o3-pro vs Llama 4 Behemoth
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare o3-pro by OpenAI against Llama 4 Behemoth by Meta across real-world coding, reasoning, token economics, and latency.
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o3-pro
深度推理模型OpenAI 推出的 o3-pro 人工智能模型，具备 200K 上下文窗口，输入价格为 $20/百万 Token，输出价格为 $80/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$20.00 / 1M
$80.00 / 1M
200K tokens
71.5%
Llama 4 Behemoth
开源权重模型Meta 推出的 Llama 4 Behemoth 人工智能模型，具备 10M 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
10M tokens
75%
核心差异一览
- Llama 4 Behemoth is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 4 Behemoth has a larger context window (10M vs 200K tokens).
- Llama 4 Behemoth is faster at 45 tok/s vs 30 tok/s.
- Llama 4 Behemoth leads on SWE-bench coding (75.0% vs 71.5%).
| Feature | o3-pro | Llama 4 Behemoth |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 58 | 93 |
Provider | OpenAI | Meta |
Input Price Cost per million input tokens | $20.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $80.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $15.50 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 10M tokens~15,000 A4 pages of text |
Max Output Tokens Per single request | 100K | 64K |
Throughput Speed Tokens generated per second | 30 tok/s | 45 tok/s |
SWE-bench Verified Real-world software engineering | 71.5% | 75% |
GPQA Diamond Graduate-level reasoning | 79.5% | 82% |
Cached Input Price Prompt caching discount per 1M tokens | $5.00 / 1M | $0.00 / 1M |
各项指标谁更胜一筹？
At-a-glance decision framework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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
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.
o3-pro · Strengths & Key Advantages
- 由 OpenAI 研发的高精度 High-effort reasoning transformer 架构，支持 200K 上下文窗口
- 极具竞争力的 API 定价（输入 $20/1M，输出 $80/1M），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
Llama 4 Behemoth · Strengths & Key Advantages
- 由 Meta 研发的高精度 Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) 架构，支持 10M 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 | o3-pro | Llama 4 Behemoth |
|---|---|---|
Cost per Task Estimated USD to finish one task | $11.68 | $0.00 |
Output Tokens / Task Tokens generated per task | 58K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 38K | 15K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | o3-pro | Llama 4 Behemoth |
|---|---|---|
| Official API Model ID (SDK) | o3-pro | meta-llama/Llama-4-Behemoth |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-06 | 2026-05 |
| Knowledge Cutoff | June 2024 | February 2026 |
| Architecture | High-effort reasoning transformer | Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) |
| Parameters | Confidential | 2T total / 288B active (16-of-2 MoE) |
| 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 ($20/1M in) across full 200K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $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) |
| 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) · 1240 GB VRAM (4-bit Quantized) · 4300 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 |
| Output Modalities | text | text |
o3-pro vs Llama 4 Behemoth: 常见问题解答 (FAQ)
Which is more intelligent, o3-pro or Llama 4 Behemoth?
Llama 4 Behemoth is more intelligent. It scores 93 compared with 58 on the composite Intelligence Index.
Which model is faster?
Llama 4 Behemoth is faster, generating 45 tokens per second compared with 30 tokens per second.
Which model is cheaper to run?
Llama 4 Behemoth is cheaper at $0.00 per 1M tokens blended, compared with $15.50 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 4 Behemoth has lower latency, with a time to first token of 1.20s compared with 5.20s.
Which model has a larger context window?
Llama 4 Behemoth has a larger context window, supporting 10M tokens compared with 200K tokens.
综合结论
Choose o3-pro for 企业级 ai 自动化、代码研发、智能体（agent）工作流以及 openai 生态深度集成, or choose Llama 4 Behemoth for 企业级 ai 自动化、代码研发、智能体（agent）工作流以及 meta 生态深度集成.
常见问题 (FAQ)
Which is cheaper: o3-pro or Llama 4 Behemoth?+
o3-pro costs $20/1M input and $80/1M output tokens, while Llama 4 Behemoth costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: o3-pro or Llama 4 Behemoth?+
o3-pro supports 200K tokens whereas Llama 4 Behemoth supports 10M tokens.
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.
数据来源: OpenAI & Meta Official Documentation