Frontier multimodal foundation model natively processing text, high-resolution imagery, video clips, and audio inputs.
Technical Specifications
- Provider
- Reka
- Category
- flagship
- Release Date
- 2024-04
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- API Pay-as-you-go
- Reasoning Effort Control
- Fixed
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- reka-core
- Supported SDK & API Protocol
- Reka REST API (OpenAI-Compatible)
- Lifecycle & Deprecation Status
- Legacy Stable (Upgrade path available in Reka lineup)
- Production Rate Limits (RPM & TPM)
- 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($3/1M in) across full 128K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.3/1M tokens
- Computer Use & Browser GUI Agent
- API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- API Prompt Engineering & RAG Distillation
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
- Weights Precision & Quantization
- Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster)
- Minimum GPU VRAM Requirement
- Zero Local VRAM Required (100% Serverless Cloud API)
- Supported Inference Engines
- Managed Reka Cloud API
- System Card & Safety Framework
- Reka Production Safety & Alignment Verified
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Reka Core Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~31 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Reka Core:
Benchmark Scores & 6-Axis Capability Radar
- MMLU-Pro
- 75%
- HumanEval
- 80%
- Chatbot Arena Elo
- 1270pts
Strengths
- Native omnimodal processing spanning text, video, speech, and imagery
- High accuracy on multi-frame video scene understanding and audio transcription
- Built by former Google DeepMind, FAIR, and Baidu frontier AI researchers
Limitations & Trade-offs
- Premium commercial pricing ($3.00 input / $15.00 output per 1M)
- Smaller enterprise ecosystem than major cloud hyper-scalers
Editorial Assessment
A versatile frontier omnimodal model built from the ground up for rich media processing.
Best for: Multimodal video search, audio-visual QA, and cross-format media intelligence
Compare Reka Core
Frequently Asked Questions about Reka Core
How much does the Reka Core API cost per 1M tokens?+
Reka Core by Reka is priced at $3 per 1M input tokens and $15 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.3 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Reka Core pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Reka Core's context window?+
Reka Core features a 128K tokens context window, which equals approximately 96,000 English words or ~256 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 8,192 tokens.
Can I run Reka Core locally on my own GPU (Open Weights)?+
No, Reka Core is a proprietary closed-weights model hosted by Reka. It cannot be downloaded for local GPU execution and must be accessed via Reka's official API or cloud platform.
Does Reka train on data sent to the Reka Core API, and is commercial use allowed?+
API payloads are excluded from model training by default. License tier: Proprietary Commercial API (Reka Terms). Full commercial ownership of generated outputs. Supported deployment channels include: Reka Cloud API & Enterprise Endpoints.
How does Reka Core perform on coding and reasoning benchmarks?+
Reka Core achieves a composite Intelligence Index of 77/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, 75% on MMLU-Pro, and 1270 pts on Chatbot Arena Elo.
What is Reka Core best used for?+
Reka Core is best suited for: Multimodal video search, audio-visual QA, and cross-format media intelligence. Assessment: A versatile frontier omnimodal model built from the ground up for rich media processing.
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Reka Core:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
An AI model trained via reinforcement learning to spend dynamic compute thinking through complex logic, math, and architectural problems before outputting a solution. Examples include OpenAI o1, o3, and DeepSeek R1.
A step-by-step reasoning technique where an AI model generates intermediate problem-solving thoughts before arriving at a final answer. Native reasoning models like OpenAI o3 and DeepSeek R1 use CoT to solve complex math and coding challenges.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.