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Llama 3.3 70B Instruct

by Meta · Released Dec 2024

Open Source
47.9
avg score
Rank #157
Compare
Better than 50% of all models
Context
131K tokens (~66 books)
Input $/1M
$0.10
Output $/1M
$0.32
Type
text
License
Open Source
Benchmarks
18 tested
Data as of
About

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model...

Tested on 18 benchmarks · BenchGecko score 47.9. Top scores: Chatbot Arena Elo — Overall (1317.8%), IFEval (90.0%), MMLU (81.7%).

Looking for similar performance at lower cost?
Gemma 4 26B A4B scores 47.8 (100% as good) at $0.07/1M input · 32% cheaper
Capabilities
coding
36.9
#147 globally
reasoning
9.7
#181 globally
math
31.7
#182 globally
knowledge
37.8
#212 globally
general
36.5
#89 globally
language
90.0
#14 globally
Benchmark Scores
Compare All
Tested on 18 benchmarks · Ranked across 7 categories
Score Distribution (all 312 models)
0255075100
▲ You are here
Aider — Code Editing

Code editing benchmark from the Aider project. Measures ability to apply targeted code changes while maintaining correctness and style.

59.4·
WeirdML

Unusual and adversarial machine learning challenges. Tests robustness of reasoning about edge cases in ML systems.

14.4·
MUSR

HuggingFace MuSR (Multi-Step Reasoning). Tests multi-hop reasoning requiring chaining multiple facts together.

15.6·
SimpleBench

Deceptively simple questions that humans find easy but AI models often get wrong. Tests common sense and reasoning gaps.

3.9·
MATH Level 5

HuggingFace evaluation of MATH Level 5 problems. Competition math requiring advanced reasoning and proof construction.

48.3·
MATH level 5

Competition-level math from AMC, AIME, and olympiad problems. Level 5 is the hardest tier, requiring creative problem-solving.

41.6·
OTIS Mock AIME 2024-2025

Mock AIME (American Invitational Mathematics Exam) problems from OTIS. Tests mathematical competition performance.

5.0·
Excellent (85+) Good (70-85) Average (50-70) Below (<50)
Recently Happened
Llama 3.3 70B Instruct pricing dropped 86%
Sep 3, 2026
Llama 3.3 70B Instruct pricing increased 610%
Aug 26, 2026
Llama 3.3 70B Instruct pricing dropped 17%
Apr 22, 2026
Llama 3.3 70B Instruct pricing increased 20%
Apr 19, 2026
Links
Documentation
Community
BenchGecko API
llama-3-3-70b-instruct
Specifications
  • Typetext
  • Context131K tokens (~66 books)
  • ReleasedDec 2024
  • LicenseOpen Source
  • StatusActive
  • Cost / Message~$0.001
Available On
Meta logoMeta$0.10
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Llama 3.3 70B Instruct is an open-source text AI model by Meta, released in December 2024. It has an average benchmark score of 47.9. Context window: 131K tokens.

Key facts · as of 2026-10-05

  • Llama 3.3 70B Instruct by Meta. BenchGecko score 47.9, rank 157 of 312 scored models (normalized average of public benchmark scores).
  • List price $0.10 input · $0.32 output per 1M tokens (as of 2026-10-05).
  • Sold by 11 providers (as of 2026-10-05): DeepInfra (fp8) $0.10 in / $0.32 out · Novita (bf16) $0.14 in / $0.40 out · AkashML (fp8) $0.20 in / $0.52 out · Parasail (fp8) $0.22 in / $0.50 out · Cloudflare (fp8) $0.29 in / $2.25 out · and 6 more. Every provider

How to cite · data as of 2026-10-05

Llama 3.3 70B Instruct · benchmarks, pricing and providers. BenchGecko, data as of 2026-10-05. https://benchgecko.ai/model/llama-3-3-70b-instruct

Credit "Source: BenchGecko" with a link. Prices per provider and Gecko Tests are BenchGecko data (CC BY 4.0); benchmark scores keep their original source, listed in the JSON. JSON · llms.txt · MCP