GPT-5.6 Sol costs $5/$30 per million tokens; K3 lists at $3/$15 with cache hits at $0.30 — and ships its weights. GPT answers fast with effort control; K3 reasons at full depth every call. Here is the honest trade.
Unofficial tool. Not affiliated with Moonshot AI or Kimi. Prices and scores are gathered from public sources and may lag official changes.
| Kimi K3Moonshot | GPT-5.6 SolOpenAI | GPT-5.6 TerraOpenAI | |
|---|---|---|---|
| Input / 1M tokens | $3.00 | $5.00 | $2.50 |
| Cached input / 1M | $0.30 | $0.50 | $0.25 |
| Output / 1M tokens | $15.00 | $30.00 | $15.00 |
| Context window | 1,049K | 1,050K | 1,050K |
| Open weights | |||
| Vision input | |||
| Reasoning control | Always max | Adjustable | Adjustable |
| Self-hostable | |||
| Released | Jul 2026 | Jul 2026 | Jul 2026 |
Prices are vendor list prices, July 2026. Capability rows are factual specs; we deliberately don't print head-to-head benchmark scores here because vendors measure under different harnesses — see the K3 overview for K3's reported numbers with caveats.
Drag to match your traffic. Cache-hit rate matters more than list price for agent workloads.
Cost per 1,000 calls at list prices. All models get the same cache-hit rate (each vendor discounts cached input ~90%). K3 always reasons, so its real output token count runs higher than a model answering directly — drag output up to model that.
The short version: K3 undercuts GPT-5.6 Sol on both sides of the meter and matches Terra's output price while claiming stronger agentic scores — but GPT keeps the edge on short-call economics, knowledge breadth, and ecosystem, and the July 9 GPT-5.6 refresh sharpened all three tiers. If your workload is agents with warm caches, price K3 first; if it's high-volume interactive chat, Terra's adjustable effort (or Luna at $1/$6) probably wins. Vendor benchmarks aren't apples-to-apples — test on your own tasks.
Against GPT-5.6 Sol, yes at list price: $3 vs $5 per 1M input tokens and $15 vs $30 output. Against GPT-5.6 Terra ($2.50/$15) it's roughly even — GPT is slightly cheaper on input, identical on output — and GPT-5.6 Luna ($1/$6) is far cheaper for simple traffic. All vendors discount cached input ~90%. The wildcard is that K3 always reasons and bills reasoning as output, which raises its effective output usage on short tasks.
Reported figures put K3 at 76.8 on SWE-bench Verified and 88.3 on Terminal-Bench 2.1 — the agentic numbers are where it claims an edge, the raw SWE score is competitive rather than dominant against frontier GPT. These are July 2026 vendor-adjacent numbers, not independent head-to-heads; the honest answer is to run both on your own repo.
They're effectively equal: K3 at 1,048,576 tokens, the GPT-5.6 family at 1,050,000. K3's differentiator is flat pricing across the entire window plus the $0.30/1M cache-hit rate, which makes repeatedly stuffing the window with a whole repo economically sane.
K3 is open-weight: Moonshot announced the 2.8T-parameter weights under a Modified MIT license (full release stated for late July 2026), so you can self-host or fine-tune. GPT models are API-only. Training data and pipeline for K3 are not released, so 'open-weight' rather than fully open source is the accurate term.
NottoAI has Kimi K3, GPT-5.6 Sol and GPT-5.6 Terra in one model switcher — ask the same question, flip models mid-thread, and judge on your own work. Free to start, no API keys required.
Kimi K3 Toolkit
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