GPT-OSS 120B vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
OpenAI 的 GPT-OSS 120B 与 Meta 的 Llama 5 是当前备受关注的两款主力 AI 模型。GPT-OSS 120B 提供 131.1K Token 上下文窗口，API 定价为每百万 Token 输入 $0.15 / 输出 $0.60；而 Llama 5 提供 5M Token 上下文窗口，定价为每百万 Token 输入 $0.00 / 输出 $0.00。以下为两者的基准测试、延迟与成本全面对比。
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GPT-OSS 120B
开源权重基础大模型OpenAI 推出的 GPT-OSS 120B 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 131K 上下文窗口、95 Token/秒生成速度与 56/100 综合智能指数（每百万 Token $0.15 输入 / $0.6 输出）。
$0.15 / 1M
$0.60 / 1M
131.1K tokens
45.5%
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）。
$0.00 / 1M
$0.00 / 1M
5M tokens
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 131K tokens).
- GPT-OSS 120B is faster at 95 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 45.5%).
| Feature | GPT-OSS 120B | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 56 | 94 |
Provider | OpenAI | Meta |
Input Price Cost per million input tokens | $0.15 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.60 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.10 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 33K | 128K |
Throughput Speed Tokens generated per second | 95 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 45.5% | 74% |
GPQA Diamond Graduate-level reasoning | 68.5% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 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.
GPT-OSS 120B · Strengths & Key Advantages
- 提供 131K 上下文窗口与最高 33K 输出容量，核心基准表现优异（SWE-bench Verified 45.5% · GPQA Diamond 68.5%）
- 高达 95 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 480 毫秒
- 开放模型权重 (117B (5.1B MoE))，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
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 | GPT-OSS 120B | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.14 | $0.00 |
Output Tokens / Task Tokens generated per task | 45K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 22K | 20K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-OSS 120B | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | gpt-oss-120b | meta-llama/Llama-5 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-08 | 2026-04 |
| Knowledge Cutoff | June 2024 | January 2026 |
| Architecture | Sparse MoE transformer with reasoning | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | 117B (5.1B active MoE) | 600B+ total (~40-80B active) |
| 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) | Flat rate ($0.15/1M in) across full 131K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.015/1M (no extra cache-write fee) | 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) | 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 | 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) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 73 GB VRAM (4-bit Quantized) · 252 GB VRAM (Native BF16) | 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) | 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, vision, video, audio |
| Output Modalities | text | text |
GPT-OSS 120B vs Llama 5: 常见问题解答 (FAQ)
Which is more intelligent, GPT-OSS 120B or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 56 on the composite Intelligence Index.
Which model is faster?
GPT-OSS 120B is faster, generating 95 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 $0.10 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-OSS 120B has lower latency, with a time to first token of 0.48s compared with 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 131.1K tokens.
综合结论
综合来看，GPT-OSS 120B（适合：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。）与 Llama 5（适合：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。）各具优势：Llama 5 在综合智能与编程基准测试中表现更强，GPT-OSS 120B 拥有更快的 Token 生成速度，而 Llama 5 则在规模化 API 部署中具备更高的成本效益。
常见问题 (FAQ)
在编程和深度推理方面，GPT-OSS 120B 和 Llama 5 哪个更强？+
在综合智能指数与编程基准评测中，Llama 5 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。
GPT-OSS 120B 与 Llama 5 的 API 价格哪个更便宜？+
GPT-OSS 120B 的 API 价格为输入 $0.15 / 输出 $0.60（每百万 Token），Llama 5 为输入 $0.00 / 输出 $0.00。综合混合成本来看，Llama 5 更具性价比。
哪款模型拥有更大的上下文窗口和更快的响应速度？+
GPT-OSS 120B 支持 131.1K Token 上下文窗口，而 Llama 5 支持 5M Token。在流式生成速度方面，GPT-OSS 120B 的吞吐表现更快。
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 与 Meta 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。