Moonshot AI released Kimi K3 around the same time OpenAI launched GPT-5.6 Sol. As of August 21, 2026, the live benchmark and provider metrics in this article are point-in-time measurements and should be rechecked before publication.
The Artificial Analysis Intelligence Index independently measures models on coding, agentic, and reasoning benchmarks. As of August 21, 2026, Sol (max) leads K3 (max) by one point, 61 to 60. Parasail is the fastest of the 12 providers Artificial Analysis tracks for K3, at 179.3 tokens/sec in the live listing. Provider benchmarks move, so recheck this figure immediately before publication.
Artificial Analysis’s live score and provider measurements change as the underlying evaluations are updated. Treat the August 21 snapshot as a comparison point, not a durable model ranking.
But provider benchmarks aren't enough here. 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 pricing.
Why vendor benchmarks don't tell the whole story
As of August 21, 2026, Artificial Analysis lists an evaluation cost of $2,823.25 for Sol and $2,425.11 for K3. That is a point-in-time benchmark cost, not a general measure of production efficiency. API pricing remains a separate workload-specific decision.
That is the tradeoff a raw score hides. At max effort, Sol has a one-point edge on Artificial Analysis’s Intelligence Index, but it carries a higher published token price. Running both models against your own tasks and budget is the only evaluation that actually answers which one fits your workload. Price, input context, weights, and architecture are what to weigh while you do.
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 further.
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 once a prompt prefix exceeds 256 tokens, with no separate write fee. Sol charges $0.40 per 1M for cached reads plus $5.00 per 1M for cache writes, and any request above 272K tokens moves to a long-context tier at $8 input / $30 output, per OpenAI's prompt caching documentation.
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 demonstrate how Kimi K3 and GPT-5.6 Sol compare on pricing in different scenarios:
The repo-review gap is widest because Sol’s long-context tier changes pricing above 272K input tokens, while Kimi’s published API rate stays flat across its supported context window.
Sol bills reasoning tokens as output. At higher effort settings the model generates more of them, so the real invoice for the agent-loop scenario above runs higher than the visible output length would suggest.
Input context
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.
As of August 21, 2026, Kimi K3 scores 82.7% on Artificial Analysis’s long-context reasoning evaluation. That evaluation uses documents from 10K to 100K tokens, so it does not establish K3’s retrieval performance at 500K–1M tokens.
OpenAI reported a 36.6% result for GPT-5.4 on MRCR v2’s eight-needle test in the 512K–1M band in its GPT-5.4 release. That result applies to GPT-5.4 specifically; it does not establish GPT-5.6 Sol’s behavior at the same depth.
Moonshot reports a 91.2% BrowseComp score when context compaction is triggered at 300K tokens and 90.4% when K3 uses the full 1M-token context without context management. This compares two context-management configurations, not performance at two context-length bands. 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; they include 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 license is open, Moonshot's own endpoint isn't your only option if its terms don't work for you.
You can self-host on a minimum of 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 896 experts plus 2 shared experts that fire on every token, activating roughly 104B of the 2.8T total parameters per step. Decode is memory-bandwidth-bound. K3’s architecture helps explain how it can target frontier-class performance with a lower published token price than Sol, but model price is a commercial decision rather than a direct measure of inference cost.
K3's cost efficiency comes from splitting its attention stack across two mechanisms.
- Kimi Delta Attention (KDA) → 69 layers: cheap and linear-cost
Standard attention costs more as context grows. KDA gives 69 layers a fixed-size recurrent state, reducing how sharply their processing requirements grow with context. K3 still includes 24 Gated MLA layers whose compute and KV-memory requirements remain context-dependent, so the model’s total context-processing cost is not flat.
- Gated MLA → 24 layers: slower and more precise, most models run throughout
The 24 Gated MLA layers retain context-dependent compute and memory requirements. K3’s 82.7% Artificial Analysis result covers 10K–100K documents, while Moonshot’s 91.2% and 90.4% BrowseComp figures compare context-management configurations. Neither result establishes K3’s retrieval quality across the full 1M-token range.
Moonshot reports roughly 2.5x scaling efficiency over K2 from these changes combined. K3’s hybrid attention architecture is fixed at training time, while its API still lets callers vary reasoning effort per request. Kimi currently charges a flat published token rate across the supported context window; that is a commercial pricing decision, not evidence that inference costs remain constant as context grows.
GPT5.6-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.
Artificial Analysis measured GPT-5.6 Sol’s cost at each setting:
Sol’s measured quality, cost, and latency vary substantially by 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 2.69 seconds; its GPT-5.6 Sol (max) measurement is 105.75 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 38.4 tokens/sec; its GPT-5.6 Sol (max) measurement is 73.7 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 in this comparison, and Kimi’s published API rate stays flat across its supported context window. 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 multi-node 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 variable 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. 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 a minimum of 16 B200/GB200-class GPUs across two nodes 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?
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.