Llama 3 (8B) vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 3 (8B) by Meta against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
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Llama 3 (8B)
Edge PowerhouseHyper-efficient compact model trained on over 15 trillion tokens, punchy enough to outperform earlier 70B models on edge hardware and single consumer GPUs.
$0.05 / 1M
$0.10 / 1M
8.2K tokens
12%
Claude Opus 5.5
Frontier Intelligence TitanAnthropic's most intelligent model for complex reasoning, research, and strategic analysis.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Key Differences at a Glance
- Llama 3 (8B) is 133.9x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 8K tokens).
- Llama 3 (8B) is faster at 210 tok/s vs 78 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 12.0%).
| Feature | Llama 3 (8B) | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 34 | 94 |
Provider | Meta | Anthropic |
Input Price Cost per million input tokens | $0.05 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.10 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.03 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 8.2K tokens~12 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 210 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 12% | 84.6% |
GPQA Diamond Graduate-level reasoning | 34.2% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.40 / 1M |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 3 (8B) · Strengths & Key Advantages
- Runs effortlessly on a single consumer GPU (RTX 4090 / Mac M-series)
- Trained on 15T tokens with 128K Tiktoken vocabulary for superior token density
- Fastest token throughput (210 tok/s) in the open-weights class
Considerations & Trade-offs
- Original Llama 3 release had an 8K context window before 3.1 upgraded to 128K
- Prone to occasional logical brevity on complex scientific prompts
Claude Opus 5.5 · Strengths & Key Advantages
- Unmatched autonomous multi-step agentic coding and refactoring
- Flawless multi-document reasoning over 1M token context with no long-prompt surcharge
- Highest composite Intelligence Index score measured (94/100), leading SWE-bench Verified at 84.6%
- Precise tool execution with robust error-recovery loops
Considerations & Trade-offs
- Premium output pricing ($20.00/M) plus heavy reasoning-token usage (~68K output tokens per complex task) keeps long agentic runs expensive
- Long thinking times at maximum effort can push a single hard agentic task toward ~$3.85+
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3 (8B) | 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 |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3 (8B) | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3-(8B) | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2024-04 | 2026-09 |
| Knowledge Cutoff | March 2024 | June 2026 |
| Architecture | Dense GQA Transformer with 128K Tokenizer | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | 8B | 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 ($0.05/1M in) across full 8K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.0125/1M 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) · 5 GB VRAM (4-bit Quantized) · 17 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 (8B) vs Claude Opus 5.5: Quick Answers
Which is more intelligent, Llama 3 (8B) or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 34 on the composite Intelligence Index.
Which model is faster?
Llama 3 (8B) is faster, generating 210 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
Llama 3 (8B) is cheaper at $0.03 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3 (8B) has lower latency, with a time to first token of 0.22s 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 8.2K tokens.
Verdict
Choose Llama 3 (8B) for local edge computing, offline laptop inference, robotics, and low-latency api routers, or choose Claude Opus 5.5 for large-scale software development agents, enterprise codebases, and mission-critical scientific synthesis.
Frequently asked questions
Which is cheaper: Llama 3 (8B) or Claude Opus 5.5?+
Llama 3 (8B) costs $0.05/1M input and $0.1/1M 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 (8B) or Claude Opus 5.5?+
Llama 3 (8B) supports 8K 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.
Sources: Meta & Anthropic Official Documentation