rekaai Reka Flash 3

Reka Flash 3 by rekaai excels in the Coding category, where it ranks #242 with a composite score of 58/100. Reka Flash 3 is a general-purpose, instruction-tuned large language model with 21 billion parameters, developed by Reka. It excels at general chat, coding tasks, instruction-following, and function calling. Featuring a 32K context length and optimized through reinforcement learning (RLOO), it provides competitive performance comparable to proprietary models within a smaller parameter footprint. Ideal for low-latency, local, or on-device deployments, Reka Flash 3 is compact, supports efficient quantization (down to 11GB at 4-bit precision), and employs explicit reasoning tags ("<reasoning>") to indicate its internal thought process. Reka Flash 3 is primarily an English model with limited multilingual understanding capabilities. The model weights are released under the Apache 2.0 license. It is particularly strong in areas highlighted by its top benchmark performance and adoption metrics, making it suitable for both individual developers and enterprise teams looking for a reliable coding solution.

Reka Flash 3 is priced at $0.10 per million input tokens and $0.20 per million output tokens (USD). Contact the provider for volume discounts and enterprise pricing. Pricing is competitive within the coding category and reflects the model's quality-to-cost ratio.

In the Coding category, Reka Flash 3 holds rank #242 out of 6 models tracked. Its quality rank is #242 and adoption rank is #242. You can use our comparison tool at /compare to see detailed side-by-side metrics with specific alternatives. Key differentiators include its composite scoring across benchmarks, community sentiment, and real-world adoption rates.

Reka Flash 3 has been evaluated across 5 different signals. Its strongest areas include Capabilities (33/100), Pricing (0/100), Context Window (76/100). These scores are derived from industry-standard benchmarks, community ratings, and real-world performance metrics. The composite score of 58/100 reflects a weighted combination of all tracked signals.

Reka Flash 3 is a paid model, though some providers may offer trial credits or limited free tiers for evaluation. Check rekaai's website for current free tier availability and promotional offers.

Reka Flash 3 supports a 66K token context window (65,536 tokens total). That translates to roughly 49,152 words in a single prompt. Plenty for most coding tasks, medium-length documents, and extended conversations.

Reka Flash 3 can generate up to 66K output tokens (65,536 tokens) per response. That is roughly 49,152 words. This is enough for generating complete code files, detailed reports, or long-form content in a single response.

Reka Flash 3 supports extended reasoning/chain-of-thought, streaming responses. These capabilities determine which workflows and integrations the model can handle natively.

Yes, Reka Flash 3 is an open-source model. You can download the weights, run it locally, fine-tune it for your use case, or deploy it on your own infrastructure. Many cloud providers also offer hosted versions if you prefer not to manage the infrastructure yourself. Self-hosting gives you full control over data privacy and eliminates per-token API costs.

Reka Flash 3 was developed by rekaai. It was released on March 12, 2025. You can access it through rekaai's API or download the model weights directly. Check our provider page for all models from rekaai and how they compare against each other.

Pick Reka Flash 3 when you need a budget-friendly option for high-volume, simpler tasks where you prioritize cost over peak performance. If your task is straightforward text completion or classification, a cheaper model might give you 90% of the quality at a fraction of the price. Run a quick benchmark on your actual use case before committing.

You can access Reka Flash 3 through rekaai's API using standard HTTP requests or their official SDK. Most providers support OpenAI-compatible endpoints, so switching between models often requires changing just the model name in your API call. Streaming is supported for real-time token-by-token output. For production use, implement proper error handling, rate limiting, and cost monitoring.