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Command R vs Llama 3.3 70B Instruct

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Command R and Llama 3.3 70B Instruct.

Cohere

Command R

Command R is Cohere's lightweight, cost-efficient model engineered for high-speed enterprise integrations, productivity automation, and retrieval-augmented generation (RAG) pipelines. Optimized for low-latency API tool use and structured JSON output, it is particularly effective in enterprise search and question-answering systems where fast, reliable responses are critical. With a 128,000-token context window and a price of $0.15/MTok for input, Command R provides strong RAG performance and multilingual support at a fraction of the cost of Command R+, making it the preferred choice for teams scaling intelligent document retrieval at high request volumes.

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Meta

Llama 3.3 70B Instruct

Meta's state-of-the-art open weights model, providing enterprise-grade reasoning and logic. Exceptionally powerful for self-hosted customer support, text generation, and tooling workflows.

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Technical Specifications

SpecificationCommand RLlama 3.3 70B Instruct
ProviderCohereMeta
Context Window128,000 tokens131,072 tokens
Agent Suitability78/10083/100
Time to First Token (TTFT)200 ms280 ms
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-03-112024-12-06

API Pricing Comparison

Input Price per Million Tokens

Command R

$0.15

Llama 3.3 70B Instruct

$0.10

Output Price per Million Tokens

Command R

$0.60

Llama 3.3 70B Instruct

$0.32

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
7100.0%vs8620.0%
Command R
Llama 3.3 70B Instruct
HumanEvalPython coding & logic synthesis
7300.0%vs8800.0%
Command R
Llama 3.3 70B Instruct
MATHComplex mathematical problem solving
5400.0%vs7500.0%
Command R
Llama 3.3 70B Instruct
GPQAGraduate-level expert reasoning
3500.0%vs5200.0%
Command R
Llama 3.3 70B Instruct
HellaSwagCommonsense reasoning and inference
7800.0%vs8850.0%
Command R
Llama 3.3 70B Instruct
MT-BenchMulti-turn conversation flow quality
750.0%vs880.0%
Command R
Llama 3.3 70B Instruct

Command R Quirks & Gotchas

  • โ–ธCost-effective RAG model โ€” strong multilingual search performance
  • โ–ธLimited agentic capability โ€” use Command R+ for complex multi-step tool use

Llama 3.3 70B Instruct Quirks & Gotchas

  • โ–ธStable, well-documented self-hosted option with strong community support
  • โ–ธOutperformed by Llama 4 Maverick for agentic tool-calling workflows