Comparador Multi-Modelo de IA (2-4 Modelos)
Compara entre 2 y 4 modelos de IA de frontera lado a lado en benchmarks de programación y razonamiento, precios de tokens API, ventana de contexto y velocidad.
Multi-Model Benchmark & Official Release Intelligence Matrix
Compare Up to 5 AI Models Side-by-Side on One Page
Evaluate benchmark scores, token pricing, speed, and official provider release specs (API Model ID, Rate Limits, Computer Use, MCP, VRAM & ZDR Compliance) side by side.
2 / 5 Models Active
1-Click Preset Multi-Model Stacks (4 Models):
Currently Compared Models:DeepSeek V3 vs Llama 3.3 70B Arena
Intelligence vs Blended Price (7:2:1)
Top-left quadrant delivers the highest intelligence per dollar
Intelligence vs Output Speed (tok/s)
Top-right quadrant delivers both frontier intelligence and real-time throughput
| Benchmark / Official Specification | DeepSeek · 2024-12 | Meta · 2024-12 |
|---|---|---|
| 1. Official Benchmarks & Capability Scores (Green = Leader) | ||
| Intelligence Index (Composite) | 44 / 100 | 38 / 100 |
| SWE-bench Verified (Agentic Coding) | 49.2% | 39.5% |
| GPQA Diamond (PhD-Level Science) | 59.1% | 48.9% |
| LiveCodeBench (Competitive Coding) | 51.7% | 42.5% |
| AIME (Olympiad Competition Math) | 68% | 56.2% |
| MMLU-Pro (10-Choice Reasoning) | 75.9% | 71% |
| HumanEval (Code Synthesis) | 89.6% | 86% |
| Terminal-Bench (Agentic CLI Execution) | 32.5% | 26.1% |
| LMSYS Chatbot Arena Elo | 1300 | 1285 |
| 2. Token Pricing, Prompt Cache & Throughput Speed | ||
| Input Price (per 1M tokens) | $0.27 / 1M | $0.00 / 1M |
| Output Price (per 1M tokens) | $1.10 / 1M | $0.00 / 1M |
| Cached Input Price (per 1M tokens) | $0.070 / 1M | $0.100 / 1M |
| Blended Price (7:2:1 Cached Workload) | $0.21 / 1M | $0.07 / 1M |
| Context Window Capacity | 128K tokens | 128K tokens |
| Max Output Tokens | 8K tokens | 4K tokens |
| Output Speed (Tokens / sec) | 60 tok/s | 280 tok/s |
| Time to First Token (TTFT Latency) | 0.68s (680 ms) | 0.25s (250 ms) |
| 3. Official Provider Release Specs, API Quotas & Long-Context Rules | ||
| Official API Model ID (SDK) | deepseek-chat | meta-llama/Llama-3.3-70B |
| API Protocol & Endpoint | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Lifecycle & GA Support Window | Legacy Stable (Upgrade path available in DeepSeek lineup) | Legacy Stable (Upgrade path available in Meta lineup) |
| 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) | Flat rate ($0.27/1M in) across full 128K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.07/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| 4. Agentic Capabilities, Self-Hosting VRAM & Enterprise Compliance | ||
| Computer Use / Browser GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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 & Code Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | 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) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 416 GB VRAM (4-bit Quantized) · 1443 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) |
| Supported Inference Engines | vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI | vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | 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 |
| Architecture & Weights License | 671B Mixture-of-Experts (37B active) · Open Weights (Self-Hostable) | Dense Autoregressive Transformer · Open Weights (Self-Hostable) |
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