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Compare these two on your own workload

Published rates rank differently once prompt caching, batch discounts and your own input-to-output ratio are applied. Run both against your numbers.

Grok 4.7 vs GPT-6.1 Sol

The same $2 input price; Grok 4.7 output costs 40% less, but it has no batch discount.

Prices verified 12 Oct 2026
Input / 1M$2.00
Output / 1M$6.00
Cached input / 1M$0.500
Batchn/a
Context500K tokens
Blended (3:1)$3.00
Input / 1M$2.00
Output / 1M$10.00
Cached input / 1M$0.100
Batch50% off
Context1.1M tokens
Blended (3:1)$4.00
Break-even. These two swap places at an output share of 0% of total tokens. Below that, GPT-6.1 Sol is cheaper; above it, the other one is. Output-heavy work, code generation, long-form writing, reasoning models that bill their thinking, sits on the far side of that line more often than teams expect.

Monthly cost by workload shape

The same volume costs very different amounts depending on the shape of the traffic. No caching or batch applied.

WorkloadGrok 4.7GPT-6.1 SolDifference
Customer support chatbot
50K/day · 800 in / 300 out
$5,174.80$7,001.20Grok 4.7saves $1,826.40Model it →
RAG / search answers
20K/day · 3000 in / 400 out
$5,113.92$6,088.00Grok 4.7saves $974.08Model it →
Agent / tool-use loop
8.0K/day · 2500 in / 600 out
$2,094.27$2,678.72Grok 4.7saves $584.45Model it →
Batch summarization
100K/day · 5000 in / 500 out
$39.6K$45.7KGrok 4.7saves $6,088.00Model it →
Code assistant
15K/day · 2000 in / 800 out
$4,018.08$5,479.20Grok 4.7saves $1,461.12Model it →

Frequently asked questions

Which is cheaper, Grok 4.7 or GPT-6.1 Sol?

At a typical 3:1 input-to-output ratio, Grok 4.7 is cheaper: $3.00 blended per 1M tokens against $4.00. That flips once output exceeds 0% of your total tokens, because the two price input and output differently.

What is the price difference between Grok 4.7 and GPT-6.1 Sol?

Grok 4.7: $2.00 input, $6.00 output per 1M tokens. GPT-6.1 Sol: $2.00 input, $10.00 output. That is 1.0x on input and 1.7x on output.

Does prompt caching change the answer?

Grok 4.7 caches input at $0.500/1M and GPT-6.1 Sol caches at $0.100/1M. For input-heavy workloads with repeated context, caching can matter more than the base rate difference, model both in the calculator rather than comparing rate cards.

This page compares published prices only. It makes no claim about which model performs better on any task; that depends entirely on your evaluations.

Rate cards decide nothing on their own.

Most teams run several models at once. Finitizer Tokenomics shows what each one actually costs you, read from your own bills and exports, which workloads could test a smaller model, what they would cost on an open-weight model, and, for models you serve yourself, the cost per token beside the API price. A routing decision starts from your spend rather than a price page.

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