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Llama 3.3 70B

Metabudget

Meta's 70B dense model from the Llama 3.3 generation. Still widely used for self-hosted deployments due to its straightforward dense architecture, strong fine-tuning ecosystem, and proven reliability. Not the most capable model anymore, but the most battle-tested open-weight option with massive community support.

Released 2024-12-06Knowledge cutoff: 2024-10
Needs review|Updated 114d ago|90% source confidence

Specifications

Context Window

128K tokens

Max Output

16.4K tokens

Input Price

$0.180 / 1M tokens

Output Price

$0.180 / 1M tokens

Latency Tier

Fast (speed score: 8/10)

Capability Profile

Cost Efficiency
8.5/10
Speed
8/10
Reasoning
7/10
Coding
7/10
Factuality
7/10
Instruction Following
7/10
Conversational
7/10
Structured Output
6.5/10
Creativity
6.5/10
Safety & Enterprise
6.5/10
Long Context
6/10
Tool Use
6/10
Multimodal
1/10

Feature Support

Vision No
Audio In No
Audio Out No
Video No
Image Generation No
Image Editing No
Function Calling Yes
JSON Mode Yes
Structured Output No
Streaming Yes
Reasoning No
Realtime No
Computer Use No
Web Search No

Best Use Cases

Self-hosted deployments where proven reliability matters more than cutting-edge capability
Fine-tuning for domain-specific applications — the largest fine-tuning ecosystem of any open model
Budget inference through hosted providers at rock-bottom pricing
Applications requiring a dense (non-MoE) architecture for simpler deployment
Teams already invested in Llama 3 tooling and infrastructure

Not Ideal For

Tasks requiring frontier-level quality (use Llama 4 Maverick or closed-source)
Multimodal tasks (text only)
Applications needing the longest context windows
Enterprise deployments requiring the strongest safety alignment

Strengths

Most mature fine-tuning ecosystem — thousands of community fine-tunes available
Dense architecture is simpler to deploy than MoE models
Self-hostable on 2 A100 GPUs or quantized on a single H100
Rock-bottom hosted pricing at ~$0.18/M tokens
Proven reliability from over a year of production deployments
Compatible with virtually every inference framework (vLLM, TGI, Ollama, llama.cpp, etc.)

Weaknesses

Outperformed by Llama 4 Scout (MoE) on most benchmarks at similar cost
Text-only — no multimodal support
128K context is modest by current standards
Instruction following is noticeably weaker than current-gen models
Knowledge cutoff is getting stale
Will eventually be deprecated in favor of Llama 4 models

Edge Cases & Notes

Still the #1 choice for teams that need dense-architecture simplicity over MoE efficiency
Massive community of fine-tunes means you can likely find a domain-specific variant
128K context claim is optimistic — quality degrades past 32K in practice
Quantized versions (4-bit) can run on consumer GPUs but with quality loss

Provider Notes

Available through Together AI, Fireworks, Replicate, Ollama, and self-hosted. The most widely deployed open model. Consider migrating to Llama 4 Scout for new projects unless dense architecture is specifically needed.

Benchmarks

MMLU86%
HumanEval84.5%
Arena Elo1235
GSM8K91%

Benchmark Notes

MMLU 86%. HumanEval 84.5%. Solid for a 70B dense model. GSM8K 91% shows good mathematical ability. Outperformed by newer MoE models but still competitive for its simplicity and maturity.

Research Meta

Last Evaluated

2026-02-01

Source Confidence

90%

Evaluation Method

Open LLM Leaderboard, LMSYS Arena, community fine-tune evaluations, self-hosting benchmarks

Needs Re-evaluation

No

Sources

  • Meta Llama 3.3 technical report
  • Open LLM Leaderboard
  • LMSYS Chatbot Arena
  • Community benchmarks and evaluations

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