Llama 3.3 Nemotron Super 49B v1 vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 3.3 Nemotron Super 49B v1 by NVIDIA against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
بدّل وقارن بين أي نموذجين
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Llama 3.3 Nemotron Super 49B v1
نموذج مفتوح الأوزاننموذج Llama 3.3 Nemotron Super 49B v1 المقدم من NVIDIA يوفر نافذة سياق تصل إلى 131K رمز، بتكلفة $0.25 لكل مليون رمز إدخال و$0.5 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.25 / 1M
$0.50 / 1M
131.1K tokens
40%
Claude Opus 5.5
نموذج استدلال عميقنموذج Claude Opus 5.5 المقدم من Anthropic يوفر نافذة سياق تصل إلى 1M رمز، بتكلفة $4.0 لكل مليون رمز إدخال و$20.0 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
أهم الفروقات في لمحة
- Llama 3.3 Nemotron Super 49B v1 is 26.0x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 131K tokens).
- Llama 3.3 Nemotron Super 49B v1 is faster at 95 tok/s vs 78 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 40.0%).
| Feature | Llama 3.3 Nemotron Super 49B v1 | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 46 | 94 |
Provider | NVIDIA | Anthropic |
Input Price Cost per million input tokens | $0.25 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.50 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.16 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 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 | 95 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 40% | 84.6% |
GPQA Diamond Graduate-level reasoning | 56% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.08 / 1M | $0.40 / 1M |
من يتفوق في كل مجال؟
At-a-glance decision frameworkنتائج الاختبارات المعيارية الموثقة
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.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 3.3 Nemotron Super 49B v1 · Strengths & Key Advantages
- بنية Compact High-Density Transformer متطورة من NVIDIA مع سعة سياق تبلغ 131K رمز
- كفاءة عالية في التكلفة ($0.25/1M إدخال و$0.5/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Claude Opus 5.5 · Strengths & Key Advantages
- بنية Extended Autonomous Hybrid Reasoning Transformer متطورة من Anthropic مع سعة سياق تبلغ 1M رمز
- كفاءة عالية في التكلفة ($4.0/1M إدخال و$20.0/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.3 Nemotron Super 49B v1 | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.22 | $3.85 |
Output Tokens / Task Tokens generated per task | 30K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 12K | 42K |
المواصفات التقنية
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3.3 Nemotron Super 49B v1 | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | llama-3-3-nemotron-super-49b-v1 | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-04 | 2026-09 |
| Knowledge Cutoff | January 2025 | June 2026 |
| Architecture | Compact High-Density Transformer | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | 49B | 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) | Flat rate ($0.25/1M in) across full 131K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.08/1M tokens | $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) | 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) · 30 GB VRAM (4-bit Quantized) · 105 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 | text, vision |
| Output Modalities | text | text |
Llama 3.3 Nemotron Super 49B v1 vs Claude Opus 5.5: إجابات سريعة (FAQ)
Which is more intelligent, Llama 3.3 Nemotron Super 49B v1 or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 46 on the composite Intelligence Index.
Which model is faster?
Llama 3.3 Nemotron Super 49B v1 is faster, generating 95 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
Llama 3.3 Nemotron Super 49B v1 is cheaper at $0.16 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3.3 Nemotron Super 49B v1 has lower latency, with a time to first token of 0.40s 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 131.1K tokens.
الحكم النهائي
Choose Llama 3.3 Nemotron Super 49B v1 for أتمتة الأعمال، تطوير البرمجيات، الوكلاء الأذكياء (ai agents)، وتطبيقات nvidia الإنتاجية, or choose Claude Opus 5.5 for أتمتة الأعمال، تطوير البرمجيات، الوكلاء الأذكياء (ai agents)، وتطبيقات anthropic الإنتاجية.
الأسئلة الشائعة (FAQ)
Which is cheaper: Llama 3.3 Nemotron Super 49B v1 or Claude Opus 5.5?+
Llama 3.3 Nemotron Super 49B v1 costs $0.25/1M input and $0.5/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: Llama 3.3 Nemotron Super 49B v1 or Claude Opus 5.5?+
Llama 3.3 Nemotron Super 49B v1 supports 131K 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.
المصادر الرسمية: NVIDIA & Anthropic Official Documentation