Comparateur Multi-Modèles d’IA (2 à 4 Modèles)
Comparez 2 à 4 modèles d’IA de pointe côte à côte : benchmarks de code et raisonnement, tarifs tokens API, fenêtre de contexte et vitesse d’inférence.
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.
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1-Click Preset Multi-Model Stacks (4 Models):
Currently Compared Models:Llama 4 Behemoth vs Gemini 4 Argon 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 | Google · 2026-09 |
|---|---|---|
| 1. Official Benchmarks & Capability Scores (Green = Leader) | ||
| Intelligence Index (Composite) | 93 / 100 | 97 / 100 |
| SWE-bench Verified (Agentic Coding) | 75% | 81.9% |
| GPQA Diamond (PhD-Level Science) | 82% | 81.5% |
| LiveCodeBench (Competitive Coding) | N/A | 80.5% |
| AIME (Olympiad Competition Math) | N/A | 94.8% |
| MMLU-Pro (10-Choice Reasoning) | 88.5% | 90.8% |
| HumanEval (Code Synthesis) | 96% | 97.2% |
| Terminal-Bench (Agentic CLI Execution) | N/A | 57.4% |
| LMSYS Chatbot Arena Elo | 1490 | 1525 |
| 2. Token Pricing, Prompt Cache & Throughput Speed | ||
| Input Price (per 1M tokens) | $0.00 / 1M | $2.00 / 1M |
| Output Price (per 1M tokens) | $0.00 / 1M | $10.00 / 1M |
| Cached Input Price (per 1M tokens) | $0.000 / 1M | $0.100 / 1M |
| Blended Price (7:2:1 Cached Workload) | $0.00 / 1M | $1.47 / 1M |
| Context Window Capacity | 10M tokens | 1M tokens |
| Max Output Tokens | 64K tokens | 1M tokens |
| Output Speed (Tokens / sec) | 45 tok/s | 115 tok/s |
| Time to First Token (TTFT Latency) | 1.20s (1200 ms) | 0.85s (850 ms) |
| 3. Official Provider Release Specs, API Quotas & Long-Context Rules | ||
| Official API Model ID (SDK) | meta-llama/Llama-4-Behemoth | models/gemini-4-argon |
| API Protocol & Endpoint | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| 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 | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| 4. Agentic Capabilities, Self-Hosting VRAM & Enterprise Compliance | ||
| Computer Use / Browser GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Vertex AI Supervised Adapter Tuning & Context Distillation |
| 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) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Supported Inference Engines | vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI | Managed Google Cloud API |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Architecture & Weights License | Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) · Open Weights (Self-Hostable) | Pathways Multimodal Sparse Mixture-of-Experts · Proprietary API |
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