Reka Flash 3
Reka AI · released Mar 11, 2025 · RekaAI/reka-flash-3
- Type
- Open weightsApache License 2.0
- Params
- 20.9B
- Context
- 66K
about 49K words of context
Our take
Written Sep 30, 2026Reka Flash 3 is a text model you can download and run yourself, or reach through one of two hosts. Its licence allows commercial use, changes and redistribution, and nothing here measures how good its answers are, so the decision rests on a trial of your own.
Use it for everyday text work where the bill matters and you can judge the output yourself, or when you want the option to run the model on your own hardware. Its licence allows commercial use, changes and redistribution (Apache 2.0). Skip it if a task needs measured coding, reasoning or chat evidence, which nothing here supplies.
The case for it
- The licence allows commercial use, changes and redistribution (Apache 2.0), so the terms are not the thing that decides this one.
- You can download it and run it yourself, with two hosts listed as an alternative route if you would rather not.
The case against it
- No benchmark scores are supplied, so the only way to judge the answers is to trial it on work you can check yourself.
- A 2025 release, so it is not the newest option if that matters to you.
How good is it?
We hold no score for this model.
So there is no figure here for everyday use, coding, agent work or writing. That is a gap in our data, not a low score.
Can you run it yourself?
Comfortable fit
GeForce RTX 4090 · 24 GB
Room to spare. 7.7 GB spare means a 10% error in the size would not change the answer.
GeForce RTX 5090 · 32 GB
Room to spare. 15.7 GB spare means a 10% error in the size would not change the answer.
Apple M2 (10-core GPU) · 24 GB
Room to spare. 2.9 GB spare means a 10% error in the size would not change the answer.
Memory use by level
Against a 24 GB card.
What is quantisation? →This model on every device we track71 devicesThe Q4 build most people download, on each device: what the weights come to, how much context the memory leaves, and whether it runs. Smallest device that runs it first.
Check against your own machine → · Where to rent it hosted →
Or rent it from someone else
Prices checked 4 hours ago — each listing carries its own date.
- per 1M tokens
- $0.10 in / $0.20 out
- Context served
- 66K
- Throughput
- ~73 tok/s
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| OpenRouterOpenRouter's own listing | $0.10 / $0.20checked 4 hours ago | 66K | not measured | Unknown | Unknown | Unknown |
| RekaThrough OpenRouter | $0.10 / $0.20checked 4 hours ago | 66K59K max reply | 73 tok/s | No | No | Confirmed |
OpenRouterAcross the 2 listings we hold: 1 says it does not train on prompts, 0 say they do and 1 does not say. 1 appears in the zero-retention registry we check; the rest are unknown to us.
What each host's API supports
From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.
| Provider | Tool calling | JSON output | Strict schema |
|---|---|---|---|
| OpenRouterOpenRouter's own listing | ✗ | ✗ | ✓ |
| RekaThrough OpenRouter | ✗ | ✗ | ✓ |
Tool calling: 0 of 2 listings say yes, 2 say no. JSON output: 0 of 2 listings say yes, 2 say no. Strict schema: 2 of 2 listings say yes.
When we formed this view
Recent changes
What moved
first indexed by our pipelineEach date is the day we first saw the change, or the day the maker announced it.
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 independent board has scored it, 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 does not say whether it trains on prompts.
- We hold no cached-input rate for any of its listings.
- We hold no batch or off-peak rate for any of its listings.
- We hold a decode speed for it, but no prompt-processing (prefill) figure, so how long the input side of a job takes is unknown to us.
Licence and identifiers
What the licence allowsApache License 2.0, 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
Apache License 2.0
Fully permissive: commercial use, redistribution, and derivatives allowed. Requires attribution and a copy of the license. Includes an express patent grant.
Identifiers
- Hugging Face
- RekaAI/reka-flash-3
- Architecture
- Dense
- Takes in, gives back
- Text in, text out
- Catalogue slug
- rekaai-reka-flash-3