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Direct Head-to-Head Benchmark

Llama 3.3 70B vs DeepSeek V3 API Pricing & Speed Benchmark (2026)

Compare Meta Llama 3.3 70B ($0.59/$0.79 on Groq) vs DeepSeek V3 ($0.27/$1.10): open-weights architectures, 300+ tok/s LPUs, and self-hosting VRAM costs.

Candidate A

Llama 3.3 70B (Groq)

$0.59 in / $0.79 out
Per 1M Tokens
VS
Candidate B

DeepSeek V3

$0.27 in / $1.10 out
Per 1M Tokens

Head-to-Head Monthly Cost Simulator

Simulate real workload expenditures for Llama 3.3 70B (Groq) vs DeepSeek V3 at your expected token volume.

Monthly Input Tokens: 50,000,000
Monthly Output Tokens: 10,000,000
Llama 3.3 70B (Groq): $0.00
DeepSeek V3: $0.00
Difference: $0.00

Comprehensive Technical & Financial Breakdown

Side-by-side audit of pricing tiers, token speeds, benchmark intelligence, and architecture limits.

Specification / Metric Llama 3.3 70B (Groq) DeepSeek V3 Financial & Engineering Impact
Input Price / 1M $0.59 $0.27 DeepSeek V3 is 54% cheaper
Output Price / 1M $0.79 $1.10 Llama 3.3 70B is 28% cheaper on output
Throughput Speed 300+ tok/s (Groq LPU) 65 tok/s (GPU) Groq LPUs deliver 4.6x faster output
Architecture 70B Dense Transformer 671B MoE (37B active) DeepSeek activates fewer params per token
Context Window 128,000 tokens 64,000 tokens Llama 3.3 offers 2x larger context
Self-Hosting Ease Fits on 2x A100 (80GB) Requires 8x H100 node Llama 3.3 70B is dramatically easier to self-host
Engineering Architecture Verdict

Choose **Llama 3.3 70B on Groq LPUs** when real-time latency is critical (voice AI, real-time code autocompletion, interactive chats) or when self-hosting on manageable GPU clusters. Choose **DeepSeek V3** for complex reasoning and large-scale bulk processing where prompt caching drives input costs down to 7 cents per million tokens.

Frequently Asked Questions: Llama 3.3 70B (Groq) vs DeepSeek V3

Real-world answers covering token budgeting, prompt caching, latency, and SLA differences.

Which model is cheaper for long prompt pipelines?

DeepSeek V3 with prompt caching is significantly cheaper ($0.07/M cached read vs $0.59/M on Groq).

Can Llama 3.3 70B be run on a single machine?

Yes, in 4-bit quantization (AWQ or EXL2), Llama 3.3 70B runs on dual consumer RTX 3090/4090 GPUs (48GB VRAM total) or a single Mac Studio with unified memory.