Model decision surface
Compare AI models
Start with GPT-5.4 Mini and GPT-6 Luna, or choose any two current ranked language models. Compare capability evidence, price, context, provider availability, and weight access without pretending one field decides every use case.
Current public data
GPT-5.4 Mini vs GPT-6 Luna
Live dataset updated 9/22/2026, 10:37:09 PM UTC
Open 1200×630 evidence receipt| Field | GPT-5.4 Mini OpenAI | GPT-6 Luna OpenAI |
|---|---|---|
| ModelCap position | #34 | #25 |
| Index score | 71.9 | 78.6 |
| Evidence | Measured4 public benchmark observations across 4 boards | Measured3 public benchmark observations across 3 boards |
| Input / 1M | $0.75 | $0.10 |
| Output / 1M | $4.50 | $0.50 |
| Pricing status | fresh | fresh |
| Context | 400K | 1M |
| Providers | 2 | 2 |
| Weight access | API only | API only |
Decision facts
- GPT-5.4 Mini is #34; GPT-6 Luna is #25 on the same current language board.
- Index scores are 71.9 for GPT-5.4 Mini and 78.6 for GPT-6 Luna. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.
- Both positions use Measured evidence.
- Listed output price per 1M tokens is $4.50 for GPT-5.4 Mini and $0.50 for GPT-6 Luna. For 1,000 requests with 2,000 input and 500 output tokens each (2M input + 0.5M output), the listed-rate estimate is $3.75 for GPT-5.4 Mini versus $0.45 for GPT-6 Luna. GPT-6 Luna costs 88.0% less in this scenario. This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the selected endpoint before budgeting.
- Published context is 400,000 tokens for GPT-5.4 Mini and 1,050,000 for GPT-6 Luna.
These are separate published fields, not a synthetic winner. ModelCap does not collapse price, access, context, and capability evidence into a hidden recommendation score.
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Each comparison page is a permanent, shareable URL with the same live figures as this tool: ModelCap Index position, API pricing, context window, provider count, weight access and every shared benchmark board.