Cohere द्वारा विकसित Command R7B एक उन्नत AI मॉडल है, जिसमें 128K टोकन का कॉन्टेक्स्ट विंडो और $0.07/1M इनपुट तथा $0.2/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।
Technical Specifications
- Provider
- Cohere
- Category
- open-source
- Release Date
- 2024-12
- 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
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Legacy Stable (Upgrade path available in Cohere lineup)
- 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 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:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 27%
- GPQA Diamond
- 42.5%
- MMLU-Pro
- 66%
- HumanEval
- 74%
- Long-Context Reasoning
- 68%
- Chatbot Arena Elo
- 1215pts
Strengths
- Cohere की उन्नत Efficient Compact RAG Transformer आर्किटेक्चर और 128K कॉन्टेक्स्ट विंडो क्षमता
- किफायती और पारदर्शी API मूल्य ($0.07/1M इनपुट, $0.2/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
- जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Limitations & Trade-offs
- बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
- विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है
Editorial Assessment
Command R7B अपने वर्ग में Cohere का एक भरोसेमंद और शक्तिशाली AI मॉडल है जो प्रदर्शन और लागत का बेहतरीन संतुलन देता है।
Best for: एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, AI एजेंट्स और Cohere आधारित प्रोडक्शन सिस्टम
Compare Command R7B
Frequently Asked Questions about Command R7B
How much does the Command R7B API cost per 1M tokens?+
Command R7B 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 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's context window?+
Command R7B 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 locally on my own GPU (Open Weights)?+
Yes, Command R7B (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 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 perform on coding and reasoning benchmarks?+
Command R7B achieves a composite Intelligence Index of 38/100, scoring 27% on SWE-bench Verified, 42.5% on GPQA Diamond, 66% on MMLU-Pro, and 1215 pts on Chatbot Arena Elo.
What is Command R7B best used for?+
Command R7B is best suited for: एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, AI एजेंट्स और Cohere आधारित प्रोडक्शन सिस्टम. Assessment: Command R7B अपने वर्ग में Cohere का एक भरोसेमंद और शक्तिशाली AI मॉडल है जो प्रदर्शन और लागत का बेहतरीन संतुलन देता है।
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:
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.