Llama 3.3 70B Instruct
Llama 3.3 Community LicenseDeveloped by Meta · Released 2025-01-01
What are the token costs and operational benchmarks for Llama 3.3 70B Instruct?
Architectural Overview
Frontier foundation model developed by Meta featuring Dense Transformer architecture.
Optimal Production Use Cases
Self-hosted and private enterprise multilingual dialogue, agentic tool-calling, and scalable RAG pipelines.
Interactive Monthly Token Economics & ROI Forecaster
Model your expected production workload across prompt (input) and completion (output) tokens.
Estimated Monthly Spend
$0.18/1M in · $0.4/1M out
$0.18/1M in · $0.72/1M out
Head-to-Head Comparisons Involving Llama 3.3 70B Instruct
Llama 3.3 70B Instruct vs Claude 3.5 Haiku
Llama 3.3 70B Instruct is 77.5% cheaper for input tokens ($0.18 vs. $0.80 per 1M tokens) and $0.40 vs. $4.00 for output tokens (4.4x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (280ms faster than Llama 3.3 70B Instruct). Claude 3.5 Haiku leads coding benchmarks at 40.6% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Claude 3.5 Sonnet
Llama 3.3 70B Instruct is 94.0% cheaper for input tokens ($0.18 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (16.7x cost difference). In terms of operational performance, Claude 3.5 Sonnet delivers faster response latency with 320ms TTFT (100ms faster than Llama 3.3 70B Instruct). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Claude 3.7 Sonnet
Llama 3.3 70B Instruct is 94.0% cheaper for input tokens ($0.18 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (16.7x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (230ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Claude Opus 5
Llama 3.3 70B Instruct is 96.4% cheaper for input tokens ($0.18 vs. $5.00 per 1M tokens) and $0.40 vs. $25.00 for output tokens (27.8x cost difference). In terms of operational performance, Claude Opus 5 delivers faster response latency with 340ms TTFT (80ms faster than Llama 3.3 70B Instruct). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Codestral 25.01
Llama 3.3 70B Instruct is 40.0% cheaper for input tokens ($0.18 vs. $0.30 per 1M tokens) and $0.40 vs. $0.90 for output tokens (1.7x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (270ms faster than Llama 3.3 70B Instruct). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Composer 2.5
Llama 3.3 70B Instruct is 90.0% cheaper for input tokens ($0.18 vs. $1.80 per 1M tokens) and $0.40 vs. $7.20 for output tokens (10.0x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (240ms faster than Llama 3.3 70B Instruct). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs DeepSeek-R1
Llama 3.3 70B Instruct is 67.3% cheaper for input tokens ($0.18 vs. $0.55 per 1M tokens) and $0.40 vs. $2.19 for output tokens (3.1x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (1380ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs DeepSeek-V3
DeepSeek-V3 is 22.2% cheaper for input tokens ($0.14 vs. $0.18 per 1M tokens) and $0.28 vs. $0.40 for output tokens (1.3x cost difference). In terms of operational performance, DeepSeek-V3 delivers faster response latency with 340ms TTFT (80ms faster than Llama 3.3 70B Instruct). DeepSeek-V3 leads coding benchmarks at 42.0% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs DeepSeek-V4 Flash
DeepSeek-V4 Flash is 22.2% cheaper for input tokens ($0.14 vs. $0.18 per 1M tokens) and $0.56 vs. $0.40 for output tokens (1.3x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (270ms faster than Llama 3.3 70B Instruct). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Fable 5
Llama 3.3 70B Instruct is 91.0% cheaper for input tokens ($0.18 vs. $2.00 per 1M tokens) and $0.40 vs. $8.00 for output tokens (11.1x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (180ms faster than Llama 3.3 70B Instruct). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Gemini 2.0 Flash
Gemini 2.0 Flash is 44.4% cheaper for input tokens ($0.10 vs. $0.18 per 1M tokens) and $0.40 vs. $0.40 for output tokens (1.8x cost difference). In terms of operational performance, Gemini 2.0 Flash delivers faster response latency with 380ms TTFT (40ms faster than Llama 3.3 70B Instruct). Gemini 2.0 Flash leads coding benchmarks at 48.0% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Gemini 3.7 Flash
Gemini 3.7 Flash is 55.6% cheaper for input tokens ($0.08 vs. $0.18 per 1M tokens) and $0.32 vs. $0.40 for output tokens (2.2x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (345ms faster than Llama 3.3 70B Instruct). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs GLM 5.3 Flash
GLM 5.3 Flash is 16.7% cheaper for input tokens ($0.15 vs. $0.18 per 1M tokens) and $0.50 vs. $0.40 for output tokens (1.2x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (290ms faster than Llama 3.3 70B Instruct). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs OpenAI GPT-4o
Llama 3.3 70B Instruct is 92.8% cheaper for input tokens ($0.18 vs. $2.50 per 1M tokens) and $0.40 vs. $10.00 for output tokens (13.9x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (140ms faster than Llama 3.3 70B Instruct). Both models demonstrate comparable coding benchmark scores.
Llama 3.3 70B Instruct vs GPT-5.6 Luna
Both models share identical input pricing at $0.18 per 1M tokens. In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (330ms faster than Llama 3.3 70B Instruct). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs GPT-5.6 Sol
Llama 3.3 70B Instruct is 97.8% cheaper for input tokens ($0.18 vs. $8.00 per 1M tokens) and $0.40 vs. $32.00 for output tokens (44.4x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (0ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs GPT-5.6 Terra
Llama 3.3 70B Instruct is 88.0% cheaper for input tokens ($0.18 vs. $1.50 per 1M tokens) and $0.40 vs. $6.00 for output tokens (8.3x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (210ms faster than Llama 3.3 70B Instruct). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Grok 3
Llama 3.3 70B Instruct is 94.0% cheaper for input tokens ($0.18 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (16.7x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (430ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs xAI Grok 4.6
Llama 3.3 70B Instruct is 91.0% cheaper for input tokens ($0.18 vs. $2.00 per 1M tokens) and $0.40 vs. $6.00 for output tokens (11.1x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (140ms faster than Llama 3.3 70B Instruct). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Mistral Large 2
Llama 3.3 70B Instruct is 91.0% cheaper for input tokens ($0.18 vs. $2.00 per 1M tokens) and $0.40 vs. $6.00 for output tokens (11.1x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (130ms faster than Mistral Large 2). Mistral Large 2 leads coding benchmarks at 39.0% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs OpenAI o1
Llama 3.3 70B Instruct is 98.8% cheaper for input tokens ($0.18 vs. $15.00 per 1M tokens) and $0.40 vs. $60.00 for output tokens (83.3x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (430ms faster than OpenAI o1). OpenAI o1 leads coding benchmarks at 48.9% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs o3-mini
Llama 3.3 70B Instruct is 83.6% cheaper for input tokens ($0.18 vs. $1.10 per 1M tokens) and $0.40 vs. $4.40 for output tokens (6.1x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (780ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 33.3% cheaper for input tokens ($0.12 vs. $0.18 per 1M tokens) and $0.36 vs. $0.40 for output tokens (1.5x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (310ms faster than Llama 3.3 70B Instruct). Microsoft Phi-4 (14B) leads coding benchmarks at 42.1% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Qwen 2.5 72B Instruct
Llama 3.3 70B Instruct is 48.6% cheaper for input tokens ($0.18 vs. $0.35 per 1M tokens) and $0.40 vs. $0.40 for output tokens (1.9x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (0ms faster than Llama 3.3 70B Instruct). Qwen 2.5 72B Instruct leads coding benchmarks at 44.0% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Qwen 2.5 Max
Llama 3.3 70B Instruct is 35.7% cheaper for input tokens ($0.18 vs. $0.28 per 1M tokens) and $0.40 vs. $0.84 for output tokens (1.6x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (60ms faster than Qwen 2.5 Max). Qwen 2.5 Max leads coding benchmarks at 44.2% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 33.3% cheaper for input tokens ($0.12 vs. $0.18 per 1M tokens) and $0.48 vs. $0.40 for output tokens (1.5x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (300ms faster than Llama 3.3 70B Instruct). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Frequently Asked Questions & Query Fan-Out
How much does Llama 3.3 70B Instruct cost per 1M tokens?
Llama 3.3 70B Instruct costs $0.18 per million prompt (input) tokens and $0.40 per million completion (output) tokens.
What is the context window for Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct supports a maximum context window of 131,072 tokens, with a maximum single-generation output of 8,192 tokens.
What are the primary use cases for Llama 3.3 70B Instruct?
Self-hosted and private enterprise multilingual dialogue, agentic tool-calling, and scalable RAG pipelines.