GPT-3 vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-3 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-3
Modello di Generazione ClassicaGPT-3 di OpenAI è un modello di IA progettato per gli assistenti conversazionali, la sintesi documentale e i flussi di lavoro aziendali, con finestra di contesto da 2K, velocità di 40 token/s, indice di intelligenza di 22/100 e tariffa di $
$20.00 / 1M
$20.00 / 1M
2K tokens
N/A
Kimi K3
Ragionamento a Pesi ApertiKimi K3 di Moonshot AI è un modello di IA progettato per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, con finestra di contesto da 1.0M, velocità di 58 token/s, indice di intelligenza di 93/100 e tariffa di
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
Differenze Chiave a Colpo d’Occhio
- Kimi K3 is 1.0x cheaper for API usage (input + output tokens combined).
- Kimi K3 has a larger context window (1M vs 2K tokens).
- Kimi K3 is faster at 58 tok/s vs 40 tok/s.
| Feature | GPT-3 | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 22 | 93 |
Provider | OpenAI | Moonshot AI |
Input Price Cost per million input tokens | $20.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $20.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $9.50 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 2K tokens~3 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 2K | 131K |
Throughput Speed Tokens generated per second | 40 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 74.5% |
GPQA Diamond Graduate-level reasoning | N/A | 79% |
Cached Input Price Prompt caching discount per 1M tokens | $5.00 / 1M | $0.00 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkEvaluations ongoing
Punteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-3 · Strengths & Key Advantages
- Finestra di contesto da 2K con capacità massima di output fino a 2K token (MMLU-Pro 43.9% · Arena Elo 980)
- Throughput elevato di 40 token/secondo con latenza al primo token (TTFT) di soli 900 ms
- Economia API competitiva ($20/$20 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati
Considerations & Trade-offs
- Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
- Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API
Kimi K3 · Strengths & Key Advantages
- Finestra di contesto da 1.0M con capacità massima di output fino a 131K token (SWE-bench Verified 74.5% · GPQA Diamond 79%)
- Throughput elevato di 58 token/secondo con latenza al primo token (TTFT) di soli 720 ms
- Pesi aperti (2.8T total (MoE)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-3 | 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 |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-3 | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | gpt-3 | kimi-k3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2020-06 | 2026-07 |
| Knowledge Cutoff | June 2020 | March 2026 |
| Architecture | Autoregressive Transformer (Davinci) | Colossus-Scale Multimodal Sparse Mixture-of-Experts |
| Parameters | 175B | 2.8T total (MoE) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($20/1M in) across full 2K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $5/1M (no extra cache-write fee) | 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 | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | 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 | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | 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) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
GPT-3 vs Kimi K3: Risposte Rapide (FAQ)
Which is more intelligent, GPT-3 or Kimi K3?
Kimi K3 is more intelligent. It scores 93 compared with 22 on the composite Intelligence Index.
Which model is faster?
Kimi K3 is faster, generating 58 tokens per second compared with 40 tokens per second.
Which model is cheaper to run?
Kimi K3 is cheaper at $0.00 per 1M tokens blended, compared with $9.50 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Kimi K3 has lower latency, with a time to first token of 0.72s compared with 0.90s.
Which model has a larger context window?
Kimi K3 has a larger context window, supporting 1.05M tokens compared with 2K tokens.
Verdetto
Choose GPT-3 for ideale per gli assistenti conversazionali, la sintesi documentale e i flussi di lavoro aziendali, sistemi rag aziendali e integrazioni api scalabili., or choose Kimi K3 for ideale per il ragionamento chain-of-thought a pesi aperti ad alta efficienza algoritmica, sistemi rag aziendali e integrazioni api scalabili..
Domande Frequenti (FAQ)
Which is cheaper: GPT-3 or Kimi K3?+
GPT-3 costs $20/1M input and $20/1M output tokens, while Kimi K3 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: GPT-3 or Kimi K3?+
GPT-3 supports 2K 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.
Fonti ufficiali: OpenAI & Moonshot AI Official Documentation