Command R vs Llama 3.3 70B
Command R vs Llama 3.3 70B: Command R is cheaper for input-heavy usage ($0.15/M vs $0.88/M input tokens), while Llama 3.3 70B is better for long-context tasks (131,072 tokens).
Direct answer: choose Command R for lower token spend and choose Llama 3.3 70B when your workload needs longer context.
Compare input and output token pricing, context windows, and monthly cost estimates on one page so you can pick the cheaper model fast.
Cost Comparison (1000 input + 500 output tokens, 100 requests/day)
Command R
Llama 3.3 70B
Cost Differences
Llama 3.3 70B costs more than Command R
Quick Recommendation
Winner for direct API pricing: Command R. At the default workload, Command R saves about $2.61/month ($31.755/year) versus Llama 3.3 70B.
Feature Comparison
| Feature | Command R | Llama 3.3 70B |
|---|---|---|
| Provider | Cohere | Meta (via Together AI) |
| Input Price | $0.15/1M tokens | $0.88/1M tokens |
| Output Price | $0.60/1M tokens | $0.88/1M tokens |
| Context Window | 128,000 tokens | 131,072 tokens |
| Max Output | 4,096 tokens | 4,096 tokens |
| Category | efficient | standard |
| Capabilities | textcode | textcode |
| Release Date | 3/11/2024 | 12/6/2024 |
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 tokens — 83% 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
Pros and Caveats at a Glance
Command R
- • Input pricing: $0.15/M tokens
- • Output pricing: $0.60/M tokens
- • Context window: 128,000 tokens
- • Max output: 4,096 tokens
Watch out for
- • Smaller context window than Llama 3.3 70B
Llama 3.3 70B
- • Input pricing: $0.88/M tokens
- • Output pricing: $0.88/M tokens
- • Context window: 131,072 tokens
- • Max output: 4,096 tokens
Watch out for
- • Higher input cost than Command R
- • Higher output cost than Command R
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
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Sign Up for Meta (via Together AI) →Frequently Asked Questions
Which is cheaper, Command R or Llama 3.3 70B?▼
What is the context window difference between Command R and Llama 3.3 70B?▼
Which model is better for AI Chatbot?▼
Which model has better overall pricing for heavy usage?▼
Where can I compare Cohere and Meta (via Together AI) API pricing beyond this model matchup?▼
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