Command R7B Arabic dari Cohere adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 128K, kecepatan 135 token/detik, indeks kecerdasan 39/100, serta tarif $0.07/$0.2 per 1M token.
Technical Specifications
- Provider
- Cohere
- Category
- open-source
- Release Date
- 2025-02
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- Free / Self-Hosted
- 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)
- command-r7b-arabic
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2027+)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($0.07/1M in) across full 128K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.02/1M 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
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 4 GB VRAM (4-bit Quantized) · 15 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Command R7B Arabic Cost, Context, Hardware & API Integration
Pay-as-you-go API is cheaper for this volume (saves $14.30/mo vs $20 flat plan).
Break-even point: ~1,754 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 Command R7B Arabic:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 26%
- GPQA Diamond
- 43%
- MMLU-Pro
- 67%
- HumanEval
- 72%
- Long-Context Reasoning
- 70%
- Chatbot Arena Elo
- 1225pts
Strengths
- Jendela konteks 128K dengan kapasitas output maksimum hingga 4K token (SWE-bench Verified 26% · GPQA Diamond 43%)
- Kecepatan generasi tinggi 135 token/detik dengan latensi token pertama (TTFT) hanya 310 ms
- Bobot terbuka (7B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Limitations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
Editorial Assessment
Memadukan skor kecerdasan 39/100, konteks 128K, dan kecepatan 135 tok/dtk, Command R7B Arabic merupakan pilihan yang sangat andal dalam ekosistem Cohere.
Best for: Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.
Compare Command R7B Arabic
Frequently Asked Questions about Command R7B Arabic
How much does the Command R7B Arabic API cost per 1M tokens?+
Command R7B Arabic by Cohere is priced at $0.07 per 1M input tokens and $0.2 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.02 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Command R7B Arabic 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 Command R7B Arabic's context window?+
Command R7B Arabic 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 4,096 tokens.
Can I run Command R7B Arabic locally on my own GPU (Open Weights)?+
Yes, Command R7B Arabic (7B) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does Cohere train on data sent to the Command R7B Arabic API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Open-Weights Research & Commercial License. Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Command R7B Arabic perform on coding and reasoning benchmarks?+
Command R7B Arabic achieves a composite Intelligence Index of 39/100, scoring 26% on SWE-bench Verified, 43% on GPQA Diamond, 67% on MMLU-Pro, and 1225 pts on Chatbot Arena Elo.
What is Command R7B Arabic best used for?+
Command R7B Arabic is best suited for: Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.. Assessment: Memadukan skor kecerdasan 39/100, konteks 128K, dan kecepatan 135 tok/dtk, Command R7B Arabic merupakan pilihan yang sangat andal dalam ekosistem Cohere.
Cohere Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Cohere's flagship reasoning tier:
North Mini Code
Command A Plus
Command A Reasoning
Command A Translate
Command A Vision
Command A
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Command R7B Arabic:
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.
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.
An open standard created by Anthropic that allows AI applications and agents to connect seamlessly to local filesystems, development environments, and third-party APIs via standardized server interfaces.
A cost and latency optimization where inference providers store pre-computed attention states for repeated prompt prefixes. Reusing cached tokens typically slashes API input costs by 50% to 90% and cuts TTFT substantially.
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.
Sumber resmi: Cohere Regional AI Solutions (Feb 2025)↗