Kimi K3 vs GPT-5.6 Sol: API Price, 1M Context, Coding, and Caching

Kimi K3 is cheaper than GPT-5.6 Sol at standard API rates, while GPT-5.6 Sol remains the stronger proprietary quality reference. K3 is the more unusual long-output option: its public API allows a completion limit up to 1,048,576 tokens, versus 128K for Sol in the current canonical data.

Moonshot itself says K3 still trails GPT-5.6 Sol overall. The decision is therefore not “which launch post won?” It is whether Sol’s higher task success rate is worth its price premium for your workload.

Price and Specification Comparison

	Kimi K3	GPT-5.6 Sol
Standard input / 1M	$3.00	$5.00
Cached input / 1M	$0.30	$0.50
Cache write / 1M	No separate public price	$6.25
Output / 1M	$15.00	$30.00
Context	1M	1.05M
Maximum output	Up to 1,048,576	128K
Multimodal input	Text, image, video	Text, image
Structured output	JSON mode and strict JSON Schema	Structured output
Reasoning control	Always max in API	Tier-dependent controls
Model availability	Hosted API; weights due by July 27	Proprietary hosted API

GPT-5.6 requests above 272K input tokens enter a higher price tier: $10/M input, $1/M cached input, $12.50/M cache writes, and $45/M output. K3 publishes a flat price across its context window.

Example Task Cost

For 2M input tokens and 500K output tokens, before caching:

Model	Calculation	Cost
Kimi K3	2 × $3 + 0.5 × $15	$13.50
GPT-5.6 Sol	2 × $5 + 0.5 × $30	$25.00

This simplified example assumes each Sol request stays below its long-context threshold. A single request above 272K input tokens should use the higher Sol rates.

At the same token shape, K3 costs 46% less. But cost per token is not cost per completed task. If one model needs more retries, longer reasoning, or more human repair, its effective advantage can disappear.

Caching Is Similar in Price, Different in Operation

Both models discount a cache hit by 90% relative to standard input. Their write behavior differs:

• K3 automatically matches stable context prefixes and does not expose a separate cache-write charge.
• GPT-5.6 Sol publishes explicit cache-write and cache-read prices.

For K3, place stable instructions and tool schemas first. For Sol, model the initial write as well as subsequent reads. Do not compare only the cached-read column for workflows that constantly change the prompt prefix.

Where K3 Fits Better

Choose K3 as an evaluation candidate when:

• You need image or video input in a long-horizon agent.
• Your workflow can reuse large, stable prompt prefixes.
• A completion may need to exceed 128K tokens.
• Flat pricing across a 1M context is operationally useful.
• You want a path toward open-weight deployment after the announced release.

K3 also supports dynamic tool loading, which can reduce tool-schema overhead when an agent has a large catalog.

Where GPT-5.6 Sol Fits Better

Choose Sol as the quality baseline when:

• Hard coding or reasoning failures are more expensive than token spend.
• You rely on OpenAI’s existing API ecosystem and operational controls.
• You need the strongest model before optimizing cost downward.
• Your evaluation proves Sol finishes more tasks with fewer retries.

The most credible source on the comparison is unusually direct: Moonshot’s K3 launch says K3 remains behind GPT-5.6 Sol overall. That does not settle every domain, but it prevents an unsupported claim that K3 is universally stronger.

Practical Recommendation

Run both models on a fixed evaluation set. Record first-pass completion, tool-call validity, tests passed, total billed tokens, cache-hit rate, latency, and human repair time. Start Sol as the quality ceiling and K3 as the cost challenger.

For a routed agent, K3 can handle long-context planning and normal patches, while Sol receives the failures that justify a premium. Read the K3 API pricing guide and use the pricing calculator for your own token ratio.

Official sources checked July 19, 2026: Kimi K3 launch, K3 API quickstart, Kimi global API pricing, OpenAI API pricing, and OpenAI model docs.