Llama 3.2 90B Vision vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 3.2 90B Vision by Meta against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
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Llama 3.2 90B Vision
Açık Ağırlıklı ModelMeta tarafından geliştirilen Llama 3.2 90B Vision, 128K token bağlam penceresi ve 1M girdi başına $0.0, 1M çıktı başına $0.0 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.00 / 1M
$0.00 / 1M
128K tokens
28.5%
Claude Opus 5.5
Derin Akıl Yürütme ModeliAnthropic tarafından geliştirilen Claude Opus 5.5, 1M token bağlam penceresi ve 1M girdi başına $4.0, 1M çıktı başına $20.0 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Bir Bakışta Temel Farklar
- Llama 3.2 90B Vision is 1.0x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 128K tokens).
- Claude Opus 5.5 is faster at 78 tok/s vs 75 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 28.5%).
| Feature | Llama 3.2 90B Vision | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 44 | 94 |
Provider | Meta | Anthropic |
Input Price Cost per million input tokens | $0.00 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 75 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 28.5% | 84.6% |
GPQA Diamond Graduate-level reasoning | 48.2% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.40 / 1M |
Hangi Alanda Kim Kazanıyor?
At-a-glance decision frameworkDoğrulanmış Benchmark Puanları
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.
Gecikme Süresi ve Yanıt Hızı
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Gerçek Zamanlı Token Maliyet Simülatörü
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 3.2 90B Vision · Strengths & Key Advantages
- Meta imzalı gelişmiş Multimodal Adapter-Fused GQA Transformer mimarisi ve 128K token bağlam kapasitesi
- Dengeli API maliyeti ($0.0/1M girdi, $0.0/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Claude Opus 5.5 · Strengths & Key Advantages
- Anthropic imzalı gelişmiş Extended Autonomous Hybrid Reasoning Transformer mimarisi ve 1M token bağlam kapasitesi
- Dengeli API maliyeti ($4.0/1M girdi, $20.0/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.2 90B Vision | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $3.85 |
Output Tokens / Task Tokens generated per task | 24K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 42K |
Teknik Özellikler
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3.2 90B Vision | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3.2-90B-Vision | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2024-09 | 2026-09 |
| Knowledge Cutoff | December 2023 | June 2026 |
| Architecture | Multimodal Adapter-Fused GQA Transformer | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | 90B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | $0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| 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) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
Llama 3.2 90B Vision vs Claude Opus 5.5: Hızlı Yanıtlar (SSS)
Which is more intelligent, Llama 3.2 90B Vision or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 44 on the composite Intelligence Index.
Which model is faster?
Claude Opus 5.5 is faster, generating 78 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Llama 3.2 90B Vision is cheaper at $0.00 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3.2 90B Vision has lower latency, with a time to first token of 0.58s compared with 1.40s.
Which model has a larger context window?
Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 128K tokens.
Sonuç
Choose Llama 3.2 90B Vision for kurumsal otomasyon, yazılım mühendisliği, otonom yz ajanları ve meta tabanlı üretim sistemleri, or choose Claude Opus 5.5 for kurumsal otomasyon, yazılım mühendisliği, otonom yz ajanları ve anthropic tabanlı üretim sistemleri.
Sıkça Sorulan Sorular (SSS)
Which is cheaper: Llama 3.2 90B Vision or Claude Opus 5.5?+
Llama 3.2 90B Vision costs $0/1M input and $0/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: Llama 3.2 90B Vision or Claude Opus 5.5?+
Llama 3.2 90B Vision supports 128K tokens whereas Claude Opus 5.5 supports 1M tokens.
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.
Resmi kaynaklar: Meta & Anthropic Official Documentation