Kimi K2.6 API for Coding, Vision, and Agent Workflows
Use the Kimi K2.6 API on GPTProto for long-context coding, image and video understanding, tool calls, and multi-step agent tasks. GPTProto lists access at 10% below the displayed market reference, with one API key and one balance shared across 200+ models. This gives developers an affordable Kimi K2.6 API route without maintaining a separate provider account or a dedicated Moonshot credit balance.
What Is Kimi K2.6?
Kimi K2.6 is Moonshot AI's open-source general model for coding, reasoning, multimodal understanding, and agent workflows. It accepts text, image, and video input, supports thinking and non-thinking modes, and can participate in multi-step tool loops. Its 262,144-token context window fits large repositories, long documents, extended conversations, and tasks that must retain substantial history.
Moonshot positions K2.6 as a long-horizon coding and agent model rather than a text-only chatbot. Its release covers Rust, Go, Python, front-end development, DevOps, and performance optimization. The company also demonstrates Agent Swarm deployments with up to 300 sub-agents and 4,000 coordinated steps. Those figures describe Moonshot's orchestration product, not an automatic behavior of every API request.
Kimi K2.6 API Specifications
| Specification | Verified value |
| Provider | Moonshot AI |
| Model string | kimi-k2.6 |
| Context window | 262,144 tokens |
| Input modalities | Text, images, and video |
| Output modality | Text |
| Reasoning modes | Thinking enabled or disabled |
| Default `max_tokens` | 32,768 |
| Tool support | Function calling, multi-step tool use, JSON Mode, and Partial Mode |
| API format | OpenAI-compatible chat completions format |
| Open-source status | Moonshot AI has released Kimi K2.6 as an open-source model |
Long-Horizon Coding and Agent Tasks
Kimi K2.6 is most relevant when a task extends beyond a short code completion. The 256K window can hold more files, requirements, logs, and tool results in one working context, reducing repeated summarization. Typical Kimi K2.6 model API workloads include repository analysis, cross-file refactoring, test generation, incident investigation, document review, and agents that alternate between reasoning and tool execution.
Moonshot reports 58.6 on SWE-Bench Pro and 66.7 on Terminal-Bench 2.0 under its published settings. Use those results to shortlist the model, then test it with your own repository conventions, tools, response schema, latency target, and recovery cases.
Image and Video Understanding
The model accepts text, image, and video in one conversation. It can interpret interface screenshots, diagrams, charts, recordings, and clips before explaining or acting on them, which is useful for browser agents, visual QA, support, and coding tasks where important state exists outside source text.
Moonshot recommends images no larger than 4096 × 2160 and video no larger than 1920 × 1080. Higher resolutions add processing time without improving understanding. Remote image URLs are not supported in the documented vision flow; use base64 or file upload and keep the complete request body under 100 MB.
Supported Media and Request Limits
| Item | Documented behavior |
| Image formats | PNG, JPEG, WebP, and GIF |
| Video formats | MP4, MPEG, MOV, AVI, X-FLV, MPG, WebM, WMV, and 3GPP |
| Recommended image resolution | Up to 4096 × 2160 |
| Recommended video resolution | Up to 1920 × 1080 |
| Request body limit | 100 MB |
| Remote image URL | Not supported in the documented vision flow |
| Reused or large media | File upload is recommended |
| Media billing | Image and video content is converted into dynamically calculated tokens |
Thinking Mode and Tool-Calling Constraints
Kimi K2.6 offers deeper reasoning and lower-overhead non-thinking responses, but its sampling parameters are not fully free-form. The documented temperature is `1.0` with thinking and `0.6` without it; `top_p` is fixed at `0.95`, while `n` and penalty values are also fixed. Unsupported values can return an error.
With thinking enabled, `tool_choice` supports `auto` or `none`. Retain the assistant message's `reasoning_content` during multi-step calls. Moonshot also warns that its built-in web-search tool is being updated and is not currently recommended; the documented search tool is not compatible with K2.6 thinking mode at the time of writing.
| Integration detail | Kimi K2.6 behavior |
| Enable thinking | `{"type":"enabled"}` |
| Disable thinking | `{"type":"disabled"}` |
| Temperature | `1.0` with thinking; `0.6` without thinking |
| `top_p` | Fixed at `0.95` in the API guide |
| Tool choice with thinking | `auto` or `none` |
| `reasoning_content` | in assistant history |
| Built-in web search | Currently under revision; check the latest Moonshot documentation before use |
Kimi K2.6 vs Kimi K3
The `kimi k2.6 vs kimi k3.0` query generally refers to Kimi K3, Moonshot AI's newer flagship. K3 leads in Moonshot's published table, while K2.6 remains relevant for open-source access, lower API cost, or compatibility with an existing integration. Choose based on the quality gain your workload receives relative to latency and token cost.
| Official Moonshot benchmark | Kimi K2.6 | Kimi K3 |
| Humanity's Last Exam | 54.0 | 58.7 |
| GPQA Diamond | 88.4 | 91.2 |
| AIME 2026 | 91.2 | 96.7 |
| SWE-Bench Pro | 58.6 | 63.4 |
| Terminal-Bench 2.0 | 66.7 | 71.8 |
These are official Moonshot results with published test settings. For a migration decision, run the same prompts, tools, and pass/fail criteria against both model IDs.
Kimi K2.6 vs Claude Opus 4.7 and GPT-5.5
The `kimi k2.6 vs opus 4.7` and `kimi k2.6 vs gpt 5.5` queries call for a workload decision, not a universal winner. GPTProto lists all three model families, allowing evaluation behind the same application interface.
| Model | Verified access fact | What to compare in your evaluation |
| Kimi K2.6 |
Available on GPTProto; 256K context; text, image, and video input; open-source release |
Long-horizon coding, multimodal input, tool-loop recovery, and total token cost |
| Kimi K3 | Available on GPTProto; newer Moonshot flagship | Quality gain over K2.6, latency, and cost per successful task |
| Claude Opus 4.7 | Available on GPTProto | Instruction fidelity, tool-call reliability, long-form output, and cost |
| GPT-5.5 | Available on GPTProto | Coding accuracy, reasoning quality, structured output, latency, and cost |
Use one GPTProto key and balance for the comparison. Keep prompts, tools, files, reasoning settings, and the success rubric constant so the result reflects model behavior rather than different test conditions.
Managed API Access vs Self-Hosting
Moonshot AI's open-source release makes self-hosting possible, but it solves a different problem from managed access. Self-hosting gives teams control over the serving stack; its cost depends on quantization, concurrency, context length, hardware, observability, and the inference engine. A hosted endpoint avoids that infrastructure work and charges for usage.
| Decision area | GPTProto API access | Self-hosted open-source deployment |
| Initial setup | Use the existing GPTProto account and key flow | Provision hardware and an inference stack |
| Ongoing work |
Monitor requests, tokens, and application behavior |
Maintain drivers, runtimes, scaling, logs, and model updates |
| Model switching | Use the same balance across 200+ models | Deploy and operate each model separately |
| Cost model | Pay per token | Hardware, hosting, engineering, and utilization costs |
| Best fit | Fast evaluation, variable traffic, and multi-model applications | Teams that require infrastructure control and can operate the serving layer |
Kimi K2.6 API Access on GPTProto
GPTProto provides access through the model string `kimi-k2.6`. The page currently lists `$0.855` per 1M input tokens and `$3.60` per 1M output tokens, both 10% below the displayed market reference. The same key and balance can also be used for Kimi K3, GPT-5.5, Claude Opus 4.7, and other supported models, removing separate provider wallets from routing and A/B tests.
