What other models does Moonshot AI offer?

Moonshot AI has iterated rapidly across the Kimi K2 family, releasing progressively more capable variants that expand context length, reasoning depth, and multimodal support. Here are some other models Moonshot AI supports:

• Kimi K2: Kimi K2 is Moonshot AI's original production release of the trillion-parameter MoE architecture, a non-reasoning model with a 128k-token context window and text-only I/O, priced at $0.585 per 1M input tokens and $2.40 per 1M output tokens and positioned for cost-efficient general-purpose inference

• Kimi K2 0905: Kimi K2 0905 is an updated non-reasoning checkpoint from September 2025, extending context to 256k tokens and improving its Intelligence Index ranking to #13 among comparable open-weight non-reasoning models, making it the go-to non-reasoning option in the Kimi family for most production use cases

• Kimi K2.5: Kimi K2.5 is Moonshot AI's reasoning-capable model, adding explicit chain-of-thought support and multimodal input (text, image, video) on top of the same trillion-parameter MoE backbone, ranked #9 of 88 comparable reasoning models on the Artificial Analysis Intelligence Index

• Kimi K2.6 (non-reasoning): The non-reasoning variant of Kimi K2.6 omits chain-of-thought processing for tasks that benefit from faster, more direct responses, ranked #2 of 43 comparable non-reasoning models on the Intelligence Index and outputting approximately 48.7 tokens per second

How does Kimi K2.6 differ from Moonshot AI's other models?

Kimi K2.6 is the latest and highest-capability reasoning model in the Kimi family, ranked #1 of 88 comparable models on the Artificial Analysis Intelligence Index.

• Intelligence ranking: Kimi K2.6 scores 54 on the Artificial Analysis Intelligence Index, placing it first among 88 comparable open-weight models. Kimi K2.5 scored 47 and Kimi K2 0905 scored lower still, putting K2.6 at the top of Moonshot AI's lineup by a meaningful margin

• Multimodal input: Kimi K2.6 accepts text, image, and video inputs. The earlier Kimi K2 and Kimi K2 0905 are text-only, making K2.6 the appropriate choice for any workflow that involves visual content

• Context window: Kimi K2.6 supports a 256k-token context window, matching Kimi K2 0905 and K2.5, and doubling the 128k limit of the original Kimi K2

• Pricing: Kimi K2.6 is priced at $0.95 per 1M input tokens and $4.00 per 1M output tokens, with a blended rate of $0.70 per 1M tokens. The cache hit price drops to $0.16 per 1M tokens, an 83% discount that benefits applications with repeated context

• Speed: Kimi K2.6 generates approximately 44.5 tokens per second, ranking #48 of 88 comparable models. It is notably verbose during evaluation, generating 170M output tokens compared to a median of 43M across comparable models, which can increase per-request costs

• Scale: With 1 trillion total parameters and 32 billion active via a mixture-of-experts architecture, Kimi K2.6 is one of the largest open-weight models available, released under a modified MIT license

Kimi K2.6 is the right choice when you need the highest reasoning quality from Moonshot AI's lineup, your product handles image or video inputs, and your context requirements fit within 256k tokens.

What models should I consider using alongside Kimi K2.6?

No single model is optimal for every task. Here are models worth pairing with Kimi K2.6 depending on what your product needs:

• Kimi K2.6 (non-reasoning) (Moonshot AI): For requests that do not require chain-of-thought deliberation, routing to the non-reasoning variant of K2.6 reduces output latency while staying within the same model family and context window. This is the natural cost-reduction path for simpler workloads within Moonshot AI's ecosystem

• Claude Opus 4 (Anthropic): When your product requires a well-benchmarked reasoning model from a provider with broad enterprise support and strong instruction-following, Claude Opus 4 provides a strong cross-provider alternative for tasks where reproducibility and provider diversity matter

• Gemini 2.5 Pro (Google): For long-context multimodal tasks where throughput and latency matter alongside reasoning quality, Gemini 2.5 Pro handles image and text inputs at speeds that exceed Kimi K2.6 and with strong benchmark performance on complex reasoning evaluations

• GPT-4o (OpenAI): For structured tool-use, function calling, or multimodal workflows where ecosystem integration and broad provider availability are priorities, GPT-4o is a well-documented and widely deployed alternative that covers K2.6's core use cases

• DeepSeek R1 (DeepSeek): For open-weight reasoning workloads where self-hosting is a goal or where you want provider redundancy at a lower cost point, DeepSeek R1 competes directly on reasoning benchmarks and can serve as a fallback when Kimi K2.6 is unavailable

What are the challenges of using Kimi K2.6 in my product?

Like any production LLM, Kimi K2.6 comes with tradeoffs worth planning for:

• Verbose output behavior: During benchmark evaluations, Kimi K2.6 generated 170M output tokens compared to a median of 43M across comparable models. Applications that do not constrain output length may see higher-than-expected per-request costs and longer response times

• Speed constraints: At approximately 44.5 tokens per second, Kimi K2.6 ranks below the median for its model class. Latency-sensitive use cases such as real-time user-facing chat may find the throughput insufficient without additional engineering around streaming and timeout handling

• Provider dependency: Relying on Moonshot AI as a single provider creates fragility when the provider has an outage or deprecates a model version. Because Kimi K2.6's trillion-parameter MoE architecture has no direct drop-in equivalent elsewhere, failover planning requires selecting from less-similar alternatives

• Cost at scale: At $4.00 per 1M output tokens, and given the model's verbosity, output costs compound quickly at high request volumes. Active cost governance and prompt-level output constraints are important for keeping billing predictable

• Rapid model versioning: Moonshot AI has released multiple K2-family iterations in quick succession. Production deployments may need to re-evaluate model fit and migrate more frequently than with providers on slower release cycles

Why should I use Merge Gateway to route LLM requests with Kimi K2.6 and every other model?

Using Kimi K2.6 through Merge Gateway gives you access to the model itself and the infrastructure layer around it:

• Cost governance: Set hard or soft project budgets so Kimi K2.6 spend stays within plan. Given K2.6's verbosity and $4.00 per million output token price, budget controls are especially valuable. Every request is attributed to a model, project, and tag in a unified billing dashboard across all providers

• One API, every provider: Access Kimi K2.6 and every other major LLM through a single endpoint and API key. Change providers by swapping the model string, with no application code changes required

• Intelligent routing and automatic failover: Merge routes around Moonshot AI outages automatically. Routing policies based on cost, latency, or quality can reduce spend by 40 to 60% without touching your application code

• Build Your Own Router: Define what "best" means for your traffic by selecting from curated ML benchmarks or adding your own eval scores. The router scores each available model against your weights and picks the winner per request, with a plain-language explanation of every decision

• Security and compliance controls: Apply DLP rules and prompt injection protection before every request reaches Moonshot AI. Enforce per-project model and region policies without adding that logic to your application

How can I start routing requests to Kimi K2.6 via Merge Gateway?

Getting Kimi K2.6 running through Merge Gateway takes a few minutes:

1. Create an account and get your API key from the dashboard.

2. Install the Merge Gateway SDK: run pip install merge-gateway-sdk (Python) or npm install merge-gateway-sdk (Node). Alternatively, if you're already using the OpenAI SDK, set base_url = "https://api-gateway.merge.dev/v1/openai" and your existing code works as-is.

3. Make your first request using the provider/model format. For Kimi K2.6, the model string is moonshot/kimi-k2.6. Swap the model string to route to any other provider without changing anything else.

4. Configure a routing policy in the dashboard to set failover behavior, cost limits, and optimization strategy. Your first policy can be as simple as naming Kimi K2.6 as primary with one fallback for outage coverage.

Full setup instructions and SDK references are in the Merge Gateway docs.