimodal model from ex-DeepMind

Reka is what happens when the team that helped build Gemini goes independent. Three multimodal models — Core (frontier), Edge (mid-size), Flash (small) — that natively understand text, images, audio, and short video clips. If you're building a product that needs vision + language + audio in one model, Reka is the most cohesive multimodal API in 2026.

Who it's for: Developers building video understanding, voice agents with vision, document AI, or any product where one model needs to ingest multiple modalities.

Key features

4-mode Native multimodal

One API handles text, images, audio, and short video clips natively. No need to chain a vision model + speech-to-text + LLM. Reka fuses them in the same forward pass.

Video Video understanding

Pass a 30-second video clip and ask questions about it. Core can identify actions, objects, on-screen text, and answer follow-ups. Better than Gemini 1.5 Pro on short-form video benchmarks.

128k 128k context window

128k token context for Core, 64k for Edge. Enough to drop in entire transcripts, long documents, or full meeting recordings.

$0.40 Reasonable pricing

Core at $0.40/M input tokens — about 1/3 the price of GPT-5 for comparable tasks. Edge and Flash drop to $0.04/M for high-volume workloads.

The honest take

✓ What works

Best-in-class multimodal fusion — text+image+audio+video in one model
Video understanding is excellent for short-form clips
Reasonable pricing vs GPT-5/Claude 4 for comparable work
Three model sizes to match workload to budget
Real-time voice agent API on the Edge model

✗ What doesn't

Smaller developer community than OpenAI/Anthropic
Pure-text reasoning still trails GPT-5 and Claude 4 Opus
No image generation — read-only on vision
Free tier is restricted — hard to evaluate properly

Verdict

Reka is the right pick when your AI workload genuinely spans modalities — video analysis, voice agents with screen understanding, document AI that includes charts and images. The multimodal fusion is genuinely better than stitching separate APIs. For pure text or pure code, Claude/GPT-5 still win. For vision-only, GPT-5 Vision or Claude Sonnet 4 are simpler. The sweet spot: when one model needs to see, hear, and reason at the same time.

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