GPT-2 vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-2 by OpenAI against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
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GPT-2
Model Open-WeightsGPT-2 dari OpenAI adalah model AI canggih dengan jendela konteks 1K token serta harga API $0/1M token input dan $0/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.
$0.00 / 1M
$0.00 / 1M
1K tokens
N/A
Claude Opus 5.5
Model Penalaran MendalamClaude Opus 5.5 dari Anthropic adalah model AI canggih dengan jendela konteks 1M token serta harga API $4.0/1M token input dan $20.0/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Perbedaan Utama Sekilas
- GPT-2 is 1.0x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 1K tokens).
- GPT-2 is faster at 120 tok/s vs 78 tok/s.
| Feature | GPT-2 | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 12 | 94 |
Provider | OpenAI | 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 | 1K tokens~2 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 1K | 128K |
Throughput Speed Tokens generated per second | 120 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 84.6% |
GPQA Diamond Graduate-level reasoning | N/A | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.40 / 1M |
Siapa Unggul di Mana?
At-a-glance decision frameworkEvaluations ongoing
Skor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-2 · Strengths & Key Advantages
- Arsitektur 48-layer Decoder Transformer mutakhir dari OpenAI dengan dukungan konteks 1K token
- Efisiensi biaya API yang kompetitif ($0/1M input, $0/1M output) dan respons cepat
- Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
- Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
- Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik
Claude Opus 5.5 · Strengths & Key Advantages
- Arsitektur Extended Autonomous Hybrid Reasoning Transformer mutakhir dari Anthropic dengan dukungan konteks 1M token
- Efisiensi biaya API yang kompetitif ($4.0/1M input, $20.0/1M output) dan respons cepat
- Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
- Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
- Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-2 | 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 |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-2 | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | gpt-2 | claude-opus-5-5-20260901 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2019-02 | 2026-09 |
| Knowledge Cutoff | 2019 | June 2026 |
| Architecture | 48-layer Decoder Transformer | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | 1.5B | 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 | Text-only completion (No GUI action loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Native Web Search tool + Hosted Code Interpreter & File Search RAG | 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) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | 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 |
GPT-2 vs Claude Opus 5.5: Jawaban Cepat (FAQ)
Which is more intelligent, GPT-2 or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 12 on the composite Intelligence Index.
Which model is faster?
GPT-2 is faster, generating 120 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
GPT-2 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?
GPT-2 has lower latency, with a time to first token of 0.30s 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 1K tokens.
Kesimpulan
Choose GPT-2 for otomasi perusahaan, pengembangan perangkat lunak, agen ai, dan alur kerja produksi berbasis openai, or choose Claude Opus 5.5 for otomasi perusahaan, pengembangan perangkat lunak, agen ai, dan alur kerja produksi berbasis anthropic.
Pertanyaan Umum (FAQ)
Which is cheaper: GPT-2 or Claude Opus 5.5?+
GPT-2 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: GPT-2 or Claude Opus 5.5?+
GPT-2 supports 1K 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.
Sumber resmi: OpenAI & Anthropic Official Documentation