Gemini 4 Argon vs GPT-6.1 Sol
At 6,000 chat requests per month (1,500 in / 400 out), the two are within a few percent: $42 versus $42.
| Spec | Gemini 4 Argon | GPT-6.1 Sol |
|---|---|---|
| Vendor | OpenAI | |
| Input / 1M tokens | $2 | $2 |
| Output / 1M tokens | $10 | $10 |
| Cached input / 1M | $0.100 | $0.100 |
| Context window | 1.0M tokens | 1.1M tokens |
| Max output | 1.0M tokens | 128K tokens |
| Input modalities | text, image, audio, video | text, image |
| Released | — | — |
| Cost per 1M cached reads | $0.100 | $0.100 |
Published list prices, last verified Oct 15, 2026. Source links are on each model's page.
Cost at three workloads
| Workload | Gemini 4 Argon | GPT-6.1 Sol | Difference |
|---|---|---|---|
| Customer-support reply (1,500 in / 400 out) | $0.0070 | $0.0070 | — |
| Document summary (20,000 in / 1,000 out) | $0.050 | $0.050 | — |
| Code review pass (8,000 in / 1,500 out) | $0.031 | $0.031 | — |
Which one fits your workload
- Long context: GPT-6.1 Sol accepts more — 1.1M versus 1.0M tokens. If your retrieved context regularly exceeds 1.0M, only one of these will hold it.
- Long answers: output is priced the same per token, so the difference is purely how many tokens you generate.
- High-volume, short answers: input is priced identically.
- Caching: at least one of these vendors publishes a discounted cached-input rate; if you resend the same system prompt and context, model the cache-hit rate or you will overestimate cost.
This comparison uses published price and specification data only. It does not rank output quality — run your own evaluation on your own prompts before switching a production default.