Kimi K3 vs. GPT-5.6 Sol: Performance, cost, and tradeoffs

Moonshot AI released Kimi K3 around the same time OpenAI launched GPT-5.6 Sol.

The Artificial Analysis Intelligence Index independently measures models on coding, agentic, and reasoning benchmarks. As of October 2, 2026, Sol (max) leads K3 (max) by three points, 47 to 44.

But provider benchmarks alone don’t tell the whole story. This article covers how each model compares on price, input context handling, weights access, and architecture, and how these factors can help teams decide which one fits your workload.

Kimi K3 vs. GPT-5.6 Sol at a glance

Sources for this table: Artificial Analysis model comparison, Kimi K3 tech blog, Kimi API pricing, OpenAI GPT-5.6 Sol model page.

Price

Kimi K3 costs $3.00 per 1M input tokens and $15.00 per 1M output tokens. GPT-5.6 Sol costs $4.00 and $20.00, roughly 1.3x more. Caching and context length widen that gap. Tokens used per task narrow it.

Prompt caching bills the repeated part of a request, like a system prompt or shared context resent every turn in an agent loop, at a lower rate instead of full price each time.

Kimi K3 caches automatically and bills the first write of a prefix as its own line item. A write costs $3.00 per 1M tokens on the default 5-minute TTL, the same as uncached input, and $6.00 on the 1-hour TTL. Sol charges $0.40 per 1M for cached reads and $5.00 per 1M for cache writes. Any Sol request above 272K input tokens moves to a long-context tier at $8 input / $30 output, per OpenAI’s pricing page.

Cache economics depend on the request shape: prefix size, number of repeats, cache-write policy, fresh input, output, and any long-context threshold. Price the actual workload with the same assumptions for both models.

Three workloads show how the two compare when both models use the same number of tokens:

Sol costs 2.6x more on the repo review. Its long-context tier doubles the input price above 272K input tokens. Kimi's published rate stays flat across its context window.

Both models bill reasoning tokens as output, and K3 produces more of them. At max effort, Artificial Analysis measures 48k output tokens per task for K3 and 29k for Sol across its Intelligence Index. The cost per task comes to $2.00 for K3 and $1.99 for Sol. In the agent loop above, K3 stays cheaper until its output reaches about 1.5x Sol's. K3's low and high effort settings reduce its reasoning output.

Input context

The two context windows are the same size, and K3 leads on the one independent long-context test. Neither model has an independent result past 100K tokens.

Kimi K3 has a 1,048,576-token total context window shared by input and output. GPT-5.6 Sol has a 1,050,000-token total context window, with up to 922,000 input tokens and 128,000 output tokens. K3 allows max_completion_tokens up to its full context limit, but the input and requested output must fit within that limit together.

Artificial Analysis tests long-context reasoning by asking questions that require combining information from documents of 10K to 100K tokens (roughly 15 to 150 pages). As of October 2, 2026, K3 answers 89% correctly and Sol answers 84%.

Above 100K tokens, the only figures come from the vendors. OpenAI reported 36.6% for GPT-5.4, an earlier model, on MRCR v2's eight-needle test in the 512K–1M band in its GPT-5.4 release.

Moonshot reports a 91.2% BrowseComp score for K3 with context compaction triggered at 300K tokens and 90.4% with the full 1M-token context and no compaction. Neither figure measures these two models against each other at that depth. Test either model at the context depth your workload actually uses.

Weights

Kimi K3’s weights are open, under the custom Kimi K3 License. GPT-5.6 Sol is available through OpenAI’s API. That difference shapes licensing terms and deployment choices. Sol’s closed API does not provide weight access, self-hosting, or fine-tuning of the underlying weights.

K3 ships under the custom Kimi K3 License, not an MIT license. Its commercial terms and display conditions need to be read against the governing license text, including conditions that apply when the licensee’s and affiliates’ aggregate revenue exceeds $20M over any consecutive 12-month period.

Moonshot’s requests run on servers in Singapore under terms that permit training on your content by default unless you negotiate a separate enterprise agreement. But because the weights are open, Moonshot's own endpoint isn't your only option if its terms don't work for you.

You can self-host on a single node of 8 B300 GPUs or on 16 B200/GB200-class GPUs across two nodes, or run it through a managed provider. Parasail runs K3 through an OpenAI-compatible shared serverless endpoint at $3.00 input / $15.00 output per 1M tokens (matching Moonshot's own rate). Teams that want a private, single-tenant endpoint instead can run on Elastic Endpoints, still billed per token, with dedicated engineering support included.

With Sol, whatever OpenAI ships is what you get. OpenAI does not offer on-premises or air-gapped deployment for Sol. It does offer regional storage in multiple regions and regional processing for eligible models through supported US and European endpoints, so regional requirements need to be checked against the specific endpoint rather than treated as categorically unavailable. You also can’t inspect or fine-tune the underlying weights.

Architecture and inference controls

K3’s architecture is published while Sol’s remains undisclosed, but both APIs expose request-level reasoning controls. K3 supports low , high , and max , defaulting to max. Sol offers six effort levels from none through max .

Kimi K3 architecture

K3 routes each token through 16 of its 896 experts, plus 2 shared experts that fire on every token. That activates roughly 104B of its 2.8T parameters per step. Its 93 attention layers are split across two mechanisms:

1. Kimi Delta Attention (KDA) → 69 layers: cheap and linear-cost

Standard attention costs more as context grows. KDA keeps a fixed-size recurrent state in place of a growing cache, so the memory these layers need does not grow with context.

2. Gated MLA → 24 layers: exact recall, cost grows with context

These layers run full attention across the whole context, which preserves exact recall of earlier tokens. Their compute and memory grow with context, so K3's total cost of processing context is not flat. Most models run full attention in every layer. K3 runs it in about a quarter of them.

This design helps explain how K3 can target frontier-class performance at a lower published token price than Sol. Model price is still a commercial decision rather than a direct measure of inference cost. Moonshot reports roughly 2.5x scaling efficiency over K2 from its architecture and training changes combined.

GPT-5.6 Sol architecture

Sol is closed, so there’s no attention mechanism or expert routing to unpack the way there is for K3. Its API exposes reasoning.effort with six settings: none , low , medium , high , xhigh , or max (default medium ). Reasoning tokens bill as output, so effort is a request-level cost and quality control.

Sol’s measured quality, cost, and latency change with reasoning effort. A fair comparison with K3 needs to specify the reasoning setting used for both models.

Other meaningful differences

Time-to-first-token: Artificial Analysis’s current first-party Kimi K3 (max) measurement is about 4.5 seconds; its GPT-5.6 Sol (max) measurement is about 110 seconds. K3 streams its reasoning, so its figure is the first reasoning chunk. Its first answer token arrives at around 63 seconds. Sol’s TTFT varies materially by reasoning effort and measurement window; compare the relevant endpoint, effort setting, and service tier before treating either result as a product guarantee.

Throughput: Artificial Analysis’s current first-party Kimi K3 (max) measurement is around 35 tokens/sec; its GPT-5.6 Sol (max) measurement is about 80 tokens/sec. Throughput depends on endpoint, effort, and service tier, so use current, like-for-like measurements for a production decision.

Image input accuracy: On MMMU-Pro, GPT-5.6 Sol (max) scores 83% versus Kimi K3 (max) at 81%.

Accuracy vs. hallucination rate: On AA-Omniscience, GPT-5.6 Sol (max) scores 59% accuracy versus Kimi K3 (max) at 48%, but its hallucination rate is 92% versus K3’s 53%. Artificial Analysis defines that rate as incorrect answers among non-correct responses, not simply the inverse of accuracy.

Which model is best for your workload

High-volume agent workloads → Use Kimi K3

K3 has lower published token prices, and Kimi's published API rate stays flat across its supported context window. At max effort, K3's extra output brings its cost per task level with Sol's, and Sol leads on terminal-heavy coding. Test agent workloads on both models. Recalculate cache economics against your prefix reuse, output volume, and context length.

Time-to-first-token-sensitive workloads → Use Kimi K3

An agent making many small tool calls pays time-to-first-token on every call, so that delay compounds in a loop. Compare current, like-for-like TTFT measurements for the endpoint and reasoning settings you plan to use.

Throughput-sensitive workloads → Use GPT-5.6 Sol

A long document, a large diff, or bulk summarization spends most of its time streaming tokens after the first one arrives. Compare current, like-for-like throughput measurements by endpoint and reasoning setting before choosing a model for that workload.

Data residency, license control, or workload-specific tuning → Use Kimi K3

Route through a managed provider if you don't want to own a GPU cluster, or self-host if you already have the infrastructure. Open weights also mean you can fine-tune K3 itself for your workload, something Sol's closed API doesn't allow.

Most production workloads are a mix of several of these. If you're weighing K3 against Sol for a specific workload, talk to an engineer and we'll work through the variables with you.

FAQ

How do Kimi K3 and GPT-5.6 Sol compare on API pricing per million tokens, and which is cheaper for high-volume agentic workloads?

K3 costs $3.00 input / $15.00 output per 1M tokens; Sol costs $4.00 / $20.00 at its short-context rate. Kimi’s published API rate stays flat across its supported context window, while Sol’s price changes above its 272K input threshold. At max effort, K3 uses more output tokens per task, which brings the two to $2.00 and $1.99 per task on Artificial Analysis's index. Recalculate high-volume workloads with the same cache and context assumptions for both models.

What are the context window limits for each model, and how do those limits affect long-document and multi-turn agent tasks?

K3 has a 1,048,576-token total context window shared by input and output. Sol has a 1,050,000-token total context window, with up to 922,000 input and 128,000 output tokens. For long documents or multi-turn agent loops, test both models at the depth your workload actually uses; the cited evidence does not establish retrieval quality past 500K tokens for either model.

Is Kimi K3's open-weights release a practical alternative to the closed GPT-5.6 Sol API for self-hosting teams?

Technically yes, but not practically for most teams: vLLM's own day-0 guidance calls for at least 8 B300 GPUs on one node, or 16 B200/GB200-class GPUs across two, just to serve it. Most teams that want K3's open-weight pricing and license terms get it through a managed provider instead of standing up that cluster themselves.

Which model performs better on software engineering benchmarks like Terminal-Bench?

On Artificial Analysis’s Terminal-Bench 4.0 run, GPT-5.6 Sol (max) scores 40% versus Kimi K3 (max) at 13%. Artificial Analysis now tags Terminal-Bench 2.1 as a legacy evaluation. It is one narrow software-engineering benchmark; cost, latency, and deployment still decide the broader fit.

Are there compliance concerns around Moonshot AI in a production environment?

Moonshot’s own API hosts data in Singapore and its terms permit training on your content by default unless you negotiate a separate enterprise agreement. Since K3’s weights are open, teams are not required to use Moonshot’s endpoint; a managed provider or self-hosted deployment may offer different data-location and training terms. Verify those terms with the chosen provider.