Reka Edge
Reka AI · released Mar 11, 2026 · RekaAI/reka-edge-2603
- Type
- Open weightsCustom licence
- Params
- 7.1B
- Context
- 16K
about 12K words of context · download allowed, licence restricts use
Our take
Written Aug 3, 2026Reka Edge is a compact multimodal model that accepts text, images and video on a small parameter budget. It is positioned as a lightweight, cost-sensitive option for teams that need vision inputs without a large footprint.
Pick this when you need multimodal inputs at minimal cost and can accept unverified quality. Use it for constrained environments where a 7.1-billion-parameter model fits. Skip it if you need measured benchmark scores, high throughput, a standard permissive licence, or a context window above sixteen thousand tokens.
The case for it
- Extremely cheap multimodal access: both tracked providers charge the same low rate for input and output.
- Handles text, images and video at a small scale unusual for multimodal models.
The case against it
- No measured quality in our data: chat, reasoning, coding and vision performance are all unverified.
- Very low throughput on native hosting, at five tokens per second.
- Custom restricted licence and a narrow sixteen-thousand-token context window, with no larger option listed.
How good is it?
We hold no score for this model.
We look for every model we track on every board we watch, and none of them has turned up Reka Edge — so there is no intelligence, coding, agentic or writing score to show you, not a low one, none.
Can you run it yourself?
- Fits in memory
- weights load entirely on the card
- Spills to system RAM
- some weights offload; much slower
- Too large
- will not load even with offload
- est
- size is calculated; the verdict could change by 10%
Comfortable fit
GeForce RTX 4090 · 24 GB
Room to spare. 16.7 GB spare means a 10% error in the size would not change the answer.
GeForce RTX 5090 · 32 GB
Room to spare. 24.7 GB spare means a 10% error in the size would not change the answer.
Apple M1 (8-core GPU) · 16 GB
Room to spare. 5.9 GB spare means a 10% error in the size would not change the answer.
Memory use by level
Against a 24 GB card.
All 3 sizes here are calculated, not measured. We hold no measured file for this model, so each size comes from the parameter count and every verdict above inherits that uncertainty.
Check against your own machine → · All 71 devices, with every size →
Or rent it from someone else
Cheapest of 2 live listings. Picked at the widest standard context we hold, within one quantisation slice, so the numbers beside it are a price one host actually charges.
- per 1M tokens
- $0.10 in / $0.10 out
- Context served
- 16K
- Throughput
- Not measured
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| OpenRouter | $0.10 / $0.10 | 16K | not measured | Unknown | Unknown | Unknown |
| Rekabf16 | $0.10 / $0.10 | 16K | 3 tok/s | No | No | Confirmed |
Across the 2 listings we hold: 1 say they do not train on prompts, 0 say they do and 1 do not say. 1 appear in the zero-retention registry we check; the rest are unknown to us rather than confirmed either way.
What each host's API supports
From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.
- ✓
- Supported
- ✗
- Not supported
- Not published
- host gave no parameter list
| Provider | Tool calling | JSON output | Strict schema |
|---|---|---|---|
| OpenRouter | ✓ | ✗ | ✓ |
| Rekabf16 | ✓ | ✗ | ✓ |
Tool calling: 2 of 2 listings say yes. JSON output: 0 of 2 listings say yes, 2 say no. Strict schema: 2 of 2 listings say yes.
When we formed this view
Dates behind this page
Prices last checked 14h ago
What we do not know about this model yet
- We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
- No board we watch has turned up a score, so we hold no quality figures at all.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
- 1 of 2 listings do not say whether they train on prompts.
- We hold no cached-input rate for any of its listings.
Licence and identifiers
What the licence allowsCustom licence, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.
Licence
Custom licence
This model ships custom license terms that don't map to a known template. We haven't parsed them, so commercial use, redistribution and derivatives are unverified — review the original terms before shipping.
Identifiers
- Hugging Face
- RekaAI/reka-edge-2603
- Architecture
- Dense
- Modality record
- text+image+video->text
- Catalogue slug
- rekaai-reka-edge