Head-to-Head Model Comparison

o3 vs Llama 5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

由 GetInfoUs 研究团队审核最后审核日期：October 2026编辑政策

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o3Profile

Llama 5Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Live comparison: no in-depth editorial review has been published for this pair yet.

Popular Matchups:

OpenAI

o3

深度逻辑推理引擎

OpenAI 推出的 o3 是一款专为博士级数理逻辑、科学推导与多步思维链 (CoT) 设计的深度推理大模型，具备 200K 上下文窗口、75 Token/秒生成速度与 56/100 综合智能指数（每百万 Token $2 输入 / $8 输出）。

Intelligence Index56 / 100

Input Token:

$2.00 / 1M

Output Token:

$8.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

69.1%

Llama 5

开源权重基础大模型

Meta 推出的 Llama 5 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型，具备 5M 上下文窗口、65 Token/秒生成速度与 94/100 综合智能指数（免费开源权重）。

Intelligence Index94 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

5M tokens

SWE-bench Verified:

74%

核心差异一览

Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
Llama 5 has a larger context window (5M vs 200K tokens).
o3 is faster at 75 tok/s vs 65 tok/s.
Llama 5 leads on SWE-bench coding (74.0% vs 69.1%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	o3	Llama 5
Intelligence Index Composite reasoning & knowledge score (0-100)	56	94
Provider	OpenAI	Meta
Input Price Cost per million input tokens	$2.00 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$8.00 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$1.55 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	200K tokens~300 A4 pages of text	5M tokens~7,500 A4 pages of text
Max Output Tokens Per single request	100K	128K
Throughput Speed Tokens generated per second	75 tok/s	65 tok/s
SWE-bench Verified Real-world software engineering	69.1%	74%
GPQA Diamond Graduate-level reasoning	76.5%	84%
Cached Input Price Prompt caching discount per 1M tokens	$0.50 / 1M	$0.00 / 1M

各项指标谁更胜一筹？

At-a-glance decision framework

Coding

Llama 5

+4.9% SWE-bench lead

Cost

Llama 5

1.0x cheaper monthly runtime

Speed

+15% faster generation

Context

Llama 5

5M tokens window

权威基准测试得分对比

Official evaluation metrics verified by independent third-party research labs.

o3Llama 5

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

69.1%vs74%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

76.5%vs84%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

87%vs87%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1345ptsvs1485pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

12.9%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

42%vsN/A

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

42.5%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

70.5%vs68%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

91.6%vs88%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

78.5%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs65 tok/s

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.

Time to First Token (TTFT)Lower is better

o32.80s

Llama 50.85s

Est. End-to-End Response (500 tokens)TTFT + generation time

o39.47s

Llama 58.54s

实时 Token 成本模拟器

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

o3 Monthly API Bill

$10.95 / month

$2/1M in · $8/1M out

Llama 5 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 5 saves approximately $10.95 / month (100%) under this workload.1.0x Savings

o3 · Strengths & Key Advantages

提供 200K 上下文窗口与最高 100K 输出容量，核心基准表现优异（SWE-bench Verified 69.1% · GPQA Diamond 76.5%）
高达 75 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 2800 毫秒
极具竞争力的 API 定价（每百万 Token $2 输入 / $8 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出

Considerations & Trade-offs

闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
在大规模高频调用场景下，建议结合提示词缓存 (Prompt Caching) 与批量 API 以控制成本

Llama 5 · Strengths & Key Advantages

提供 5M 上下文窗口与最高 128K 输出容量，核心基准表现优异（SWE-bench Verified 74% · GPQA Diamond 84%）
高达 65 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 850 毫秒
开放模型权重 (600B+ total (~40-80B ))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调

Considerations & Trade-offs

在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	o3	Llama 5
Cost per Task Estimated USD to finish one task	$1.35	$0.00
Output Tokens / Task Tokens generated per task	48K	40K
Reasoning Tokens / Task Thinking tokens burned per task	26K	20K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

Feature	o3	Llama 5
Official API Model ID (SDK)	o3	meta-llama/Llama-5
API Protocol	OpenAI Responses API & Chat Completions v1	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-04	2026-04
Knowledge Cutoff	June 2024	January 2026
Architecture	Reasoning transformer with adjustable thinking effort	Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active)
Parameters	Confidential	600B+ total (~40-80B active)
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)	Flat rate ($2/1M in) across full 200K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix caching at $0.5/1M (no extra cache-write fee)	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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	Native Web Search tool + Hosted Code Interpreter & File Search RAG	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
Quantization & Min GPU VRAM	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16)
Safety & Frontier Classification	OpenAI Preparedness Framework Audited & System Card Published	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text, vision	text, vision, video, audio
Output Modalities	text	text

o3 vs Llama 5: 常见问题解答 (FAQ)

Which is more intelligent, o3 or Llama 5?

Llama 5 is more intelligent. It scores 94 compared with 56 on the composite Intelligence Index.

Which model is faster?

o3 is faster, generating 75 tokens per second compared with 65 tokens per second.

Which model is cheaper to run?

Llama 5 is cheaper at $0.00 per 1M tokens blended, compared with $1.55 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 5 has lower latency, with a time to first token of 0.85s compared with 2.80s.

Which model has a larger context window?

Llama 5 has a larger context window, supporting 5M tokens compared with 200K tokens.

综合结论

Choose o3 for 专为博士级数理逻辑、科学推导与多步思维链 (cot) 设计的深度推理任务、企业级 rag 检索增强生成、自动化智能体工作流与高可靠 api 集成。, or choose Llama 5 for 支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、企业级 rag 检索增强生成、自动化智能体工作流与高可靠 api 集成。.

常见问题 (FAQ)

Which is cheaper: o3 or Llama 5?+

o3 costs $2/1M input and $8/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.

Which model has a larger context window: o3 or Llama 5?+

o3 supports 200K tokens whereas Llama 5 supports 5M tokens.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

o3 vs Llama 5

切换并对比任意模型

o3

Llama 5

各项指标谁更胜一筹？

权威基准测试得分对比

延迟与响应速度

实时 Token 成本模拟器

o3 · Strengths & Key Advantages

Considerations & Trade-offs

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

技术规格参数

o3 vs Llama 5: 常见问题解答 (FAQ)

综合结论

常见问题 (FAQ)

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