Kimi K3 API: Online text generation, tutorials and integration guide

What is Kimi K3

A native multimodal model for long-horizon coding and knowledge work

We position Kimi K3 as a reasoning and Agent model for complex tasks, with a focus on work that requires sustained understanding of context, tool calls, and revisions based on results. Developed by Moonshot AI, it is a multimodal model with native vision that can understand text, images, and video. Its applications include project coding, research, and multi-step knowledge work.

Its multimodal capabilities broaden its applications, including image understanding/OCR. Compared with traditional OCR solutions, its advantage is that you can obtain good comprehension without training a separate model.

An open-weight MoE model with a million-token context

Kimi K3 uses a mixture-of-experts architecture, or MoE, with 2.8 trillion total parameters and approximately 104 billion active parameters per token. Its context window is 1,048,576 tokens. The complete model weights have been released under the Kimi K3 License.

We believe the practical value of a million-token context is the ability to include more related material in one analysis, such as project specifications, code from multiple modules, and previous discussions. For teams that need their own deployment, open weights also make self-hosting possible for some businesses and users.

Always-on thinking with three reasoning effort levels

Kimi K3 always thinks. The current official Kimi API offers three reasoning effort levels through reasoning_effort: low, high, and max, with max as the default.

We recommend assigning a reasoning budget according to task difficulty: start with low for simple classification and information extraction, high for multi-step analysis, and compare max for complex tasks that require repeated constraint checks. In our experience testing other models, max does not necessarily offer the best overall return, so test and judge it against your use case.

The Kimi K3 online tool and API on RequestBeam

With RequestBeam, we want to help you understand Kimi K3's performance on your own tasks more quickly. Sign in, create an API Key, and add a small amount of credit to start using the online tool. You do not need to write API integration code first or buy a fixed subscription for a short evaluation.

MiniMax M3 core features and domain strengths

Project coding: fixing and extending existing code

We would prioritize MiniMax M3 for project iterations with clear goals, such as fixing existing issues, adding features according to interface contracts, or adjusting implementations based on test results. Thinkwright's public engineering experiments show that M3 and GLM-5.2 perform similarly on repairs and feature additions to existing code, with a more noticeable gap in building projects from scratch. Much of building from scratch involves establishing architecture and conventions. Therefore, if your business is just getting started, M3 may not necessarily suit you.

Long-document analysis: reading codebases, documents, and runtime logs together

MiniMax M3 uses MSA sparse attention to handle long contexts. Its technical paper describes a method that selects relevant information blocks before computing attention, reducing the computational overhead of very long inputs.

We focus more on its value in cross-material analysis, such as checking whether code meets requirements, comparing interface contracts across versions, or reconstructing incidents from logs. We recommend retaining file names, versions, and timestamps, and requiring conclusions to point to specific locations. For very long tasks, you can first ask the model to list the key evidence it found before making a judgment, so you can assess its performance in your business as you use it.

Image and video understanding: incorporating interfaces and visual material into analysis

We would use MiniMax M3's visual capabilities for work that requires understanding images, such as checking whether a webpage implementation matches a screenshot, analyzing chart content, or documenting a process from a recording. Like the official MiniMax API, RequestBeam provides image and video inputs for M3, using image_url content blocks for images and video_url content blocks for videos.

We recommend making the observation goal explicit: identify the areas to inspect in an interface, specify the metrics to verify in a chart, and limit the time range and events to identify in a video. This makes it easier to align model output with the original material and continue modifying code or adding explanations.

Tool collaboration: continuing multi-step tasks from intermediate results

MiniMax M3 supports tool calling and can continue reasoning between rounds of tool interaction. Once an application provides retrieval, code execution, or file-processing tools, the model can decide its next action based on the returned results.

Customer feedback on M3's Agent experience is mixed: some users appreciate its execution capabilities, while others report confusion in complex workflows. We therefore recommend starting with small examples if you plan to use M3 in your business. Begin with clearly bounded workflows, specifying each step's input, expected result, and failure handling, before gradually extending the task.

Long-running optimization: continuous improvement against verifiable goals

MiniMax has demonstrated an FP8 matrix multiplication kernel optimization case in which M3 made 147 benchmark submissions and 1,959 tool calls over approximately 24 hours, continually modifying the implementation based on feedback.

We believe this capability is worth using for work with clear feedback and defined boundaries, such as code performance optimization, test repairs, and experiment reproduction. We recommend setting correctness checks, performance goals, and stopping conditions in advance. When results stop improving for an extended period, review the task description, tool feedback, or implementation direction rather than merely increasing the number of calls.

Kimi K3 benchmarks and performance

In this section, we separately use Moonshot's official launch-period tests and independent Artificial Analysis evaluations available as of October 7, 2026. All official Kimi K3 results below use max thinking effort. We also retain benchmark versions and key test conditions to help you judge their relevance to real tasks.

Software engineering: 67.5% on DeepSWE and 77.8% on ProgramBench

Moonshot reports Kimi K3 scores of 67.5% on DeepSWE v1.1 and 77.8% on ProgramBench. Both K3 tests use the Kimi Code harness. We use them as references for the model's ability to work on code repositories and sustained coding tasks.

For teams integrating a coding assistant, we recommend separately testing existing issue repairs, small feature development, and small project implementation. Focus on first-pass completion, recovery after failure, and the amount of manual rework the final code needs. These results are easier to connect to development costs.

Terminal tasks: Terminal-Bench 2.1 and 4.0 must be interpreted separately

In the official launch-period tests, Kimi K3 with Kimi Code scored 88.3% on Terminal-Bench 2.1. In the current Artificial Analysis Terminal-Bench 4.0 test, K3 Max scores approximately 13%.

The two results use different task sets and execution harnesses. AA's 4.0 test contains 66 more difficult terminal tasks, uses mini-swe-agent, and repeats each task three times. For real business use cases, we recommend testing with other harnesses.

Research and tool calling: 91.2% on BrowseComp and 94.5% on MCPMark-Verified

The official report gives Kimi K3 a BrowseComp score of 91.2% and an MCPMark-Verified score of 94.5%. The 91.2% BrowseComp result uses a strategy that triggers context compaction at 300K tokens; directly using a 1M context without context management produces 90.4%.

We believe these results show that research and tool collaboration are Kimi K3 strengths, while also reminding us to examine how applications manage context. In our experience, context management can have a major effect on practical results.

Long-document reasoning: 88.7% on AA-LCR v1.1

Artificial Analysis's dedicated AA-LCR v1.1 page records Kimi K3 Max at 88.7%. This evaluation contains 100 questions that require synthesizing information from long documents, including company reports, papers, and industry material. Document lengths range from approximately 10,000 to 100,000 tokens.

We regard this result as relatively persuasive evidence of Kimi K3's cross-material analysis capability. We still recommend separate testing for real inputs approaching a million tokens. In our past testing, output has not been particularly good when input approaches a model's allowed limit.

Intelligence index and response speed: 44 points and approximately 45 tokens per second

Artificial Analysis Intelligence Index v4.3.2 lists Kimi K3 Max at 44 points. Its output speed is approximately 45 tokens per second, with around 3.92 seconds to the first token and 48.58 seconds to the first answer token when thinking is included.

We measure the time until data starts returning, the time until the actual answer appears, and the time until the whole task finishes separately. For frequently interactive features, these times affect the user experience; for complex analysis, they must be weighed against result quality in the business use case.

Kimi K3 compared with DeepSeek V4 Pro, V4.1 Flash, Claude Fable 5.1, and GPT-5.6 Sol

We compare data from the same Artificial Analysis v4.3.2 version. All entries below use Max effort; Claude Fable 5.1 retains the Default Fallback configuration listed on the evaluation page. The comparison pages display AA-LCR as integer percentages, so Kimi K3's 88.7% appears as 89%.

Task costs in the table are weighted average costs calculated by AA for its evaluations, including input, caching, thinking, and answer usage, and serve as references for model selection. Actual RequestBeam charges are calculated from platform prices and request usage. Cost methodology

Sources: V4 Pro comparison, V4.1 Flash comparison, Fable 5.1 comparison, and GPT-5.6 Sol comparison.

Compared with DeepSeek V4 Pro: stronger long-document analysis at higher evaluation task cost

In these evaluations, Kimi K3 scores above DeepSeek V4 Pro 0813 in the intelligence index, long-document reasoning, and knowledge work. We would prioritize K3 for tasks with complex connections across materials and multiple types of evidence, then check whether the capability difference reduces manual organization and review time. AA like-for-like comparison

We would also test V4 Pro for large volumes of repetitive text tasks with clear acceptance criteria. Its task cost is lower in this evaluation suite. If its output already meets business requirements, that cost difference may become important when scaling usage.

Compared with DeepSeek V4.1 Flash: an overall and long-document lead, with different automation trade-offs

Kimi K3 leads in the intelligence index and AA-LCR, but V4.1 Flash scores 69% on AutomationBench-AA compared with K3's 58%; on Terminal-Bench 4.0, they score 27% and 13%, respectively. We believe this shows how task types can change the model ranking. AA like-for-like comparison

For applications primarily analyzing long materials, we recommend focusing on K3. For workflow automation, terminal operations, and high-frequency calls, we would treat V4.1 Flash as an important candidate and compare both success rates and complete execution costs.

Compared with Claude Fable 5.1: a gap in terminal coding, with long-document tasks worth testing separately

Under the Default Fallback configuration above, Claude Fable 5.1 scores 53 on the intelligence index compared with Kimi K3's 44, and 52% on Terminal-Bench 4.0 compared with 13%. We would use Fable 5.1 as an important reference for complex terminal coding. K3 achieves a higher AA-LCR v1.1 score and a lower evaluation task cost. AA like-for-like comparison

For applications that answer specific questions after reading multiple sources, we recommend testing K3 separately. For tasks requiring sustained terminal operation, environment repair, and engineering verification, we would focus more on first-pass success and the number of manual interventions before deciding whether the additional model cost is worthwhile.

Compared with GPT-5.6 Sol: different long-document reasoning results at similar complete task costs

Kimi K3 scores higher on AA-LCR than GPT-5.6 Sol, while GPT-5.6 Sol scores higher on the intelligence index and Terminal-Bench 4.0. We pay particular attention to their task costs: approximately $2.00 and $1.99, respectively, in this evaluation suite, which are already very close. AA like-for-like comparison

We therefore recommend comparing costs after completing the whole task, including thinking, tool interactions, retries, and the final answer. Prioritize comparing K3 for information-intensive analysis; use GPT-5.6 Sol as a key reference for terminal engineering tasks. Ultimately, choose routing based on actual task completion in your business.

Kimi K3 API frequently asked questions

What endpoint and model name should I use to call Kimi K3 through RequestBeam?

Our current Kimi K3 endpoint is POST https://api.requestbeam.com/v1/chat-completions, the model identifier is kimi-k3, and authentication uses a RequestBeam API Key. First confirm connectivity with the minimal request below, then add optional parameters one by one. RequestBeam Kimi API documentation

curl -X POST "https://api.requestbeam.com/v1/chat-completions" \
  -H "Authorization: Bearer $REQUESTBEAM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k3",
    "messages": [
      {"role": "user", "content": "Organize this requirement into goals, constraints, and acceptance criteria: create an admin dashboard for internal employees with email login and password reset."}
    ],
    "stream": false
  }'

Does Kimi K3 support streaming and follow-up questions?

We provide both streaming and non-streaming responses. Setting stream: true returns SSE; setting stream: false or omitting the field returns complete JSON. We recommend streaming for interfaces that display results incrementally. RequestBeam Kimi API documentation

For follow-up questions, the application must maintain conversation history according to the integration protocol and include relevant context in subsequent requests. Memory in the online tool adds reusable turns from the current conversation. We recommend retaining material relevant to the question and watching the input token costs as history grows. Online tool context explanation

Why can answers still be truncated if Kimi K3 supports a million-token context?

We first check context capacity and output budget separately. RequestBeam's max_tokens configures the output allowance, which thinking and the final answer share. If the allowance is too small, the response may end with finish_reason: "length" before the answer is complete. RequestBeam output budget explanation

When you encounter length, we recommend checking whether you have left enough room for thinking and the answer before increasing the budget or splitting the task into steps. If the error indicates that the context limit was exceeded, remove irrelevant history and duplicate material.

Finally, we need to clarify one point: a million-token context does not mean it can actually return good results at that scale. If correctness is your priority, controlling context length is, so far, your only option.

How should I handle 400, 401, 402, and 429 errors when calling Kimi K3?

We recommend checking the HTTP status first, then the numeric code in the response. RequestBeam's Chat Completions documentation defines common errors as follows. RequestBeam error response documentation

We recommend starting troubleshooting with the minimal request, then restoring parameters one at a time. Handle the HTTP status and the response code separately. If the issue persists, retain the request identifier from the response and relevant parameters to help us locate the specific failure.