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Kimi K3 API: Pricing and Text Integration

Review Kimi K3 token pricing and Chat Completions examples for coding and document analysis. Check supported inputs, reasoning behavior and integration limits.

Kimi K3 API pricing and billing

Official price

Input
$3.0000 /M
Output
$15.0000 /M
Cache input
$0.3000 /M

API Hop price Save 30%

Input
$2.1000 /M
Output
$10.5000 /M
Cache input
$0.2100 /M

Reference price - not independently verified. Check the publisher's current rates, billing units and applicable terms before comparing costs.

Kimi K3 online chat tool

Model settings

Advanced parameters
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No fixed character limit; the full request is subject to request-size and model-context limits. This is not a token count.
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-2 – 2
-2 – 2

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Recorded API Hop samples

Recorded on October 22, 2026 through API Hop. Each result is one request for the named model and mode, not a success-rate, speed or production-quality guarantee. Model replies below remain in their original language.

Release decision with paragraph citations

kimi-k3 · document-citations ·

Verbatim excerpt

**Answer summary:** Production release is **not approved**. Staging established only export-formatting/export v7 verification — nothing about production. **Mei** owns the remaining duplicate-delivery tests; documentation ownership is **unassigned**.
Full model reply (unmodified)
**Answer summary:** Production release is **not approved**. Staging established only export-formatting/export v7 verification — nothing about production. **Mei** owns the remaining duplicate-delivery tests; documentation ownership is **unassigned**.

| Conclusion | Exact supporting quote | Paragraph ID | Uncertainty |
|---|---|---|---|
| Production release is not approved; approval was withheld pending tests | "the release reviewer withheld production approval until duplicate-delivery tests pass" | P31 | Low — explicit statement |
| No approval or completed test recorded later (cross-check of P04's Friday shipping proposal, which "was not an approval") | "No completed test result or production approval is recorded in this document." | P36 | Low — explicit; P04 proposal superseded by P31/P36 |
| Staging established export formatting checks passed (repeated across components), and verified export v7 on synthetic data only | "staging verified export version 7 with three synthetic records. Production deployment and billing checks were not performed." | P18 | Low for staging result; production readiness not established |
| Staging did not measure production latency, charges, or customer outcomes | "no production latency, charge or customer outcome was measured" | P01 (and P02–P03, P05–P17, P19–P30, P32–P35, identical wording) | Low — explicit; applies to all component entries |
| Remaining duplicate-delivery tests are owned by Mei | "Mei owns those tests." | P31 | Low — explicit; tests scheduled, not completed ("Mei scheduled duplicate-delivery tests for October 16," P36) |
| Documentation has no owner; a documentation check was only planned | "Documentation ownership remains unassigned." | P31 (earlier: "Documentation ownership was still unassigned," P04) | Low that it is unassigned as of this document; future owner unknown. Note: component entries say "The named reviewer will check documentation next week" (e.g., P01), but no name is given and no completion is recorded |

**Unresolved questions:**
1. Did the duplicate-delivery tests scheduled for October 16 (P36) pass, and has the release reviewer since granted production approval?
2. Who will be assigned documentation ownership, and did the unnamed "named reviewer" ever complete the documentation check promised for "next week" (P01 et al.)?

The document is synthetic, not a maximum-context test. The conclusions agree with the input, but two auxiliary quotes replace sentence-ending periods with commas: the P36 test-scheduling quote and the P04 documentation-ownership quote. Those punctuation marks are not verbatim; manual citation checks are still required.

Complete request and input

POST /v1/chat-completions

{
  "model": "kimi-k3",
  "messages": [
    {
      "role": "user",
      "content": "Use only the following synthetic document. Answer: Is production release approved? What did staging establish? Who owns the remaining tests and documentation? Return a compact table of conclusion, exact supporting quote, paragraph ID and uncertainty, followed by two unresolved questions. Cross-check proposals against later updates; never infer a completed test or production success. Keep the answer under 500 words. This sample tests citation accuracy on this document, not maximum context length.\n\n[P01] Component 1 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P02] Component 2 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P03] Component 3 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P04] On October 10, the release coordinator proposed shipping export version 7 on Friday. The proposal was not an approval. Documentation ownership was still unassigned.\n\n[P05] Component 5 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P06] Component 6 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P07] Component 7 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P08] Component 8 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P09] Component 9 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P10] Component 10 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P11] Component 11 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P12] Component 12 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P13] Component 13 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P14] Component 14 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P15] Component 15 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P16] Component 16 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P17] Component 17 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P18] On October 11, staging verified export version 7 with three synthetic records. Production deployment and billing checks were not performed.\n\n[P19] Component 19 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P20] Component 20 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P21] Component 21 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P22] Component 22 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P23] Component 23 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P24] Component 24 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P25] Component 25 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P26] Component 26 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P27] Component 27 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P28] Component 28 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P29] Component 29 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P30] Component 30 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P31] On October 12, the release reviewer withheld production approval until duplicate-delivery tests pass. Mei owns those tests. Documentation ownership remains unassigned.\n\n[P32] Component 32 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P33] Component 33 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P34] Component 34 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P35] Component 35 review: staging checks passed for export formatting. Production rollout has not been approved by this entry. The named reviewer will check documentation next week; no production latency, charge or customer outcome was measured. This paragraph records a proposed review, not completion.\n\n[P36] On October 13, Mei scheduled duplicate-delivery tests for October 16. No completed test result or production approval is recorded in this document."
    }
  ],
  "stream": false,
  "max_tokens": 4096
}
Response metadata and reported usage
{
  "requestId": "339d0045e51385a03300d75fa74f0863",
  "jobId": null,
  "responseModel": "kimi-k3",
  "responseAt": "2026-10-22T12:28:31.361Z",
  "finishedAt": "2026-10-22T12:28:31.364Z",
  "usage": {
    "prompt_tokens": 2058,
    "completion_tokens": 1086,
    "total_tokens": 3144,
    "prompt_tokens_details": {
      "cached_tokens": 0,
      "cache_write_tokens": 0,
      "text_tokens": 0,
      "audio_tokens": 0,
      "image_tokens": 0
    },
    "completion_tokens_details": {
      "text_tokens": 0,
      "image_tokens": 0,
      "audio_tokens": 0,
      "reasoning_tokens": 0
    },
    "input_tokens": 0,
    "output_tokens": 0,
    "input_tokens_details": null,
    "output_tokens_details": null,
    "claude_cache_creation_5_m_tokens": 0,
    "claude_cache_creation_1_h_tokens": 0
  },
  "outputSha256": "e3ba3715b7facfd224f3e1982c457c3647c33114774768190023cef8b1eef396",
  "responseEvidence": {
    "sha256": "2f1507aa711a70a17163506c7420517b599e4564fcf69aaef6db8cf5d7b09506",
    "bytes": 6024,
    "phase": "submission",
    "http": 200,
    "requestId": "339d0045e51385a03300d75fa74f0863",
    "observedAt": "2026-10-22T12:28:31.361Z"
  },
  "assets": []
}
Download public evidence record

Kimi K3 prompt examples

These are evaluation prompts for Kimi K3, not recorded model outputs. They analyze only supplied text; they do not execute code, inspect production systems or prove long-context performance. Check quoted evidence and acceptance criteria, then review response metadata and usage before production use.

Use only this document sample: [P1] The export fix passed staging checks. [P2] A Friday release was proposed, not approved. [P3] Mei will review error messages on Tuesday. [P4] No owner is assigned to documentation. Return a table of claim, exact supporting quote, paragraph ID and uncertainty. Is production release approved? Who owns documentation? Mark missing facts unknown. Every quote must match the supplied sample; do not infer production success from staging. This small sample does not test maximum context length.

Extract these synthetic records into JSON with fields id, owner, deadline and evidence: [R1] Issue 41: Ana owns the export fix; no deadline agreed. [R2] Issue 42: review due October 15; owner not assigned; year not stated. Use null for missing values and preserve partial dates as given. Quote the source in evidence. Do not invent a year or owner. Check each field against its record; this is a text-formatting task, not a database operation.

A fictional two-person team runs 2,000 reports daily. Completion within 30 minutes is acceptable. It has a database but no queue service; jobs must recover after restart and avoid duplicate delivery. Compare a database-backed batch with a dedicated queue. Give a decision table, a conditional recommendation, three unknowns and one condition that reverses your choice. Do not invent benchmarks, costs or tested guarantees.

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.

Kimi K3 core features and domain strengths

Long-horizon software development: from understanding requirements to fixing issues and running tests

We believe long-horizon coding is the Kimi K3 capability most worth testing first. When an Agent framework provides file, terminal, and testing tools, it can work through code reading, implementation changes, test execution, and issue fixes, rather than simply generating a piece of code from a one-line requirement.

In the customer experiences we have collected, one user reported that Kimi K3 helped move a previously stalled project forward through several consecutive development stages. Other developers said it performed well at cross-file analysis and identifying relationships between modules, but its first proposed changes were not always safe and could sometimes introduce new problems.

We think this illustrates how to use Kimi K3 in real development: rather than simply asking the model to "fix this bug for us," provide explicit test cases, existing behaviors that must remain unchanged, and the scope of files it may modify.

For backend refactoring, complex data processing, and production code, we recommend having it explain its change plan first, then make the changes, and finally verify the results with automated tests. Do not skip code review even when the model reports that the task is complete.

Visual development: using interface screenshots to continually refine frontend and interaction design

Kimi K3's visual understanding lets it work with interface screenshots, design requirements, and code within the same task.

For example, we can first ask the model to generate a webpage from a reference image, then use browser tools to capture the actual rendered page. The model can compare it with the design and continue adjusting the layout, colors, typography, and interaction details.

In feedback from our customer group, some users have praised Kimi K3's work on frontends, visualization projects, and small games.

We believe Kimi K3 is well suited to product prototypes, campaign pages, visual dashboards, and development tasks that require repeated comparison with screenshots. Independent developers in particular can use it to generate a working version first, then gradually refine the page experience.

Agent tool collaboration: multi-step tasks need clear boundaries

Kimi K3 can work with external tools to read and write files, operate terminals, retrieve information, and perform multi-step automation. The model decides what to do next, while the application or Agent framework provides the actual tool calls and execution environment.

We pay particular attention to one observation: the same model can have noticeably different task completion rates in different Agent frameworks.

Moonshot AI also cautions that Kimi K3 may proactively make decisions beyond expectations when user intent is unclear. For tasks that require strict control over behavior, we therefore recommend defining operating permissions in system prompts or AGENTS.md.

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

Model and test configurationIntelligence indexAA-LCR v1.1Weighted cost per task
Kimi K3 Max4489%$2.00
DeepSeek V4 Pro 0813 Max3680%$0.67
DeepSeek V4.1 Flash Max3984%$0.27
Claude Fable 5.1 Max, Default Fallback5385%$7.63
GPT-5.6 Sol Max4784%$1.99
Artificial Analysis v4.3.2 2026-10-07 · All models at Max
Intelligence Index Score · Higher is better
  • Kimi K3 Max 44
  • DeepSeek V4 Pro 0813 Max 36
  • DeepSeek V4.1 Flash Max 39
  • Claude Fable 5.1 MaxDefault Fallback 53
  • GPT-5.6 Sol Max 47
AA-LCR v1.1 Percent · Higher is better
  • Kimi K3 Max 89%
  • DeepSeek V4 Pro 0813 Max 80%
  • DeepSeek V4.1 Flash Max 84%
  • Claude Fable 5.1 MaxDefault Fallback 85%
  • GPT-5.6 Sol Max 84%
Weighted cost per task USD/task · Lower is better
  • Kimi K3 Max $2.00
  • DeepSeek V4 Pro 0813 Max $0.67
  • DeepSeek V4.1 Flash Max $0.27
  • Claude Fable 5.1 MaxDefault Fallback $7.63
  • GPT-5.6 Sol Max $1.99
AA-LCR is rounded: Kimi K3 88.7% → 89%. AA task costs include input, cache, reasoning and answers; these are not API Hop prices.

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.

Why use Kimi K3 through API Hop?

Usage-based billing and model cost optimization

Charges follow current API Hop rates and actual usage. Top up on demand.

Unified AI models and data APIs, with multi-model access in one place

One API key connects multiple AI models and data services, reducing integration costs and letting you switch with one click. Business applications need both AI and data.

Call logs and application access controls, with clarity on every call

Every API call is recorded in your dashboard. Create separate API keys for different applications.

Evaluate outputs against your task

Use the documented model ID and supported parameters through API Hop. Evaluate outputs against your own acceptance criteria, then inspect usage records and response metadata before production use. A model name or example does not guarantee each result.

How to use Kimi K3 on API Hop

After creating an API key in the console, follow the documentation to get started with Kimi K3.

Prepare your account and test online

Sign in, confirm your balance, and create an API key.

Try it in the Playground

Explore the model's capabilities in the Playground to find the best way to use it.

Integrate Kimi K3 into your business

Follow our developer documentation to integrate Kimi K3 into your business.

Future model upgrades

To switch to a newer model, first confirm that its exact ID is returned by the model catalog for your API key. If it supports the same endpoint and request fields, update the model ID, then test response formats, streaming, output limits and billing before sending production traffic. For asynchronous image or video tasks, also verify job submission, status polling and result retrieval.

Kimi K3 API FAQ

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
  }'

This API Hop model uses the unified Chat Completions interface, not OpenAI Responses or Claude Messages.

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

The Playground memory is not persistent across sessions. Disabling it leaves history visible but excludes it from new requests. Failed, stopped, truncated, or non-reusable replies are not appended as normal history.

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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.

API clients must distinguish reasoning_content from the final content.

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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

HTTP statusNumeric codeMeaningOur recommended action
40010000Invalid requestCheck model, messages, field types, and optional parameters
40120000Missing or invalid authenticationCheck the RequestBeam API Key and Bearer authentication header
40240000Insufficient available balanceCheck the account balance and the allowance this request needs
42940001Request rate limitedReduce concurrency and frequency, and retry with backoff

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.

For 403, check model permissions in the catalog visible to your key. Diagnose 502, 503, 504 and in-stream SSE errors from the complete error response.

Can the Kimi K3 Playground accept images, PDFs, or video?

This page provides text conversations without file or image upload controls. Native visual understanding requires an interface supporting those inputs. This tool does not download linked files, read external documents, or automatically search the web. Extract relevant text first when testing here.

Can one request return multiple alternatives or guarantee valid JSON?

Kimi K3 supports only n=1 through API Hop. Ask for multiple alternatives within one answer. For JSON, define fields, types, and missing-value behavior, then validate syntax, schema, and business meaning in your application.

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