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Head-to-Head Model Comparison

Llama 3.2 90B Vision vs Llama 3.2 3B

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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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Llama 3.2 90B Vision vs Llama 3.2 3B: Benchmarks, Pricing & Speed Comparison

Meta 的 Llama 3.2 90B Vision 与 Meta 的 Llama 3.2 3B 是当前备受关注的两款主力 AI 模型。Llama 3.2 90B Vision 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 Llama 3.2 3B 提供 128K 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。

模型直接对比 (VS)

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Meta

Llama 3.2 90B Vision

原生多模态视觉模型

Meta 推出的 Llama 3.2 90B Vision 是一款原生支持图文多模态理解、视觉图表解析与跨模态推理的大模型，具备 128K 上下文窗口、75 Token/秒生成速度与 44/100 综合智能指数（免费开源权重）。

Intelligence Index44 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

28.5%

Meta

Llama 3.2 3B

开源权重基础大模型

Meta 推出的 Llama 3.2 3B 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、240 Token/秒生成速度与 52/100 综合智能指数（免费开源权重）。

Intelligence Index52 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

N/A

核心差异一览

  • Both models cost the same under this workload — choose on capability, not price.
  • Llama 3.2 3B is faster at 240 tok/s vs 75 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureLlama 3.2 90B VisionLlama 3.2 3B
Intelligence Index
Composite reasoning & knowledge score (0-100)
4452
Provider
MetaMeta
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 text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
8K4K
Throughput Speed
Tokens generated per second
75 tok/s240 tok/s
SWE-bench Verified
Real-world software engineering
28.5%N/A
GPQA Diamond
Graduate-level reasoning
48.2%N/A

各项指标谁更胜一筹？

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Tied
Identical monthly runtime cost
Speed
Llama 3.2 3B
+220% faster generation
Context
Tied
128K tokens window

权威基准测试得分对比

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

Llama 3.2 90B VisionLlama 3.2 3B
SWE-bench Verified (Autonomous Coding)

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

28.5%vsN/A
GPQA Diamond (Doctorate-Level Science & Math)

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

48.2%vsN/A
MMLU-Pro (Multi-task Language Understanding)

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

73.5%vs56.5%
LMSYS Chatbot Arena Elo

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

1260ptsvs1190pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs240 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
Llama 3.2 90B Vision0.58s
Llama 3.2 3B0.16s
Est. End-to-End Response (500 tokens)TTFT + generation time
Llama 3.2 90B Vision7.25s
Llama 3.2 3B2.24s

实时 Token 成本模拟器

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Llama 3.2 90B Vision Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Llama 3.2 3B Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
These models cost the same under this workload — no savings either way.Break-even

Llama 3.2 90B Vision · Strengths & Key Advantages

  • 提供 128K 上下文窗口与最高 8K 输出容量，核心基准表现优异（SWE-bench Verified 28.5% · GPQA Diamond 48.2%）
  • 高达 75 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 580 毫秒
  • 开放模型权重 (90B)，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 3.2 3B · 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 56.5% · Arena Elo 1190）
  • 高达 240 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 160 毫秒
  • 开放模型权重 (3B)，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.

MetricLlama 3.2 90B VisionLlama 3.2 3B
Cost per Task
Estimated USD to finish one task
$0.20$0.00
Output Tokens / Task
Tokens generated per task
24K3K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureLlama 3.2 90B VisionLlama 3.2 3B
Official API Model ID (SDK)meta-llama/Llama-3.2-90B-Visionmeta-llama/Llama-3.2-3B
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2024-092024-09
Knowledge CutoffDecember 2023December 2023
ArchitectureMultimodal Adapter-Fused GQA TransformerPruned and distilled compact transformer
Parameters90B3B
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (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 & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 56 GB VRAM (4-bit Quantized) · 194 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 6 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext
Output Modalitiestexttext

Llama 3.2 90B Vision vs Llama 3.2 3B: 常见问题解答 (FAQ)

Which is more intelligent, Llama 3.2 90B Vision or Llama 3.2 3B?

Llama 3.2 3B is more intelligent. It scores 52 compared with 44 on the composite Intelligence Index.

Which model is faster?

Llama 3.2 3B is faster, generating 240 tokens per second compared with 75 tokens per second.

Which model has lower latency?

Llama 3.2 3B has lower latency, with a time to first token of 0.16s compared with 0.58s.

综合结论

综合来看，Llama 3.2 90B Vision（适合：原生支持图文多模态理解、视觉图表解析与跨模态推理的任务、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 3.2 3B（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 3B 在综合智能与编程基准测试中表现更强，Llama 3.2 3B 拥有更快的 Token 生成速度，而 Llama 3.2 90B Vision 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 90B Vision 和 Llama 3.2 3B 哪个更强？+

在综合智能指数与编程基准评测中，Llama 3.2 3B 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 90B Vision 与 Llama 3.2 3B 的 API 价格哪个更便宜？+

Llama 3.2 90B Vision 的 API 价格为输入 $0.00 / 输出 $0.00（每百万 Token），Llama 3.2 3B 为输入 $0.00 / 输出 $0.00。综合混合成本来看，Llama 3.2 90B Vision 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 90B Vision 支持 128K Token 上下文窗口，而 Llama 3.2 3B 支持 128K Token。在流式生成速度方面，Llama 3.2 3B 的吞吐表现更快。

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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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 Meta 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。