Llama 2 (70B) von Meta ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 4K Token zu API-Preisen von $0.4/1M Input- und $0.8/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
Technical Specifications
- Provider
- Meta
- Category
- historical
- Release Date
- 2023-07
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Fixed
- Structured Outputs
- No
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- meta-llama/Llama-2-(70B)
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Legacy Stable (Upgrade path available in Meta lineup)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($0.4/1M in) across full 4K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.1/1M tokens
- Computer Use & Browser GUI Agent
- Text-only completion (No GUI action loop)
- MCP & Parallel Tool Calling
- Prompt-based JSON extraction only
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Llama 2 (70B) Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~370 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Llama 2 (70B):
Benchmark Scores & 6-Axis Capability Radar
- MMLU-Pro
- 56.8%
- HumanEval
- 37.8%
- Chatbot Arena Elo
- 1105pts
Strengths
- Fortschrittliche Grouped-Query Attention (GQA) Transformer-Architektur von Meta mit 4K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0.4/1M Input, $0.8/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Limitations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Editorial Assessment
Llama 2 (70B) von Meta überzeugt durch eine ausgewogene Kombination aus Intelligenz, Geschwindigkeit und Kosteneffizienz.
Best for: Unternehmensautomatisierung, Software-Engineering, autonome KI-Agenten und Meta-Produktionssysteme
Compare Llama 2 (70B)
Frequently Asked Questions about Llama 2 (70B)
How much does the Llama 2 (70B) API cost per 1M tokens?+
Llama 2 (70B) by Meta is priced at $0.4 per 1M input tokens and $0.8 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.1 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Llama 2 (70B) pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Llama 2 (70B)'s context window?+
Llama 2 (70B) features a 4K tokens context window, which equals approximately 3,072 English words or ~8 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 4,096 tokens.
Can I run Llama 2 (70B) locally on my own GPU (Open Weights)?+
Yes, Llama 2 (70B) (70B (also 7B, 13B)) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does Meta train on data sent to the Llama 2 (70B) API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Llama Community License (Commercial Permitted). Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Llama 2 (70B) perform on coding and reasoning benchmarks?+
Llama 2 (70B) achieves a composite Intelligence Index of 31/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, 56.8% on MMLU-Pro, and 1105 pts on Chatbot Arena Elo.
What is Llama 2 (70B) best used for?+
Llama 2 (70B) is best suited for: Unternehmensautomatisierung, Software-Engineering, autonome KI-Agenten und Meta-Produktionssysteme. Assessment: Llama 2 (70B) von Meta überzeugt durch eine ausgewogene Kombination aus Intelligenz, Geschwindigkeit und Kosteneffizienz.
Meta Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Meta's flagship reasoning tier:
Llama 4 Behemoth
Llama 5
Muse Spark 1.1
Llama 4 Maverick
Llama 4 Scout
Llama 3.3 70B
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Llama 2 (70B):
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
A cost and latency optimization where inference providers store pre-computed attention states for repeated prompt prefixes. Reusing cached tokens typically slashes API input costs by 50% to 90% and cuts TTFT substantially.
A GPU memory optimization that stores previously calculated Key and Value attention matrices so the model does not recompute attention for existing tokens at each generation step. KV cache is vital for fast streaming generation and prompt caching.
The neural network architecture introduced in 2017 that powers modern LLMs, using self-attention mechanisms to process all tokens in a sequence concurrently rather than sequentially.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Offizielle Quellen: Meta AI: Llama 2: Open Foundation and Fine-Tuned Chat Models (2023)↗