Llama 3.2 1B vs Qwen 2.5 32B Instruct
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Meta 的 Llama 3.2 1B 与 Alibaba 的 Qwen 2.5 32B Instruct 是当前备受关注的两款主力 AI 模型。Llama 3.2 1B 提供 128K Token 上下文窗口，API 定价为每百万 Token 输入 $0.00 / 输出 $0.00；而 Qwen 2.5 32B Instruct 提供 131.1K Token 上下文窗口，定价为每百万 Token 输入 $0.18 / 输出 $0.36。以下为两者的基准测试、延迟与成本全面对比。
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Llama 3.2 1B
开源权重基础大模型Meta 推出的 Llama 3.2 1B 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 128K 上下文窗口、320 Token/秒生成速度与 42/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
128K tokens
N/A
Qwen 2.5 32B Instruct
开源权重基础大模型Alibaba 推出的 Qwen 2.5 32B Instruct 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、85 Token/秒生成速度与 41/100 综合智能指数（每百万 Token $0.18 输入 / $0.36 输出）。
$0.18 / 1M
$0.36 / 1M
131.1K tokens
40.8%
核心差异一览
- Llama 3.2 1B is 1.0x cheaper for API usage (input + output tokens combined).
- Qwen 2.5 32B Instruct has a larger context window (131K vs 128K tokens).
- Llama 3.2 1B is faster at 320 tok/s vs 85 tok/s.
| Feature | Llama 3.2 1B | Qwen 2.5 32B Instruct |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 42 | 41 |
Provider | Meta | Alibaba |
Input Price Cost per million input tokens | $0.00 / 1M | $0.18 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.36 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.10 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 4K | 8K |
Throughput Speed Tokens generated per second | 320 tok/s | 85 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 40.8% |
GPQA Diamond Graduate-level reasoning | N/A | 48.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.04 / 1M |
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.
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.
Llama 3.2 1B · Strengths & Key Advantages
- 提供 128K 上下文窗口与最高 4K 输出容量，核心基准表现优异（MMLU-Pro 46% · Arena Elo 1110）
- 高达 320 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 120 毫秒
- 开放模型权重 (1B)，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
Qwen 2.5 32B Instruct · Strengths & Key Advantages
- 提供 131K 上下文窗口与最高 8K 输出容量，核心基准表现优异（SWE-bench Verified 40.8% · GPQA Diamond 48.5%）
- 高达 85 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 480 毫秒
- 开放模型权重 (32B)，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 | Llama 3.2 1B | Qwen 2.5 32B Instruct |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.16 |
Output Tokens / Task Tokens generated per task | 2K | 28K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3.2 1B | Qwen 2.5 32B Instruct |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3.2-1B | qwen-2-5-32b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-09 | 2024-09 |
| Knowledge Cutoff | December 2023 | June 2024 |
| Architecture | Pruned and distilled ultra-compact transformer | Dense Autoregressive Transformer |
| Parameters | 1B | 32B |
| 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) | Flat rate ($0.18/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Cached input at $0.036/1M tokens |
| 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) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 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 3.2 1B vs Qwen 2.5 32B Instruct: 常见问题解答 (FAQ)
Which is more intelligent, Llama 3.2 1B or Qwen 2.5 32B Instruct?
Llama 3.2 1B is more intelligent. It scores 42 compared with 41 on the composite Intelligence Index.
Which model is faster?
Llama 3.2 1B is faster, generating 320 tokens per second compared with 85 tokens per second.
Which model is cheaper to run?
Llama 3.2 1B 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?
Llama 3.2 1B has lower latency, with a time to first token of 0.12s compared with 0.48s.
Which model has a larger context window?
Qwen 2.5 32B Instruct has a larger context window, supporting 131.1K tokens compared with 128K tokens.
综合结论
综合来看，Llama 3.2 1B（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 Qwen 2.5 32B Instruct（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 3.2 1B 在综合智能与编程基准测试中表现更强，Llama 3.2 1B 拥有更快的 Token 生成速度，而 Llama 3.2 1B 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，Llama 3.2 1B 和 Qwen 2.5 32B Instruct 哪个更强？+
在综合智能指数与编程基准评测中，Llama 3.2 1B 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。
Llama 3.2 1B 与 Qwen 2.5 32B Instruct 的 API 价格哪个更便宜？+
Llama 3.2 1B 的 API 价格为输入 $0.00 / 输出 $0.00（每百万 Token），Qwen 2.5 32B Instruct 为输入 $0.18 / 输出 $0.36。综合混合成本来看，Llama 3.2 1B 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？+
Llama 3.2 1B 支持 128K Token 上下文窗口，而 Qwen 2.5 32B Instruct 支持 131.1K Token。在流式生成速度方面，Llama 3.2 1B 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.
Related reading
- Llama 3.2 1B vs Llama 3.2 11B Vision：基准测试、API 价格与速度全面对比 (2026)对比
- Llama 3.2 3B vs Llama 3.2 1B：基准测试、API 价格与速度全面对比 (2026)对比
- Pixtral 12B vs Qwen 2.5 32B Instruct：基准测试、API 价格与速度全面对比 (2026)对比
- Qwen 2.5 32B Instruct vs Qwen 2.5 7B Instruct：基准测试、API 价格与速度全面对比 (2026)对比
- Qwen 2.5 72B vs Qwen 2.5 32B Instruct：基准测试、API 价格与速度全面对比 (2026)对比
- Aion-3.0 vs Aion-Mini：基准测试、API 价格与速度全面对比 (2026)对比
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: Meta 与 Alibaba 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。