Model Intelligence
Model Comparison Engine
Public pricing, benchmarks, and live side-by-side runs.
| Model | Vendor | Context | Input $/1M | Output $/1M | Latency | Accuracy | RAG fit | Enterprise |
|---|---|---|---|---|---|---|---|---|
| GPT-5.2 | OpenAI | 256K | $1.75 | $14.00 | 78 | 92 | 90 | 95 |
| Claude Sonnet 4.5 | Anthropic | 200K | $3.00 | $15.00 | 75 | 94 | 94 | 93 |
| Gemini 3 Flash | 1000K | $0.50 | $3.00 | 95 | 88 | 92 | 88 | |
| Claude Opus 4.8 | Anthropic | 200K | $5.00 | $25.00 | 60 | 97 | 92 | 94 |
| Llama 3.3 70B | Meta (Groq hosted) | 128K | $0.59 | $0.79 | 88 | 82 | 80 | 85 |
| Qwen 3 72B | Alibaba (Together hosted) | 128K | $0.86 | $1.74 | 76 | 84 | 82 | 80 |
GPT-5.2
OpenAIStrengths
- +Best-in-class structured output
- +Strong reasoning across domains
- +Mature function calling
- +Wide tool ecosystem
Weaknesses
- −Higher output cost vs Gemini Flash for high-volume
- −Stricter content policies in regulated industries
Use Cases
Agentic workflowsCode generationEnterprise RAGAnalytics copilots
Claude Sonnet 4.5
AnthropicStrengths
- +Exceptional long-document comprehension
- +Strong instruction following
- +Safer default behavior for regulated industries
- +Excellent for nuanced writing
Weaknesses
- −Slower than Flash-class models
- −Output cost matches GPT class
Use Cases
Legal/compliance RAGExecutive report writingCustomer support agentsPolicy summarization
Gemini 3 Flash
GoogleStrengths
- +Largest context window (1M tokens)
- +Lowest cost in this class
- +Very low latency
- +Strong multimodal capability
Weaknesses
- −Slightly behind on hardest reasoning benchmarks
- −Newer enterprise tooling vs OpenAI
Use Cases
High-volume customer supportDocument QA at scaleReal-time copilotsMultimodal analysis
Claude Opus 4.8
AnthropicStrengths
- +Top-tier reasoning
- +Best for high-stakes analysis
- +Adaptive thinking
Weaknesses
- −Most expensive in class
- −Higher latency
Use Cases
Strategy advisoryComplex researchCritical-path decisioning
Llama 3.3 70B
Meta (Groq hosted)Strengths
- +Self-hostable (data sovereignty)
- +Open weights
- +Lowest hosted-API cost
Weaknesses
- −Self-host requires GPU infra (~$4K+/mo TCO)
- −Behind on agentic benchmarks
Use Cases
Air-gapped deploymentsDefense/banking on-premCost-controlled inference
Qwen 3 72B
Alibaba (Together hosted)Strengths
- +Strong multilingual (CJK)
- +Open weights
- +Competitive reasoning
Weaknesses
- −Smaller ecosystem in West
- −Self-host adds GPU TCO
Use Cases
Multilingual customer supportAPAC-region deploymentsOn-prem RAG
Live Side-by-Side Benchmark
Same prompt, every selected model, real latency.