Skip to main content

Command R vs Llama 3.3 70B

Compare Cohere and Meta (via Together AI) AI models

Cohere
Command R
vs
Meta (via Together AI)
Llama 3.3 70B

Cost Comparison (1000 input + 500 output tokens, 100 requests/day)

Command R

Per Request:$0.000450
Daily:$0.045
Monthly:$1.35
Yearly:$16.425

Llama 3.3 70B

Per Request:$0.001320
Daily:$0.132
Monthly:$3.96
Yearly:$48.18

Cost Differences

+$0.000870
Per Request
+$0.087
Daily
+$2.61
Monthly
+$31.755
Yearly

Llama 3.3 70B costs more than Command R

Feature Comparison

FeatureCommand RLlama 3.3 70B
ProviderCohereMeta (via Together AI)
Input Price$0.15/1M tokens$0.88/1M tokens
Output Price$0.60/1M tokens$0.88/1M tokens
Context Window128,000 tokens131,072 tokens
Max Output4,096 tokens4,096 tokens
Categoryefficientstandard
Capabilities
textcode
textcode
Release Date3/11/202412/6/2024

Command R vs Llama 3.3 70B: Which Should You Choose?

Choosing between Command R and Llama 3.3 70B depends on your priorities: cost efficiency, context length, or raw capability. Command R is the more affordable option at $0.15/1M input tokens83% cheaper than Llama 3.3 70B. Meanwhile, Llama 3.3 70B offers a significantly larger context window at 131,072 tokens vs 128,000 for Command R.

These models come from different providers — Cohere and Meta (via Together AI) — which means different API ecosystems, SDKs, rate limits, and terms of service. If you're already integrated with Cohere, switching to Meta (via Together AI)involves migration effort beyond just pricing. Factor in your existing infrastructure when deciding.

These models target different tiers: Command R is a efficient model while Llama 3.3 70B is standard. This means they're optimized for different workloads. Llama 3.3 70B targets more demanding workloads, while Command R provides a cost-effective option for everyday tasks.

Output costs matter too. Command R charges $0.60/1M output tokens vs $0.88 for Llama 3.3 70B. For generation-heavy workloads (content creation, code generation, summarization), output pricing often dominates your bill. Command R has the edge here at $0.60/1M output tokens.

Best Use Cases

Choose Command R when:

  • • Budget is a primary concern
  • • You're already using Cohere's API ecosystem
  • • You're running high-volume, latency-sensitive workloads

Choose Llama 3.3 70B when:

  • • You need a larger context window (131,072 tokens)
  • • You're already using Meta (via Together AI)'s API ecosystem

Try Different Scenarios

Use the calculator below to see how costs change with different usage patterns

Command R (Cohere)

Llama 3.3 70B (Meta (via Together AI))

Start using Command R today

Sign Up for Cohere

Start using Llama 3.3 70B today

Sign Up for Meta (via Together AI)

Frequently Asked Questions

Which is cheaper, Command R or Llama 3.3 70B?
Command R is cheaper for input tokens at $0.15 per million tokens vs $0.88 for Llama 3.3 70B — that's 83% savings on input costs.
What is the context window difference between Command R and Llama 3.3 70B?
Command R supports 128,000 tokens while Llama 3.3 70B supports 131,072 tokens — a difference of 3,072 tokens in favor of Llama 3.3 70B.
Which model is better for AI Chatbot?
Both models support text. For ai chatbot, Command R is the lower-cost option, while Llama 3.3 70B offers a larger context window (131,072 vs 128,000 tokens). Choose Command R for budget sensitivity or Llama 3.3 70B for longer context tasks.
Which model has better overall pricing for heavy usage?
At 100 requests/day with 1,000 input and 500 output tokens each, Command R costs about $1.35/month and Llama 3.3 70B costs about $3.96/month. Overall, Command R has lower combined input + output rates ($0.15 in, $0.60 out) vs Llama 3.3 70B.

Related Comparisons

Related Articles