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Codestral vs Llama 3.3 70B

Compare Mistral AI and Meta (via Together AI) AI models

Mistral AI
Codestral
vs
Meta (via Together AI)
Llama 3.3 70B

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

Codestral

Per Request:$0.000750
Daily:$0.075
Monthly:$2.25
Yearly:$27.375

Llama 3.3 70B

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

Cost Differences

+$0.000570
Per Request
+$0.057
Daily
+$1.71
Monthly
+$20.805
Yearly

Llama 3.3 70B costs more than Codestral

Feature Comparison

FeatureCodestralLlama 3.3 70B
ProviderMistral AIMeta (via Together AI)
Input Price$0.30/1M tokens$0.88/1M tokens
Output Price$0.90/1M tokens$0.88/1M tokens
Context Window128,000 tokens131,072 tokens
Max Output32,768 tokens4,096 tokens
Categorybalancedstandard
Capabilities
textcode
textcode
Release Date7/30/202512/6/2024

Codestral vs Llama 3.3 70B: Which Should You Choose?

Choosing between Codestral and Llama 3.3 70B depends on your priorities: cost efficiency, context length, or raw capability. Codestral is the more affordable option at $0.30/1M input tokens66% 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 Codestral.

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

These models target different tiers: Codestral is a balanced 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 Codestral provides a cost-effective option for everyday tasks.

Output costs matter too. Codestral charges $0.90/1M 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. Codestral has the edge here at $0.90/1M output tokens.

Best Use Cases

Choose Codestral when:

  • • Budget is a primary concern
  • • You need longer outputs (up to 32,768 tokens)
  • • You're already using Mistral AI'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

Codestral (Mistral AI)

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

Start using Codestral today

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Start using Llama 3.3 70B today

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Frequently Asked Questions

Which is cheaper, Codestral or Llama 3.3 70B?
Codestral is cheaper for input tokens at $0.30 per million tokens vs $0.88 for Llama 3.3 70B — that's 66% savings on input costs.
What is the context window difference between Codestral and Llama 3.3 70B?
Codestral 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, Codestral is the lower-cost option, while Llama 3.3 70B offers a larger context window (131,072 vs 128,000 tokens). Choose Codestral 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, Codestral costs about $2.25/month and Llama 3.3 70B costs about $3.96/month. Overall, Codestral has lower combined input + output rates ($0.30 in, $0.90 out) vs Llama 3.3 70B.

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