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.
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Live comparison: no in-depth editorial review has been published for this pair yet.
GPT-2
Hito en cero muestrasde OpenAI landmark unsupervised modelo de lenguaje demonstrating zero-shot tarea transfer un través de reading comprensión, traducción, y resumen.
$0.00 / 1M
$0.00 / 1M
1K tokens
N/A
Kimi K3
Razonador abierto de 2.8TEl gigante modelo MoE abierto de 2.8T de Moonshot AI con ventana de contexto de 1 millón de tokens y capacidades revolucionarias en codificación.
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
Diferencias Clave de un Vistazo
- 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.
| Característica | 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% |
¿Quién gana en cada área?
At-a-glance decision frameworkEvaluations ongoing
Puntuaciones de Benchmarks Verificadas
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-2 · Strengths & Key Advantages
- Pioneered zero-shot tarea completion sin explicit tarea-specific training sets
- alto-coherencia multi-paragraph texto generación
- Openly released modelo weights para global investigación
Considerations & Trade-offs
- ventana de contexto limitado un 1,024 tokens
- Prone un repetition y factual hallucinations sobre largo passages
Kimi K3 · Strengths & Key Advantages
- Largest de pesos abiertos modelo fundacional en existence (2.8T parámetros)
- 1480 LMSYS Chatbot Arena Elo rivaling propietario cerrado de frontera leaders
- completo 1M token ventana de contexto paired con masivo 131K max salida tokens
Considerations & Trade-offs
- Extreme hardware prerequisites (distributed multinodo clúster implementación)
- genera extensive razonamiento traces que demand careful token económico management
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 |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | 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: Respuestas Rápidas (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.
Veredicto
Choose GPT-2 for historical nlp investigación, ligero local texto generación experiments, y de pesos abiertos study, or choose Kimi K3 for auditorías de código de contexto largo, análisis de repositorios completos y trabajo agéntico autónomo.
Preguntas frecuentes
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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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: OpenAI & Moonshot AI Official Documentation