What is the difference between Llama 3.3 70B and Mistral Large 2?

Llama 3.3 70B (Meta) features 70B Dense with a 128k context window, while Mistral Large 2 (Mistral AI) features 123B Dense with a 128k context window.

Which is cheaper for production inference, Llama 3.3 70B or Mistral Large 2?

Llama 3.3 70B input pricing is $0 per 1M tokens vs Mistral Large 2 at $2 per 1M tokens.

Can I run Llama 3.3 70B or Mistral Large 2 locally on private GPU hardware?

Llama 3.3 70B is an open-weights model and can be hosted locally using Ollama or vLLM. Mistral Large 2 is a closed cloud API.

Back to LLM Benchmark & Hardware Sizing Engine

MetavsMistral AI

Llama 3.3 70B vs Mistral Large 2

Side-by-side technical showdown between Llama 3.3 70B and Mistral Large 2 on TheModelverse. Compare verified LLM benchmark scores, quantization compression, local GPU hardware sizing, and API inference pricing.

Executive Showdown & Winner Breakdown

Who Wins Where: Llama 3.3 70B vs Mistral Large 2

Verified across benchmarks, pricing & local VRAM footprint

Reasoning & STEM Intelligence

+25.9% lead

Leader:Mistral Large 2(54.0 vs 68.0)

Mistral Large 2 outperforms in advanced scientific and math problem-solving benchmarks.

Software Engineering & Code

+12.6% lead

Leader:Mistral Large 2(81.7 vs 92.0)

Mistral Large 2 demonstrates higher code generation accuracy and agentic bug resolution.

API Cost & Token Economics

100% cheaper

Leader:Llama 3.3 70B($0.00 vs $2.00 / 1M in)

Llama 3.3 70B delivers significantly lower input/output token pricing for high-throughput production.

Local Portability & Sovereignty

Self-Hostable

Leader:Llama 3.3 70B(Open Weights vs Closed API)

Llama 3.3 70B can be deployed on private GPUs, Ollama, and on-prem clusters without third-party vendor lock-in.

Hardware Sizing & Model Compression

LLM Hardware Sizing & Quantization Sizing

Simulate weight compression levels and dynamic KV-cache expansion to verify whether these models fit on your local hardware or cloud GPU cluster.

Quantization Level

Active Precision

4 bits / parameter

VRAM Reduction

-72.5% vs FP16

Benchmark Retention

96–98% (Sweet Spot)

Context Simulator

Total VRAM Footprint @ 8k Context (INT4 (GGUF / AWQ))

Model 1Open Weights

Llama 3.3 70B

44.8GB

Weights: ~38.5 GBKV Cache: ~0.4 GB+15% CUDA Buffer

Total Params:

70B Dense

Active Compute:

Dense

Attention Scheme:

GQA (Grouped-Query)

Max Context:

128k tokens

Target Hardware: 1x NVIDIA A100 / H100 (80GB)

Model 2Cloud Hosted

Mistral Large 2

Zero Local VRAM

Inference runs fully on provider cloud infrastructure. Zero GPU required locally.

Total Params:

123B Dense

Active Compute:

Dense

Attention Scheme:

GQA / Multi-Head

Max Context:

128k tokens

Target Hardware: Zero Local VRAM (Managed Cloud API)

GPU & Hardware Tier Compatibility Matrix

Hardware Configuration	Available VRAM	Llama 3.3 70B	Mistral Large 2
16 GB VRAM RTX 4070 / 4080 (16GB), Mac M-Series (16GB)	16 GB	✕ OOM	Cloud API
24 GB VRAM 1x RTX 3090 / 4090 (24GB), Mac M-Series (32GB)	24 GB	✕ OOM	Cloud API
48 GB VRAM 2x RTX 4090 (TP=2), 1x L40S, Mac M-Series (64GB)	48 GB	Tight (Low Ctx)	Cloud API
80 GB VRAM 1x NVIDIA A100 / H100 (80GB), Mac Studio (128GB)	80 GB	Optimal	Cloud API
160 GB Node 2x H100 (TP=2), 4x L40S, Mac Studio (192GB)	160 GB	Optimal	Cloud API
Multi-Node Cluster 4x–8x H100 Datacenter Cluster	320 GB	Optimal	Cloud API

LLM Benchmark Database

LLM Benchmark Showdown & Head-to-Head Deltas

Normalized evaluation scores across code generation, advanced reasoning, mathematics, and multidisciplinary exams.

GPQA Diamond (Hard Reasoning)Score % / Points

Llama 3.3 70B

54.0

Mistral Large 2

—

MATH-500 (Mathematics)Score % / Points

Llama 3.3 70B

+5.0 pts73.0

Mistral Large 2

68.0

MMLU-Pro (Multitask Knowledge)Score % / Points

Llama 3.3 70B

+2.0 pts86.0

Mistral Large 2

84.0

HumanEval (Python Code)Score % / Points

Llama 3.3 70B

81.7

Mistral Large 2

+10.3 pts92.0

Inference Economics & Cost Simulator

Token Pricing & Scale Economics

Calculate estimated monthly cloud API spend and evaluate the breakeven point vs self-hosted GPU hardware.

Monthly Queries: 100,000

10k500k1M

Avg Prompt Tokens: 1000

Avg Output Tokens: 300

Pricing Metric	Llama 3.3 70B	Mistral Large 2
Input Cost (/1M tokens)	$0.00	$2.00
Cached Input (/1M tokens)	—	—
Output Cost (/1M tokens)	$0.00	$6.00
Simulated Monthly Bill (100,000 calls)	$0 API Cost Open Weights	$380.00 ~$3.80 / 1k queries
Self-Hosted Breakeven vs $864/mo GPU	Self-Hostable Day 1	Cloud API is more cost effective

Architecture Matrix

Feature	Llama 3.3 70B	Mistral Large 2
Release Date	12/6/2024	7/24/2024
Routing / MoE	Dense	Dense
Attention Mechanism	Standard / GQA	Standard / GQA
Source Type	Open Weights (Llama Community License)	Proprietary

Customize or Add a 3rd Model to this Showdown

More Head-to-Head Comparisons for Llama 3.3 70B & Mistral Large 2

DeepSeek-V3vsLlama 3.3 70B Llama 3.3 70BvsGPT-4o Mistral Large 2vsGPT-4o DeepSeek-R1-Distill-Qwen-32BvsLlama 3.3 70B