Kimi K3 vs Claude Opus 4.8 — the first matchup where an open-weight model outscores a Western frontier flagship. K3 posts 57 on the Artificial Analysis Intelligence Index against Opus 4.8’s 56, at roughly 40% lower list pricing. Below is the full side-by-side: specifications, API pricing, context window, local hardware requirements, and a clear, data-driven recommendation on which to pick.
| Spec | Kimi K3 | Claude Opus 4.8 |
|---|---|---|
| Developer | Moonshot AI | Anthropic |
| Type | LLM (MoE, reasoning, multimodal) | LLM (reasoning) |
| Parameters | 2.8T total / 16 of 896 experts active (MoE) | Undisclosed |
| Context window | 1M | 1M |
| Modality | Text, Vision → Text | Text, Vision → Text |
| License | Open weights (due 2026-07-27) | Proprietary |
| Open weights | ✅ Yes | ❌ No |
| Input price ($/1M) | $3.00 | $5.00 |
| Output price ($/1M) | $15.00 | $25.00 |
| VRAM (4-bit) | ~1.4 TB | — |
| Min GPU (local) | Multi-node cluster (e.g. 2× 8-GPU H200 141GB nodes) | — |
| Released | 2026-07 | 2026 |
Key differences
- Cost: Kimi K3 is 67% cheaper than Claude Opus 4.8 on a blended-token basis.
- Openness: Kimi K3 is open-weight (self-hostable, private, fine-tunable); Claude Opus 4.8 is proprietary (API-only, but fully managed).
- Run Kimi K3 locally: ~~1.4 TB at 4-bit (min Multi-node cluster (e.g. 2× 8-GPU H200 141GB nodes)).
Which should you choose?
Choose Kimi K3 if you want the lower per-token cost for high-volume workloads, or you want to self-host, fine-tune, or keep data fully private.
Choose Claude Opus 4.8 if you prefer a fully managed API with no infrastructure to run.
→ Estimate real costs in the API cost calculator · check local hardware in the VRAM calculator · browse all 30+ models.
The verdict
Pick Kimi K3 if you run agentic or long-context workloads and want the best intelligence-per-dollar at the frontier: it delivers about 6.3 intelligence points per blended dollar versus 3.7 for Opus 4.8 — roughly 1.7× better value — with a 1M-token context and weights due 27 July 2026 for teams that must self-host. Its BrowseComp (91.2%) and Terminal-Bench 2.1 (88.3%) scores are the strongest published at release.
Pick Claude Opus 4.8 if you need a proven, enterprise-supported model with mature tooling, stronger safety guarantees and predictable behaviour in production. The one-point index gap is inside the noise; the ecosystem gap is not.
And consider neither if your workload is ordinary chat, summarisation or classification. Both are frontier-priced. GLM 5.2 returns 2.8× more capability per dollar than K3, and DeepSeek V4-Flash around 30× — see the full ranking in our 2026 AI Price-Performance Index. Full background on the newcomer is in our Kimi K3 explainer.
All specs and prices are pulled live from our AI models database and kept current. Compare either model against others, or estimate your own monthly spend with the free API cost calculator.
