Llama 3.2 11B Vision vs Llama 3.1 8B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 3.2 11B Vision by Meta and Llama 3.1 8B by Meta represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 128K tokens. In latency-sensitive production environments, Llama 3.1 8B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Llama 3.2 11B Vision
开源权重模型Meta 推出的 Llama 3.2 11B Vision 人工智能模型，具备 128K 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
128K tokens
N/A
Llama 3.1 8B
开源权重模型Meta 推出的 Llama 3.1 8B 人工智能模型，具备 128K 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
128K tokens
22%
核心差异一览
- Both models cost the same under this workload — choose on capability, not price.
- Llama 3.1 8B is faster at 160 tok/s vs 120 tok/s.
| Feature | Llama 3.2 11B Vision | Llama 3.1 8B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 66 | 60 |
Provider | Meta | Meta |
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 text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 4K | 8K |
Throughput Speed Tokens generated per second | 120 tok/s | 160 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 22% |
GPQA Diamond Graduate-level reasoning | N/A | 38% |
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.
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 11B Vision · Strengths & Key Advantages
- 由 Meta 研发的高精度 Multimodal cross-attention vision-language transformer 架构，支持 128K 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
Llama 3.1 8B · Strengths & Key Advantages
- 由 Meta 研发的高精度 Dense auto-regressive multilingual transformer 架构，支持 128K 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 | Llama 3.2 11B Vision | Llama 3.1 8B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 3K | 4K |
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 11B Vision | Llama 3.1 8B |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3.2-11B-Vision | meta-llama/Llama-3.1-8B |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-09 | 2024-07 |
| Knowledge Cutoff | December 2023 | December 2023 |
| Architecture | Multimodal cross-attention vision-language transformer | Dense auto-regressive multilingual transformer |
| Parameters | 11B | 8B |
| 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) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | 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) | 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) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 7 GB VRAM (4-bit Quantized) · 24 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 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, vision | text |
| Output Modalities | text | text |
Llama 3.2 11B Vision vs Llama 3.1 8B: 常见问题解答 (FAQ)
Which is more intelligent, Llama 3.2 11B Vision or Llama 3.1 8B?
Llama 3.2 11B Vision is more intelligent. It scores 66 compared with 60 on the composite Intelligence Index.
Which model is faster?
Llama 3.1 8B is faster, generating 160 tokens per second compared with 120 tokens per second.
Which model has lower latency?
Llama 3.1 8B has lower latency, with a time to first token of 0.22s compared with 0.31s.
综合结论
Verdict & Model Selection Recommendation: Choose Llama 3.2 11B Vision if your priority is Local document OCR and extraction, image captioning, and open visual search. Its performance in demanding tasks justifies its operational token cost. Choose Llama 3.1 8B if your workflow prioritizes Local offline development, edge device inference, and cost-free SaaS backends. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does Llama 3.2 11B Vision compare to Llama 3.1 8B in API token pricing and cost per token?+
Both Llama 3.2 11B Vision and Llama 3.1 8B are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: Llama 3.2 11B Vision or Llama 3.1 8B?+
Both Llama 3.2 11B Vision and Llama 3.1 8B offer an identical context window of 128K tokens (~96k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Llama 3.2 11B Vision or Llama 3.1 8B?+
Llama 3.2 11B Vision scores at the frontier of coding benchmarks, while Llama 3.1 8B scores 22%. For complex autonomous debugging and agentic engineering, Llama 3.1 8B provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Llama 3.2 11B Vision and Llama 3.1 8B?+
Llama 3.2 11B Vision is optimized for low-latency direct generation. In contrast, Llama 3.1 8B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Llama 3.2 11B Vision and Llama 3.1 8B?+
Llama 3.2 11B Vision generates approximately 120 tokens per second, while Llama 3.1 8B delivers 160 tokens per second. In latency-sensitive production environments, Llama 3.1 8B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama 3.2 11B Vision and Llama 3.1 8B to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Llama 3.1 8B, and route deep architectural or complex reasoning queries to Llama 3.2 11B Vision. This balanced routing strategy maximizes system throughput and answer quality.
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: GetInfoUs Independent AI Benchmarks & Official API Specs