Llama 3.2 11B Vision vs OLMo 2 13B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 3.2 11B Vision by Meta and OLMo 2 13B by AI2 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, Llama 3.2 11B Vision provides an expanded memory horizon with 128K tokens. In latency-sensitive production environments, OLMo 2 13B 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
Открытые веса (Open Weights)Llama 3.2 11B Vision от Meta — передовая модель искусственного интеллекта с контекстным окном 128K токенов и стоимостью API $0/1M входящих и $0/1M исходящих токенов для программирования, аналитики и автоматизации.
$0.00 / 1M
$0.00 / 1M
128K tokens
N/A
OLMo 2 13B
Открытые веса (Open Weights)OLMo 2 13B от AI2 — передовая модель искусственного интеллекта с контекстным окном 32K токенов и стоимостью API $0/1M входящих и $0/1M исходящих токенов для программирования, аналитики и автоматизации.
$0.00 / 1M
$0.00 / 1M
32.8K tokens
N/A
Ключевые отличия кратко
- Both models cost the same under this workload — choose on capability, not price.
- Llama 3.2 11B Vision has a larger context window (128K vs 33K tokens).
- OLMo 2 13B is faster at 130 tok/s vs 120 tok/s.
| Feature | Llama 3.2 11B Vision | OLMo 2 13B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 66 | 71 |
Provider | Meta | AI2 |
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 | 32.8K tokens~49 A4 pages of text |
Max Output Tokens Per single request | 4K | 4K |
Throughput Speed Tokens generated per second | 120 tok/s | 130 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
Кто лидирует в каждой категории?
At-a-glance decision frameworkEvaluations ongoing
Результаты проверенных бенчмарков
Official evaluation metrics verified by independent third-party research labs.
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.
Задержка и скорость ответа
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Интерактивный калькулятор стоимости токенов
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 3.2 11B Vision · Strengths & Key Advantages
- Современная архитектура Multimodal cross-attention vision-language transformer от Meta с поддержкой до 128K токенов контекста
- Оптимальная стоимость использования ($0/1M вход, $0/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
OLMo 2 13B · Strengths & Key Advantages
- Современная архитектура Fully Open Research Transformer (Data, Code & Weights) от AI2 с поддержкой до 32K токенов контекста
- Оптимальная стоимость использования ($0/1M вход, $0/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.2 11B Vision | OLMo 2 13B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 3K | 10K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Технические характеристики
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3.2 11B Vision | OLMo 2 13B |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3.2-11B-Vision | olmo-2-13b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-09 | 2024-11 |
| Knowledge Cutoff | December 2023 | August 2024 |
| Architecture | Multimodal cross-attention vision-language transformer | Fully Open Research Transformer (Data, Code & Weights) |
| Parameters | 11B | 13B |
| 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) · 8 GB VRAM (4-bit Quantized) · 28 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 OLMo 2 13B: Быстрые ответы (FAQ)
Which is more intelligent, Llama 3.2 11B Vision or OLMo 2 13B?
OLMo 2 13B is more intelligent. It scores 71 compared with 66 on the composite Intelligence Index.
Which model is faster?
OLMo 2 13B is faster, generating 130 tokens per second compared with 120 tokens per second.
Which model has a larger context window?
Llama 3.2 11B Vision has a larger context window, supporting 128K tokens compared with 32.8K tokens.
Вердикт
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 OLMo 2 13B if your workflow prioritizes Academic interpretability studies, AI safety research, and reproducible science. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Часто задаваемые вопросы (FAQ)
How does Llama 3.2 11B Vision compare to OLMo 2 13B in API token pricing and cost per token?+
Both Llama 3.2 11B Vision and OLMo 2 13B 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 OLMo 2 13B?+
Llama 3.2 11B Vision supports a context window of 128K tokens (~96k words), while OLMo 2 13B supports 33K tokens (~25k words). Therefore, Llama 3.2 11B Vision can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Llama 3.2 11B Vision or OLMo 2 13B?+
Llama 3.2 11B Vision scores at the frontier of coding benchmarks, while OLMo 2 13B scores competitively for software generation. For complex autonomous debugging and agentic engineering, Llama 3.2 11B Vision provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Llama 3.2 11B Vision and OLMo 2 13B?+
Llama 3.2 11B Vision is optimized for low-latency direct generation. In contrast, OLMo 2 13B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Llama 3.2 11B Vision and OLMo 2 13B?+
Llama 3.2 11B Vision generates approximately 120 tokens per second, while OLMo 2 13B delivers 130 tokens per second. In latency-sensitive production environments, OLMo 2 13B 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 OLMo 2 13B 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 OLMo 2 13B, 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.
Официальные источники: GetInfoUs Independent AI Benchmarks & Official API Specs