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Codestral 2508 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 Codestral 2508 and Llama 3.3 70B Instruct.

Mistral

Codestral 2508

Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation. [Blog Post](https://mistral.ai/news/codestral-25-08)

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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

SpecificationCodestral 2508Llama 3.3 70B Instruct
ProviderMistralMeta
Context Window256,000 tokens131,072 tokens
Agent SuitabilityN/A83/100
Time to First Token (TTFT)N/A280 ms
Deployment Modelself hostableself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2025-08-012024-12-06

API Pricing Comparison

Input Price per Million Tokens

Codestral 2508

$0.30

Llama 3.3 70B Instruct

$0.10

Output Price per Million Tokens

Codestral 2508

$0.90

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
N/Avs8620.0%
Codestral 2508
Llama 3.3 70B Instruct
HumanEvalPython coding & logic synthesis
N/Avs8800.0%
Codestral 2508
Llama 3.3 70B Instruct
MATHComplex mathematical problem solving
N/Avs7500.0%
Codestral 2508
Llama 3.3 70B Instruct
GPQAGraduate-level expert reasoning
N/Avs5200.0%
Codestral 2508
Llama 3.3 70B Instruct
HellaSwagCommonsense reasoning and inference
N/Avs8850.0%
Codestral 2508
Llama 3.3 70B Instruct
MT-BenchMulti-turn conversation flow quality
N/Avs880.0%
Codestral 2508
Llama 3.3 70B Instruct

Codestral 2508 Quirks & Gotchas

No developer gotchas reported.

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