Compare Models
Side-by-side pricing and specs. Pick two models, set your token volume, choose your currency.
| Spec | GPT-5.2 | Claude Opus 4.6 |
|---|---|---|
| Provider | OpenAI | Anthropic |
| Input / 1M tokens | $1.75 | $5.00 |
| Output / 1M tokens | $14.00 | $25.00 |
| Input cost (1,000,000 tokens) | $1.75 | $5.00 |
| Output cost (1,000,000 tokens) | $14.00 | $25.00 |
| Combined, in + out (1,000,000 each) | $15.75 | $30.00 |
| Context window | 400K | 1.0M |
| Max output | 16K | 32K |
Cost at Scale (Input Tokens)
Exchange rates are approximate. Pricing as of July 2026. The green highlight indicates the better value for each metric.
How to Compare LLM Models
- 1
Pick two models
Select any two models from the dropdown menus. Mix providers freely: compare GPT-5 against Claude, Gemini against Grok, or any combination.
- 2
Set your token volume
Use the slider to set your expected monthly token usage. The cost comparison updates in real time.
- 3
Read the comparison
Review pricing differences, context window sizes, output limits, and the volume cost chart. The cheaper model is highlighted automatically.
Why Use This Comparison Tool
- Any-to-any model comparison, pick from 148+ models across 26 providers
- Volume-based cost chart showing where one model becomes cheaper than another
- Savings percentage calculated automatically between the two models
- Multi-currency support with instant conversion (USD, EUR, GBP, INR, JPY)
- Context window and max output comparison alongside pricing
Common Use Cases
Migration planning
Compare your current model against alternatives before switching. See exact cost differences at your usage level.
Budget justification
Generate a clear side-by-side comparison to present to stakeholders when proposing a model change.
Performance vs. cost tradeoff
Compare a premium model against a cheaper alternative to decide if the cost difference is worth it.
Cross-provider evaluation
Put models from different providers head to head: OpenAI vs Anthropic, Google vs xAI.
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Frequently Asked Questions
Common questions about comparing LLM models