OpenAI's flagship, Sol, is priced at $5 / $30 per million tokens. Kimi K3 is $3.00 / $15.00 — 40% less on input and half on output. On published third-party rankings the two sit close together, with Sol placing above K3 on the Artificial Analysis Intelligence Index. So this is a real capability-versus-cost trade rather than a tier mismatch.
Kimi K3 vs GPT-5.6 Sol at a glance
| Kimi K3 | GPT-5.6 Sol | |
|---|---|---|
| Price (per 1M tokens) | $3.00 in / $15.00 out | $5 in / $30 out |
| Context window | 1M tokens | 1.5M tokens |
| Max output | 131,072 tokens (up to 1M) | Not published |
| Open weights | Yes — Kimi K3 License | No |
| Vision | Native, images and video | Yes |
| Reasoning | Always on; three effort levels | Configurable |
| Parallel mode | K3 Swarm Max | Sol Ultra (parallel sub-agent mode) |
| Cloud availability | Kimi API, or self-hosted | Azure-centric |
GPT-5.6 Sol figures are from our GPT-5.6 vs Claude Fable 5 breakdown, current as of July 2026. Relative intelligence-index placements are third-party (Artificial Analysis) and reflect that index's methodology at a point in time; there is no published direct head-to-head benchmark between these two models.
Choose Kimi K3 if
- Output-heavy workloads. $15 against $30 per million output tokens is a 2x difference, and generation-heavy work is where that lands hardest.
- Open weights, self-hosting optionality, and no dependence on a single vendor's roadmap.
- Video input as a first-class capability.
- Cost-constrained agentic work, where token spend is the binding constraint on how much you can run.
Choose GPT-5.6 Sol if
- Single-shot ingestion beyond 1M tokens — Sol's 1.5M window is a genuine 50% edge.
- Existing OpenAI tooling, Codex workflows, and Azure-first infrastructure.
- Independent rankings place Sol above K3 on general intelligence; if you are buying the top of the market rather than value, that matters.
- Enterprise agreements and compliance posture already established with OpenAI or Microsoft.
The verdict
K3 is the value play and Sol is the capability play, with a smaller gap between them than the price difference implies. If your workload is generation-heavy, the 2x output-price difference is likely to dominate any benchmark delta. Run twenty representative tasks on both before committing — the published scores are not measured on your work.
Whichever way you lean, run the decision on your own workload rather than a benchmark table. Twenty representative tasks from your real queue will tell you more than any published score — and because K3 is OpenAI- and Anthropic-compatible, setting up that comparison is a base URL change rather than a project.
More on Kimi K3
Start with the complete Kimi K3 guide for the overview, or go deeper:
Ready to go deeper?
Read the full Kimi K3 guideFrequently Asked Questions
Which is cheaper, Kimi K3 or GPT-5.6 Sol?
Kimi K3, on both sides: $3 against $5 per million input tokens, and $15 against $30 per million output. The output difference is the significant one — a full 2x — and it compounds fast on generation-heavy or agentic workloads.
Which has the bigger context window?
GPT-5.6 Sol, at 1.5M tokens against K3's 1,048,576 — a genuine 50% advantage for single-shot ingestion of very large corpora. K3 counters with a published output ceiling of up to 1M tokens, where Sol's maximum output has not been published.
Is GPT-5.6 Sol smarter than Kimi K3?
On third-party index rankings, Sol places above K3 — which sits in the top five overall, behind Claude Fable 5 and Sol. Those indices measure aggregate performance on public benchmarks, not your workload. Given the 2x output price difference, the practical question is whether the gap is worth double.
