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:Llama 4 Behemoth vs Nemotron 3 Ultra 550B A55B 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 | Meta · 2026-05 | NVIDIA · 2026-06 |
|---|---|---|
| 1. Official Benchmarks & Capability Scores (Green = Leader) | ||
| Intelligence Index (Composite) | 93 / 100 | 60 / 100 |
| SWE-bench Verified (Agentic Coding) | 75% | 64.5% |
| GPQA Diamond (PhD-Level Science) | 82% | 76% |
| LiveCodeBench (Competitive Coding) | N/A | N/A |
| AIME (Olympiad Competition Math) | N/A | 86% |
| MMLU-Pro (10-Choice Reasoning) | 88.5% | 87.5% |
| HumanEval (Code Synthesis) | 96% | 95% |
| Terminal-Bench (Agentic CLI Execution) | N/A | 53% |
| LMSYS Chatbot Arena Elo | 1490 | 1425 |
| 2. Token Pricing, Prompt Cache & Throughput Speed | ||
| Input Price (per 1M tokens) | $0.00 / 1M | $1.00 / 1M |
| Output Price (per 1M tokens) | $0.00 / 1M | $3.00 / 1M |
| Cached Input Price (per 1M tokens) | $0.000 / 1M | $0.250 / 1M |
| Blended Price (7:2:1 Cached Workload) | $0.00 / 1M | $0.68 / 1M |
| Context Window Capacity | 10M tokens | 1M tokens |
| Max Output Tokens | 64K tokens | 66K tokens |
| Output Speed (Tokens / sec) | 45 tok/s | 65 tok/s |
| Time to First Token (TTFT Latency) | 1.20s (1200 ms) | 0.62s (620 ms) |
| 3. Official Provider Release Specs, API Quotas & Long-Context Rules | ||
| Official API Model ID (SDK) | meta-llama/Llama-4-Behemoth | nemotron-3-ultra-550b-a55b |
| API Protocol & Endpoint | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Lifecycle & GA Support Window | Active GA (Guaranteed support through 2028+) | Active GA (Guaranteed support through 2028+) |
| 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) | Flat rate ($1/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Cached input at $0.25/1M tokens |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| 4. Agentic Capabilities, Self-Hosting VRAM & Enterprise Compliance | ||
| Computer Use / Browser GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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) · 1240 GB VRAM (4-bit Quantized) · 4300 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 341 GB VRAM (4-bit Quantized) · 1183 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 | Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) · Open Weights (Self-Hostable) | Colossal Sparse MoE (550B Total / 55B Active) · Open Weights (Self-Hostable) |
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