GPT-2 vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-2 by OpenAI against Kimi K3 by Moonshot AI across real-world coding, reasoning, token economics, and latency.
بدّل وقارن بين أي نموذجين
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Live comparison: no in-depth editorial review has been published for this pair yet.
GPT-2
نموذج مفتوح الأوزاننموذج GPT-2 المقدم من OpenAI يوفر نافذة سياق تصل إلى 1K رمز، بتكلفة $0 لكل مليون رمز إدخال و$0 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.00 / 1M
$0.00 / 1M
1K tokens
N/A
Kimi K3
نموذج مفتوح الأوزاننموذج Kimi K3 المقدم من Moonshot AI يوفر نافذة سياق تصل إلى 1.0M رمز، بتكلفة $0 لكل مليون رمز إدخال و$0 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
أهم الفروقات في لمحة
- Both models cost the same under this workload — choose on capability, not price.
- Kimi K3 has a larger context window (1M vs 1K tokens).
- GPT-2 is faster at 120 tok/s vs 58 tok/s.
| Feature | GPT-2 | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 12 | 93 |
Provider | OpenAI | Moonshot AI |
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 | 1K tokens~2 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 1K | 131K |
Throughput Speed Tokens generated per second | 120 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 74.5% |
GPQA Diamond Graduate-level reasoning | N/A | 79% |
من يتفوق في كل مجال؟
At-a-glance decision frameworkEvaluations ongoing
نتائج 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.
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.
GPT-2 · Strengths & Key Advantages
- بنية 48-layer Decoder Transformer متطورة من OpenAI مع سعة سياق تبلغ 1K رمز
- كفاءة عالية في التكلفة ($0/1M إدخال و$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
Kimi K3 · Strengths & Key Advantages
- بنية Colossus-Scale Multimodal Sparse Mixture-of-Experts متطورة من Moonshot AI مع سعة سياق تبلغ 1.0M رمز
- كفاءة عالية في التكلفة ($0/1M إدخال و$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 | GPT-2 | Kimi K3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $2.00 |
Output Tokens / Task Tokens generated per task | 24K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 18K |
المواصفات التقنية
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-2 | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | gpt-2 | kimi-k3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2019-02 | 2026-07 |
| Knowledge Cutoff | 2019 | March 2026 |
| Architecture | 48-layer Decoder Transformer | Colossus-Scale Multimodal Sparse Mixture-of-Experts |
| Parameters | 1.5B | 2.8T total (MoE) |
| 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 | 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 | 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) | 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) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1736 GB VRAM (4-bit Quantized) · 6020 GB VRAM (Native BF16) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | 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 | text, vision |
| Output Modalities | text | text |
GPT-2 vs Kimi K3: إجابات سريعة (FAQ)
Which is more intelligent, GPT-2 or Kimi K3?
Kimi K3 is more intelligent. It scores 93 compared with 12 on the composite Intelligence Index.
Which model is faster?
GPT-2 is faster, generating 120 tokens per second compared with 58 tokens per second.
Which model has lower latency?
GPT-2 has lower latency, with a time to first token of 0.30s compared with 0.72s.
Which model has a larger context window?
Kimi K3 has a larger context window, supporting 1.05M tokens compared with 1K tokens.
الحكم النهائي
Choose GPT-2 for أتمتة الأعمال، تطوير البرمجيات، الوكلاء الأذكياء (ai agents)، وتطبيقات openai الإنتاجية, or choose Kimi K3 for أتمتة الأعمال، تطوير البرمجيات، الوكلاء الأذكياء (ai agents)، وتطبيقات moonshot ai الإنتاجية.
الأسئلة الشائعة (FAQ)
Which is cheaper: GPT-2 or Kimi K3?+
GPT-2 costs $0/1M input and $0/1M output tokens, while Kimi K3 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: GPT-2 or Kimi K3?+
GPT-2 supports 1K tokens whereas Kimi K3 supports 1.0M 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: OpenAI & Moonshot AI Official Documentation