OpenAI GPT-5.6 Sol (1M) vs Moonshot Kimi K3.7 (512k)
Side-by-side comparison of pricing, context window, capabilities, and a real-cost sample workload.
Option A
OpenAI GPT-5.6 Sol (1M)
by OpenAI
Context:1M
Input:$12.00 / 1M
Output:$72.00 / 1M
Released:2026-07
Option B
Moonshot Kimi K3.7 (512k)
by Moonshot AI
Context:512K
Input:$0.60 / 1M
Output:$2.50 / 1M
Released:2026-07
Detailed Comparison
| Dimension | OpenAI GPT-5.6 Sol (1M) | Moonshot Kimi K3.7 (512k) |
|---|---|---|
| Provider | OpenAI | Moonshot AI |
| Context window Winner | 1M | 512K |
| Input price ($/1M) Winner | $12.00 | $0.60 |
| Output price ($/1M) Winner | $72.00 | $2.50 |
| Sample workload cost Winner 1M input + 500K output tokens | $48.00 | $1.85 |
| Released TieTie | 2026-07 | 2026-07 |
| Tokenizer | o200k_base | kimi |
The verdict
On the dimensions we measured, Moonshot Kimi K3.7 (512k) wins more often - particularly on cost-effectiveness for a typical 1M+0.5M workload.
OpenAI GPT-5.6 Sol (1M) - Key features
OpenAI's Sol tier for GPT-5.6: a long-thinking configuration that spends far more reasoning tokens per request and tops OpenAI's own agentic and math evaluations. Priced as a premium reasoning endpoint.
- Extended reasoning by default
- ~1M token context window
- 128K max output tokens
- Parallel tool calling
- Priority processing tier
Moonshot Kimi K3.7 (512k) - Key features
Moonshot's flagship Kimi K3.7, an open-weight mixture-of-experts model with a 512K token window. K3.7 closes most of the gap to Western frontier models on agentic coding while staying dramatically cheaper.
- 512K token context window
- Open-weight MoE architecture
- Strong agentic tool use
- Aggressive price per token
- Native long-horizon planning
How to choose
- Pick the cheaper model if your workload is mostly straightforward classification, extraction, or summarization.
- Pick the bigger context if you process long documents, large codebases, or multi-document research.
- Pick the more recent release if you need state-of-the-art reasoning quality and don't mind paying a bit more.
- Use both via a routing layer - send simple tasks to the cheaper one and complex tasks to the smarter one. This is the highest-ROI optimization in production AI.
Estimate the real cost of either model for your prompts using our Token Calculator.