Llama 3.1 8B vs Llama 3.1 70B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Llama 3.1 8B by Meta and Llama 3.1 70B by Meta represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 128K tokens. In latency-sensitive production environments, Llama 3.1 8B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Llama 3.1 8B
Small Open LlamaThe premier compact open model of 2024 with 128K context, multilingual fluency, and native tool use.
$0.00 / 1M
$0.00 / 1M
128K tokens
22%
Llama 3.1 70B
Open WorkhorseEnterprise open-source workhorse offering competitive performance with closed frontier models across 128K context.
$0.00 / 1M
$0.00 / 1M
128K tokens
42%
Key Differences at a Glance
- Both models cost the same under this workload — choose on capability, not price.
- Llama 3.1 8B is faster at 160 tok/s vs 75 tok/s.
- Llama 3.1 70B leads on SWE-bench coding (42.0% vs 22.0%).
| Feature | Llama 3.1 8B | Llama 3.1 70B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 60 | 75 |
Provider | Meta | Meta |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 160 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | 22% | 42% |
GPQA Diamond Graduate-level reasoning | 38% | 52% |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 3.1 8B · Strengths & Key Advantages
- Exceptional intelligence for an 8B model
- Full 128K context window with clean recall
- Massive open-source developer ecosystem
Considerations & Trade-offs
- Smaller capacity limits complex math and architectural code reasoning
- Text-only model
Llama 3.1 70B · Strengths & Key Advantages
- Rivals GPT-4 class models on general knowledge and coding
- 128K context window
- Can be served on dual consumer GPUs with 4-bit quantisation
Considerations & Trade-offs
- Computationally heavy for modest hardware
- Does not feature native vision understanding
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.1 8B | Llama 3.1 70B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 4K | 6K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3.1 8B | Llama 3.1 70B |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3.1-8B | meta-llama/Llama-3.1-70B |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-07 | 2024-07 |
| Knowledge Cutoff | December 2023 | December 2023 |
| Architecture | Dense auto-regressive multilingual transformer | High-capacity dense multilingual transformer |
| Parameters | 8B | 70B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
Llama 3.1 8B vs Llama 3.1 70B: Quick Answers
Which is more intelligent, Llama 3.1 8B or Llama 3.1 70B?
Llama 3.1 70B is more intelligent. It scores 75 compared with 60 on the composite Intelligence Index.
Which model is faster?
Llama 3.1 8B is faster, generating 160 tokens per second compared with 75 tokens per second.
Which model has lower latency?
Llama 3.1 8B has lower latency, with a time to first token of 0.22s compared with 0.40s.
Verdict
Verdict & Model Selection Recommendation: Choose Llama 3.1 8B if your priority is Local offline development, edge device inference, and cost-free SaaS backends. Its performance in demanding tasks justifies its operational token cost. Choose Llama 3.1 70B if your workflow prioritizes Self-hosted corporate chatbots, private document indexing, and code generation. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does Llama 3.1 8B compare to Llama 3.1 70B in API token pricing and cost per token?+
Both Llama 3.1 8B and Llama 3.1 70B are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: Llama 3.1 8B or Llama 3.1 70B?+
Both Llama 3.1 8B and Llama 3.1 70B offer an identical context window of 128K tokens (~96k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Llama 3.1 8B or Llama 3.1 70B?+
Llama 3.1 8B scores 22% on SWE-bench Verified while Llama 3.1 70B scores 42%. For autonomous software engineering and multi-file debugging, Llama 3.1 70B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Llama 3.1 8B and Llama 3.1 70B?+
Llama 3.1 8B is optimized for low-latency direct generation. In contrast, Llama 3.1 70B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Llama 3.1 8B and Llama 3.1 70B?+
Llama 3.1 8B generates approximately 160 tokens per second, while Llama 3.1 70B delivers 75 tokens per second. In latency-sensitive production environments, Llama 3.1 8B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama 3.1 8B and Llama 3.1 70B to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Llama 3.1 8B, and route deep architectural or complex reasoning queries to Llama 3.1 8B. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs