Kimi + OpenClaw: Ultra-Long-Context Workflows for Research & Contracts

Most AI models hit a wall somewhere between 8,000 and 32,000 tokens. That is fine for a quick email draft or a short Q&A session, but it falls apart the moment you need to process a 150-page contract, synthesise twenty research papers, or audit a regulatory filing against an entire policy manual. Kimi, developed by Moonshot AI, removes that wall with a 200,000+ token context window. Combined with OpenClaw's multi-model orchestration, it opens up workflows that were previously impractical with any single model.

What Is Kimi and Why Does Context Length Matter?

Kimi is a large language model built by Moonshot AI, a Beijing-based company that has made ultra-long-context processing its core differentiator. While most frontier models offer context windows of 8K to 128K tokens, Kimi pushes past 200,000 tokens in production. That is roughly 300 to 400 pages of dense text processed in a single pass, with no chunking, no summarisation losses, and no sliding-window approximations.

Context length is not just a spec-sheet number. It determines what an AI model can actually do in practice. A model with an 8K context window can handle a short memo. A model with 128K can handle a long report. But many real-world business documents exceed those limits:

• Commercial contracts: 50 to 200 pages, often with cross-referenced schedules and exhibits
• Academic research papers: 20 to 60 pages each, and meaningful synthesis requires processing multiple papers simultaneously
• Regulatory filings: Canadian securities filings, OSFI submissions, and environmental assessments routinely run 100+ pages
• Codebases: Even a modest application spans tens of thousands of lines across hundreds of files
• Litigation bundles: Discovery documents, depositions, and exhibit packages measured in hundreds of pages

When a model cannot hold the entire document in context, you are forced to chunk it into pieces and process each piece separately. That introduces a fundamental problem: the model loses the ability to cross-reference information across chunks. It cannot notice that clause 14.3 contradicts clause 7.1, or that a finding in paper twelve undermines the methodology of paper three. Kimi eliminates this problem by holding everything in memory at once.

How OpenClaw Routes Long-Document Tasks to Kimi

OpenClaw's multi-model architecture treats AI models as specialised tools rather than one-size-fits-all solutions. When a task arrives, OpenClaw's routing layer evaluates the input characteristics and selects the optimal model for the job.

For long-context tasks, the routing logic works as follows:

1. Input analysis. OpenClaw measures the token count of the incoming document or document set. If the input exceeds a configurable threshold (typically 32K tokens), it flags the task as long-context.
2. Model selection. The routing engine selects Kimi as the primary processing model. If the task also requires strong reasoning or creative generation, OpenClaw can chain Kimi with a second model (such as ChatGPT or Claude) for downstream processing.
3. Task execution. The full document is sent to Kimi in a single API call. No chunking. No retrieval-augmented generation workarounds. The entire document lives in context.
4. Output handoff. Kimi's output (extracted clauses, synthesised findings, identified gaps) is passed to the next step in the workflow, which might involve a different model for summarisation, formatting, or human-readable report generation.

This routing happens automatically. You do not need to manually select Kimi or manage API calls. OpenClaw handles the orchestration, and you interact with a single unified interface.

Workflow 1: Contract Review and Risk Extraction

Contract review is one of the highest-value applications for long-context AI. A typical commercial contract review involves reading 50 to 200 pages, identifying key clauses, flagging risks, and producing an executive summary. With traditional AI approaches, this requires chunking the contract and losing cross-reference capability. With Kimi and OpenClaw, the entire workflow changes.

How the Workflow Runs

1. Upload the full contract (PDF or Word) to OpenClaw
2. OpenClaw detects the document length and routes it to Kimi
3. Kimi reads the entire contract in a single pass and extracts: key obligations, termination clauses, liability caps, indemnification provisions, change-of-control triggers, and non-compete restrictions
4. Kimi cross-references clauses to flag internal contradictions or unusual terms
5. OpenClaw passes the extracted data to ChatGPT, which generates a structured executive summary with risk ratings (high, medium, low) for each flagged item
6. The final output is delivered as a reviewable report with page references back to the original contract

Workflow 2: Research Synthesis Across Multiple Papers

Academic and market research synthesis is another task where long context is transformative. The traditional approach involves reading papers one at a time, taking notes, and manually identifying connections. With Kimi, you can feed multiple papers into a single context and ask the model to find patterns across the entire corpus.

How the Workflow Runs

1. Upload 10 to 20 research papers (or paste their full text) into OpenClaw
2. OpenClaw concatenates the papers and routes the combined input to Kimi
3. Kimi processes all papers simultaneously and identifies: shared findings, contradictory results, methodological differences, gaps in the literature, and emerging consensus points
4. OpenClaw passes the synthesis to ChatGPT for formatting into a structured literature review with proper citations
5. The output includes a summary table comparing key findings across papers, a narrative synthesis, and a list of suggested follow-up questions

This workflow is particularly valuable for Canadian research teams working on grant applications, policy briefs, or systematic reviews. Instead of spending two weeks on a literature review, the first draft is generated in minutes and refined by the researcher over a day or two.

Workflow 3: Regulatory Compliance Gap Analysis

Regulatory compliance is inherently a long-document problem. You need to compare your company's internal policies against an external regulation, identify gaps, and generate remediation action items. Both documents are typically long, and the value comes from precise cross-referencing.

How the Workflow Runs

1. Upload the full regulation (e.g., PIPEDA guidelines, OSFI E-23, or provincial health privacy legislation) and your company's internal policy documents
2. OpenClaw routes both documents to Kimi as a combined input
3. Kimi performs a clause-by-clause comparison, identifying: requirements in the regulation that your policy does not address, policy provisions that conflict with regulatory requirements, areas where your policy meets the requirement but lacks specificity, and requirements that are adequately covered
4. OpenClaw generates a gap analysis report with prioritised action items, each linked to the specific regulatory clause and corresponding policy section

For organisations subject to multiple regulations (common in financial services and healthcare), this workflow can be run iteratively against each applicable regulation, building a comprehensive compliance map. For more background on AI governance in regulated sectors, see our AI governance guide.

Workflow 4: Codebase Analysis and Documentation

Large codebases present the same long-context challenge as long documents. Understanding architecture, identifying patterns, and generating documentation all benefit from having the entire codebase (or a significant portion of it) in context at once.

How the Workflow Runs

1. Point OpenClaw at a repository or upload key source files (up to 200K tokens of code)
2. Kimi ingests the codebase and builds an understanding of the architecture: module structure, dependency graphs, data flow patterns, and API boundaries
3. You can then query the codebase conversationally: "How does the authentication flow work?", "Which modules depend on the billing service?", "Where are the database migrations handled?"
4. For documentation generation, Kimi produces architectural overviews, module-level documentation, and API reference guides based on the actual code rather than outdated READMEs

This is especially useful for teams onboarding new developers or conducting code audits on acquired codebases. The model's ability to hold the full codebase in context means it can answer questions that require understanding relationships across multiple files, something that chunk-based approaches consistently fail at.

Cost Comparison: Kimi vs GPT-4o for Long Documents

Cost is a practical concern for any production workflow. Long-context processing is token-intensive, and the per-token pricing differences between models add up quickly.

The key insight is that Kimi and GPT-4o are not competitors in a winner-take-all sense. They excel at different things. Kimi is purpose-built for long-context ingestion and extraction. GPT-4o is stronger at general reasoning, creative writing, and short-context tasks. OpenClaw's value is in combining both models so each handles the tasks it does best, which is exactly the approach we outline in our Kimi vs ChatGPT routing guide.

For a team processing 50 long documents per week (a typical volume for a legal department or research group), using Kimi for the long-context extraction and GPT-4o for downstream summarisation can reduce monthly API costs by 40 to 60 percent compared to running everything through GPT-4o alone.

Canadian Business Considerations

Canadian organisations considering Kimi for production workflows should evaluate three factors specific to the Canadian context.

Data Residency and PIPEDA Compliance

Moonshot AI's hosted API processes data on servers outside Canada. For documents that contain personal information subject to PIPEDA, you need to assess whether sending that data to an external API constitutes a transfer that requires consent or contractual safeguards. For many business documents (contracts, technical documentation, market research), this is not an issue. For documents containing customer personal information, health records, or financial data tied to identifiable individuals, proceed carefully.

When to Self-Host Kimi

Moonshot AI has released open-weight versions of Kimi that can be deployed on your own infrastructure. Self-hosting makes sense when:

• You process sensitive documents that cannot leave your network (legal discovery, M&A due diligence, patient records)
• Your organisation operates in a regulated industry with strict data residency requirements (OSFI-regulated financial institutions, provincial health authorities)
• You process high enough volume that self-hosting is cheaper than API fees (typically above 10 million tokens per day)
• You need to customise the model with fine-tuning on your domain-specific documents

Self-hosting requires GPU infrastructure (minimum A100 or H100 GPUs for the full model) and ML engineering expertise to manage the deployment. For organisations building out AI infrastructure, adding Kimi to an existing GPU cluster is straightforward. For those without existing infrastructure, the hosted API is the practical starting point.

Bilingual and Multilingual Documents

Canadian businesses frequently work with bilingual (English/French) documents, particularly in government contracting, Quebec operations, and federal regulatory submissions. Kimi handles multilingual content well within its long context window, meaning you can process a bilingual contract or a mixed-language research corpus without needing to separate and translate first.

Frequently Asked Questions

Key Takeaways