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DeepSeek R1 V3.2 vs Llama 3.3 70B

Compare DeepSeek and Meta (via Together AI) AI models

DeepSeek
DeepSeek R1 V3.2
vs
Meta (via Together AI)
Llama 3.3 70B

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

DeepSeek R1 V3.2

Per Request:$0.000490
Daily:$0.049
Monthly:$1.47
Yearly:$17.885

Llama 3.3 70B

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

Cost Differences

+$0.000830
Per Request
+$0.083
Daily
+$2.49
Monthly
+$30.295
Yearly

Llama 3.3 70B costs more than DeepSeek R1 V3.2

Feature Comparison

FeatureDeepSeek R1 V3.2Llama 3.3 70B
ProviderDeepSeekMeta (via Together AI)
Input Price$0.28/1M tokens$0.88/1M tokens
Output Price$0.42/1M tokens$0.88/1M tokens
Context Window128,000 tokens131,072 tokens
Max Output65,536 tokens4,096 tokens
Categoryreasoningstandard
Capabilities
textreasoningcode
textcode
Release Date1/20/202512/6/2024

DeepSeek R1 V3.2 vs Llama 3.3 70B: Which Should You Choose?

Choosing between DeepSeek R1 V3.2 and Llama 3.3 70B depends on your priorities: cost efficiency, context length, or raw capability. DeepSeek R1 V3.2 is the more affordable option at $0.28/1M input tokens68% 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 DeepSeek R1 V3.2.

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

These models target different tiers: DeepSeek R1 V3.2 is a reasoning model while Llama 3.3 70B is standard. This means they're optimized for different workloads. DeepSeek R1 V3.2 is built for complex tasks that require deeper reasoning, while Llama 3.3 70B offers better value for routine operations.

Output costs matter too. DeepSeek R1 V3.2 charges $0.42/1M 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. DeepSeek R1 V3.2 has the edge here at $0.42/1M output tokens.

Best Use Cases

Choose DeepSeek R1 V3.2 when:

  • • Budget is a primary concern
  • • You need more capabilities (reasoning)
  • • You need longer outputs (up to 65,536 tokens)
  • • You're already using DeepSeek's API ecosystem

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

DeepSeek R1 V3.2 (DeepSeek)

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

Start using DeepSeek R1 V3.2 today

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Start using Llama 3.3 70B today

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Frequently Asked Questions

Which is cheaper, DeepSeek R1 V3.2 or Llama 3.3 70B?
DeepSeek R1 V3.2 is cheaper for input tokens at $0.28 per million tokens vs $0.88 for Llama 3.3 70B — that's 68% savings on input costs.
What is the context window difference between DeepSeek R1 V3.2 and Llama 3.3 70B?
DeepSeek R1 V3.2 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, DeepSeek R1 V3.2 is the lower-cost option, while Llama 3.3 70B offers a larger context window (131,072 vs 128,000 tokens). Choose DeepSeek R1 V3.2 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, DeepSeek R1 V3.2 costs about $1.47/month and Llama 3.3 70B costs about $3.96/month. Overall, DeepSeek R1 V3.2 has lower combined input + output rates ($0.28 in, $0.42 out) vs Llama 3.3 70B.

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