Large Language ModelText GenerationLlama 3.1 FamilyEnriched

Llama 3.1 70B

Llama 3.1 70B is a large-scale open-weight language model from Meta designed to provide near frontier-level performance in reasoning, coding, and general-purpose assistance while remaining efficient enough for production deployment. It supports long-context understanding and strong multilingual capabilities, and is intended for both research and commercial use under the Llama 3.1 Community License.

by MetaReleased 2024-07-23Llama 3.1 Community
Parameters
70B
MMLU
86.4%
HumanEval
67.0%
API Access
Available

Key Capabilities

  • +Strong general-purpose chat and instruction following
  • +High-quality code generation and debugging across multiple languages
  • +Competitive performance on math and logical reasoning tasks
  • +Multilingual understanding and generation
  • +Long-context comprehension and summarization
  • +Tool and API calling when integrated into an agent stack
  • +Suitable for fine-tuning and domain adaptation via open weights

Limitations

  • -Still lags top proprietary frontier models on the hardest reasoning and safety benchmarks
  • -Can hallucinate facts, code, or citations, especially outside its training distribution
  • -Safety and alignment depend heavily on downstream deployment, guardrails, and fine-tuning
  • -Context window can lose fidelity for very early details in extremely long prompts
  • -Official Meta APIs do not yet expose native fine-tuning; requires custom infra or third-party tooling

Benchmark Performance

reasoning

reasoning

Massive Multitask Language Understanding

83.6%
reasoning

BIG-Bench Hard

85.6%
reasoningsource

MMLU Professional

61.0%
reasoningsource

HellaSwag

88.5%
reasoningsource

WinoGrande

87.2%

coding

coding

HumanEval

80.5%
codingsource

Mostly Basic Python Problems

78.0%

math

math

Grade School Math 8K

95.1%
math

MATH

68.0%

conversation

conversation

Chatbot Arena Elo

1247.0Elo
conversation

Multi-Turn Benchmark

9.1score

Alternatives & Comparisons

Closed-weight frontier model with stronger overall performance and multimodal support but no self-hosting.

Strengths
  • + State-of-the-art performance on many public benchmarks
  • + Rich ecosystem, plugins, and tooling
Weaknesses
  • - Proprietary and closed weights
  • - Higher and more complex pricing at scale

Highly capable proprietary model with strong reasoning and safety focus, but closed weights and no self-hosting.

Strengths
  • + Very strong reasoning and writing quality
  • + Robust safety and alignment work
Weaknesses
  • - Closed-source and not self-hostable
  • - Dependent on Anthropic’s API availability and pricing

Smaller, more efficient sibling model optimized for edge and low-latency use cases with lower raw capability.

Strengths
  • + Much cheaper and faster to run
  • + Easier to deploy on a single GPU or small cluster
Weaknesses
  • - Lower peak performance on complex reasoning and coding
  • - Weaker long-context and multilingual performance