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.
2 / 5 Models Active
1-Click Preset Multi-Model Stacks (4 Models):
Currently Compared Models:Aya Expanse 32B vs Llama 3.2 90B Vision 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 | Cohere · 2024-10 | Meta · 2024-09 |
|---|---|---|
| 1. Official Benchmarks & Capability Scores (Green = Leader) | ||
| Intelligence Index (Composite) | 44 / 100 | 44 / 100 |
| SWE-bench Verified (Agentic Coding) | 33.5% | 28.5% |
| GPQA Diamond (PhD-Level Science) | 50% | 48.2% |
| LiveCodeBench (Competitive Coding) | N/A | N/A |
| AIME (Olympiad Competition Math) | N/A | N/A |
| MMLU-Pro (10-Choice Reasoning) | 73% | 73.5% |
| HumanEval (Code Synthesis) | 81% | 84.8% |
| Terminal-Bench (Agentic CLI Execution) | N/A | N/A |
| LMSYS Chatbot Arena Elo | 1265 | 1260 |
| 2. Token Pricing, Prompt Cache & Throughput Speed | ||
| Input Price (per 1M tokens) | $0.25 / 1M | $0.00 / 1M |
| Output Price (per 1M tokens) | $0.50 / 1M | $0.00 / 1M |
| Cached Input Price (per 1M tokens) | $0.060 / 1M | $0.000 / 1M |
| Blended Price (7:2:1 Cached Workload) | $0.14 / 1M | $0.00 / 1M |
| Context Window Capacity | 128K tokens | 128K tokens |
| Max Output Tokens | 8K tokens | 8K tokens |
| Output Speed (Tokens / sec) | 85 tok/s | 75 tok/s |
| Time to First Token (TTFT Latency) | 0.44s (440 ms) | 0.58s (580 ms) |
| 3. Official Provider Release Specs, API Quotas & Long-Context Rules | ||
| Official API Model ID (SDK) | aya-expanse-32b | meta-llama/Llama-3.2-90B-Vision |
| 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 Cohere 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 ($0.25/1M in) across full 128K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Cached input at $0.06/1M tokens | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| 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) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 56 GB VRAM (4-bit Quantized) · 194 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 | High-Capacity Multilingual Transformer · Open Weights (Self-Hostable) | Multimodal Adapter-Fused GQA Transformer · Open Weights (Self-Hostable) |
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