Llama 3.1 70B vs OLMo 2 13B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 3.1 70B by Meta and OLMo 2 13B by AI2 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, Llama 3.1 70B provides an expanded memory horizon with 128K tokens. In latency-sensitive production environments, OLMo 2 13B 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 70B
开源权重模型Meta 推出的 Llama 3.1 70B 人工智能模型，具备 128K 上下文窗口，输入价格为 $0/百万 Token，输出价格为 $0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.00 / 1M
$0.00 / 1M
128K tokens
42%
OLMo 2 13B
开源权重模型AI2 推出的 OLMo 2 13B 人工智能模型，具备 32K 上下文窗口，输入价格为 $0/百万 Token，输出价格为 $0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.00 / 1M
$0.00 / 1M
32.8K tokens
N/A
核心差异一览
- Both models cost the same under this workload — choose on capability, not price.
- Llama 3.1 70B has a larger context window (128K vs 33K tokens).
- OLMo 2 13B is faster at 130 tok/s vs 75 tok/s.
| Feature | Llama 3.1 70B | OLMo 2 13B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 75 | 71 |
Provider | Meta | AI2 |
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 | 32.8K tokens~49 A4 pages of text |
Max Output Tokens Per single request | 8K | 4K |
Throughput Speed Tokens generated per second | 75 tok/s | 130 tok/s |
SWE-bench Verified Real-world software engineering | 42% | N/A |
GPQA Diamond Graduate-level reasoning | 52% | N/A |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
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.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 3.1 70B · Strengths & Key Advantages
- 由 Meta 研发的高精度 High-capacity dense multilingual transformer 架构，支持 128K 上下文窗口
- 极具竞争力的 API 定价（输入 $0/1M，输出 $0/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
OLMo 2 13B · Strengths & Key Advantages
- 由 AI2 研发的高精度 Fully Open Research Transformer (Data, Code & Weights) 架构，支持 32K 上下文窗口
- 极具竞争力的 API 定价（输入 $0/1M，输出 $0/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.1 70B | OLMo 2 13B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 6K | 10K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3.1 70B | OLMo 2 13B |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3.1-70B | olmo-2-13b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-07 | 2024-11 |
| Knowledge Cutoff | December 2023 | August 2024 |
| Architecture | High-capacity dense multilingual transformer | Fully Open Research Transformer (Data, Code & Weights) |
| Parameters | 70B | 13B |
| 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) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 8 GB VRAM (4-bit Quantized) · 28 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 70B vs OLMo 2 13B: 常见问题解答 (FAQ)
Which is more intelligent, Llama 3.1 70B or OLMo 2 13B?
Llama 3.1 70B is more intelligent. It scores 75 compared with 71 on the composite Intelligence Index.
Which model is faster?
OLMo 2 13B is faster, generating 130 tokens per second compared with 75 tokens per second.
Which model has lower latency?
OLMo 2 13B has lower latency, with a time to first token of 0.31s compared with 0.40s.
Which model has a larger context window?
Llama 3.1 70B has a larger context window, supporting 128K tokens compared with 32.8K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose Llama 3.1 70B if your priority is Self-hosted corporate chatbots, private document indexing, and code generation. Its performance in demanding tasks justifies its operational token cost. Choose OLMo 2 13B if your workflow prioritizes Academic interpretability studies, AI safety research, and reproducible science. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does Llama 3.1 70B compare to OLMo 2 13B in API token pricing and cost per token?+
Both Llama 3.1 70B and OLMo 2 13B 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 70B or OLMo 2 13B?+
Llama 3.1 70B supports a context window of 128K tokens (~96k words), while OLMo 2 13B supports 33K tokens (~25k words). Therefore, Llama 3.1 70B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Llama 3.1 70B or OLMo 2 13B?+
Llama 3.1 70B scores 42% on SWE-bench Verified, while OLMo 2 13B scores competitively for software generation. For complex autonomous debugging and agentic engineering, Llama 3.1 70B provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Llama 3.1 70B and OLMo 2 13B?+
Llama 3.1 70B is optimized for low-latency direct generation. In contrast, OLMo 2 13B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Llama 3.1 70B and OLMo 2 13B?+
Llama 3.1 70B generates approximately 75 tokens per second, while OLMo 2 13B delivers 130 tokens per second. In latency-sensitive production environments, OLMo 2 13B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama 3.1 70B and OLMo 2 13B 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 OLMo 2 13B, and route deep architectural or complex reasoning queries to Llama 3.1 70B. 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.
Related reading
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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.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs