Llama 3.2 90B Vision vs Llama 3.2 3B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Llama 3.2 90B Vision by Meta and Llama 3.2 3B 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 3.2 3B 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.
Changez et Comparez les Modèles
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Llama 3.2 90B Vision
Modèle phare de vision open sourcede Meta de référence ouvert multimodal modèle de fondation pairing 128K contexte windows avec cross-attention visuel encoders à interpret complexe charts, graphs, et images.
$0.00 / 1M
$0.00 / 1M
128K tokens
28.5%
Llama 3.2 3B
Petit sur appareilléger 3B ouvert modèle balancing sur-device vitesse avec surprisingly capable instruction-following.
$0.00 / 1M
$0.00 / 1M
128K tokens
N/A
Différences Clés en un Coup d’Œil
- 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.
| Caractéristique | Llama 3.2 90B Vision | Llama 3.2 3B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 44 | 52 |
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 | 8K | 4K |
Throughput Speed Tokens generated per second | 75 tok/s | 240 tok/s |
SWE-bench Verified Real-world software engineering | 28.5% | N/A |
GPQA Diamond Graduate-level reasoning | 48.2% | N/A |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 3.2 90B Vision · Strengths & Key Advantages
- natif ouvert visuel raisonnement à travers complexe documents, schematics, et maps
- Cross-attention adapter architecture preserves texte raisonnement tandis que unlocking vision
- 128,000 token fenêtre de contexte
Considerations & Trade-offs
- nécessite spécialisé multi-GPU servers pour élevé-débit visuel batching
- sortie génération capped à 8,192 tokens
Llama 3.2 3B · Strengths & Key Advantages
- excellent instruction following dans sous 3GB mémoire
- 128K contexte capacité
- rapide local responsiveness
Considerations & Trade-offs
- Capacité limitée de résolution de problèmes STEM
- texte-seulement entrée
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.2 90B Vision | Llama 3.2 3B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.00 |
Output Tokens / Task Tokens generated per task | 24K | 3K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Llama 3.2 90B Vision | Llama 3.2 3B |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3.2-90B-Vision | meta-llama/Llama-3.2-3B |
| 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 | December 2023 |
| Architecture | Multimodal Adapter-Fused GQA Transformer | Pruned and distilled compact transformer |
| Parameters | 90B | 3B |
| 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) · 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 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 90B Vision vs Llama 3.2 3B: Réponses Rapides (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.
Verdict
Verdict & Model Selection Recommendation: Choose Llama 3.2 90B Vision if your priority is Self-hosted OCR extraction, visual document analysis, and private enterprise multimodal agents. Its performance in demanding tasks justifies its operational token cost. Choose Llama 3.2 3B if your workflow prioritizes Personal desktop assistants, mobile apps, and lightweight classification. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does Llama 3.2 90B Vision compare to Llama 3.2 3B in API token pricing and cost per token?+
Both Llama 3.2 90B Vision and Llama 3.2 3B 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 90B Vision or Llama 3.2 3B?+
Both Llama 3.2 90B Vision and Llama 3.2 3B 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 90B Vision or Llama 3.2 3B?+
Llama 3.2 90B Vision scores 28.5% on SWE-bench Verified, while Llama 3.2 3B scores competitively for software generation. For complex autonomous debugging and agentic engineering, Llama 3.2 90B Vision provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Llama 3.2 90B Vision and Llama 3.2 3B?+
Llama 3.2 90B Vision is optimized for low-latency direct generation. In contrast, Llama 3.2 3B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Llama 3.2 90B Vision and Llama 3.2 3B?+
Llama 3.2 90B Vision generates approximately 75 tokens per second, while Llama 3.2 3B delivers 240 tokens per second. In latency-sensitive production environments, Llama 3.2 3B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama 3.2 90B Vision and Llama 3.2 3B 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 3.2 3B, and route deep architectural or complex reasoning queries to Llama 3.2 90B 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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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs