TL;DR: Claude, ChatGPT and Gemini are all genuinely capable general-purpose models that can assist with legal writing, summarisation and analysis. But all three are trained on broad internet data, not on a curated corpus of Indian judgments. That single fact means any of them can - and regularly do - fabricate Indian case citations that look real but simply do not exist. For grounded Indian legal research you need a purpose-built tool. Niyam.ai indexes 72,000+ Indian Supreme Court judgments, returns cited answers and tells you whether a case is still good law.

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Why this comparison matters for Indian lawyers

The last two years have produced a wave of curiosity in Indian chambers about general-purpose AI. Senior advocates use ChatGPT to draft arguments. Junior associates ask Gemini to summarise hundred-page affidavits. Litigation boutiques subscribe to Claude to speed up due diligence memos.

None of that enthusiasm is misplaced. These tools are genuinely useful for certain things. But lawyers using them for Indian legal research - without understanding what they can and cannot do - are taking on professional risk that most have not fully reckoned with.

The risk is not that AI is unreliable in general. The risk is specific: large language models predict text. When you ask for Indian case law, they produce citations that match the statistical shape of a real citation. They know the format of a Supreme Court neutral citation (2023 INSC 500, for example). They know how a headnote reads. So they produce outputs that look authoritative. The case may not exist.

In the United States, in Mata v. Avianca (S.D.N.Y. 2023), a lawyer filed a brief containing six AI-generated citations to cases that were entirely fictional. The judge imposed sanctions. Indian courts have begun to encounter similar issues, and the professional consequences in a jurisdiction where advocates are officers of the court are serious.

This piece gives you an honest account of each of the three dominant general-purpose models - what they are genuinely good at, where they fall short for Indian legal work, and why the citation problem is structural rather than something a better prompt can fix.

If you want the broader context on how grounded versus ungrounded AI differs, read our piece on native legal AI in India versus generic GPT.

How we evaluated each model

Rather than fabricating benchmark numbers we cannot verify, this comparison is based on qualitative assessment across five dimensions that matter most to practising Indian lawyers:

  1. Long-document handling - Can you paste a 100-page judgment or a multi-hundred-page arbitral award and get useful analysis?
  2. Legal reasoning quality - Does the model apply legal logic coherently, identify relevant principles, and distinguish authorities?
  3. Indian case-law knowledge - Does it know Indian judgments accurately, and does it know the limits of its own knowledge?
  4. Citation reliability - When it cites Indian cases, are those cases real and correctly stated?
  5. Data privacy - What happens to the client-confidential text you paste into these tools?

ChatGPT (OpenAI)

ChatGPT is the model most Indian lawyers tried first. It became synonymous with AI itself for many practitioners, which is both a tribute to OpenAI’s distribution and a source of some confusion: people often assume other models work the same way, when in fact they are quite different.

What ChatGPT does well for legal work

ChatGPT’s instruction-following is very good. If you give it a structured task - summarise this affidavit in bullet points, rewrite this clause in simpler language, draft a letter before action following this format - it executes reliably. The default output style is clean and reasonably formal, which suits legal documents.

Its web-browsing capability (where available) means it can sometimes retrieve live content, including recent legislative text or news. For tasks like checking whether a bill has passed or finding a ministry notification, this can be useful.

GPT-4-class models handle reasonably large documents. Pasting a single judgment of thirty to forty pages and asking for a summary or key-holdings analysis generally works well.

Where it falls short for Indian legal research

ChatGPT’s training data is broad but not weighted toward Indian law. It knows Indian constitutional provisions tolerably well because those are widely reproduced online. But its knowledge of Indian case law - especially High Court decisions, NCLAT orders, and NCLT rulings - is patchy.

More critically, when it does not know a case but the question implies one should exist, it invents one. It will produce a citation with the right formatting, a plausible bench composition, and a holding that fits the question. The case will not exist. There is no internal alarm that fires when the model crosses from memory into confabulation.

ChatGPT also changes over time. OpenAI updates models continuously, and a prompt that worked last month may behave differently today. Practitioners who have built workflows around it need to re-verify outputs regularly.

Privacy note

The default ChatGPT interface sends your inputs to OpenAI’s servers and, under standard terms, may use them to improve future models. For client-confidential drafts this is a material concern. ChatGPT Enterprise and the API with privacy settings configured differently address some of this, but the default consumer product does not.

For a deeper look at using ChatGPT specifically for Indian practice, see our ChatGPT for lawyers in India guide.

Claude (Anthropic)

Claude, built by Anthropic, has developed a strong following among lawyers who do a lot of reading and writing. It has a noticeably different character from ChatGPT: more cautious, more willing to say it is uncertain, and more focused on nuance.

What Claude does well for legal work

Claude’s strongest suit for legal work is handling very long documents. Its context window is large enough to take an entire contract or a lengthy High Court judgment and reason across the whole document at once, not just the last few pages. For tasks like “identify all the conditions precedent in this agreement” or “find every paragraph where the court discusses mens rea,” Claude performs well.

The writing style is also notably good. If you are drafting a detailed opinion letter, a section 34 petition, or a legal memorandum, Claude produces prose that is analytical in tone, properly hedged, and structurally sound. It does not pad unnecessarily.

Claude also tends to flag uncertainty more readily than some competitors. If you ask about a niche point of Indian tax law, Claude is more likely to say something like “I am not certain of the current position under Indian law and you should verify this” - which is a professionally appropriate response even if it feels less satisfying.

Where it falls short for Indian legal research

The same citation problem applies. Claude’s training corpus contains Indian legal material, but it is not grounded in a live, curated Indian judgment database. Ask Claude to cite Indian authority for a proposition and it will often produce citations that are plausible in format but not reliably accurate in content. The case may be a composite of several real cases, or may simply be invented.

Claude is also less useful for tasks that require live data. It has a training cutoff and does not access the internet by default. Asking it about a judgment decided last month will yield either silence or a hallucinated response.

Privacy note

Anthropic offers a consumer product (Claude.ai) and an enterprise tier with different data-handling terms. The consumer product, like ChatGPT, may use inputs for model improvement under default settings. Claude for Enterprise addresses this. For client work, use the appropriate tier.

Gemini (Google)

Gemini is Google’s flagship AI model family. For Indian legal practitioners it has a particular potential advantage: Google indexes an enormous volume of Indian legal content through its web crawl, and Gemini can in some configurations access the live web via Google Search.

What Gemini does well for legal work

Gemini’s integration with Google’s search infrastructure means it can retrieve current legislative text, recent government notifications, and sometimes recent judgments from publicly available sources. For tasks like “what does Section 17 of the PMLA say” or “has this notification been amended,” Gemini with search enabled can give you a more current answer than a model working purely from training data.

The Gemini family also handles multimodal input well. If you need to extract text from a scanned document, identify a signature block in a PDF, or process a table in an affidavit, Gemini’s vision capabilities are competitive.

For practitioners already embedded in the Google Workspace ecosystem - Google Docs, Gmail, Meet - Gemini integration within those tools can speed up routine legal writing tasks without requiring a separate workflow.

Where it falls short for Indian legal research

Despite Google’s indexing advantage, Gemini does not retrieve from a curated, legally verified Indian judgment corpus. Web search retrieval is not the same as retrieval from a structured database of verified judgments. A web-retrieved result might be a judgment summary from a legal news site, an incorrect headnote from a blog, or a superseded version of a circular.

Gemini also inherits the same core problem as the others: it is a generative model. Where retrieval fails or is absent, it generates. That generated output can include fabricated Indian citations.

Legal reasoning quality is solid but not more nuanced than Claude’s, in our assessment. For dense constitutional analysis or a complex multi-layered argument about statutory interpretation, Claude’s prose quality tends to be somewhat sharper.

Privacy note

Google Workspace enterprise plans have clear data-processing terms that are generally acceptable for professional use. The consumer Gemini product has similar caveats to ChatGPT and Claude.ai regarding input use. Check your plan’s data terms before pasting client-confidential material.

Head-to-head comparison table

CriterionChatGPTClaudeGemini
Long-document analysisGoodExcellentGood
Legal reasoning qualityGoodExcellentGood
Indian constitutional law knowledgeModerateModerateModerate
Indian case-law citation reliabilityLowLowLow
Flags its own uncertaintySometimesOftenSometimes
Live / current information accessWith browsing enabledNo (training cutoff)With search enabled
Multimodal (scanned docs, images)GoodModerateExcellent
Legal drafting styleGoodExcellentGood
Data privacy (default consumer product)Review termsReview termsReview terms
Enterprise/API privacy controlsYesYesYes
Grounded in Indian judgment corpusNoNoNo
Can fabricate Indian citationsYesYesYes

The pattern in that final row is the one that matters most for practising lawyers. All three models are capable generalists. None is grounded in Indian case law. All three can and do fabricate Indian citations.

The Indian citation problem: why all three fail at the same point

This is worth spending a moment on because it is not obvious why a sophisticated model that handles complex reasoning would get citations wrong.

The reason is architectural. These models are trained to generate the most statistically plausible continuation of a prompt. They are not databases. They do not retrieve records. When the plausible continuation of your question involves a Supreme Court citation, the model generates one - drawing on the patterns of thousands of real citations it has seen. The result looks exactly like a real citation.

What the model cannot do is verify that the case it just cited was actually decided, that the bench composition is correct, that the holding it attributed to the case is what the court actually held, or that the case has not been overruled.

For Indian law this problem is sharper than for US or UK law for a few reasons. First, Indian case law is vast. The Supreme Court alone has decided hundreds of thousands of cases. High Court databases run into millions of judgments. No general-purpose model trained on broad internet data will have reliable coverage of this corpus. Second, Indian citation formats vary - some use the old AIR format, some use SCR, some use SCC, and neutral citations are a relatively recent introduction. A model that knows these formats can produce very credible-looking fakes. Third, the publicly indexed version of Indian judgments online is inconsistent. Many High Court judgments are not indexed at all, or appear only in imperfect OCR scans. The training signal is patchy.

The result is that asking ChatGPT, Claude or Gemini to support a proposition with Indian authority is a meaningful professional risk. The model will usually produce something. That something may be invented.

See also our guide on how to vet legal AI citation accuracy for a practical verification checklist.

The structural fix to the citation problem is retrieval. Instead of generating a citation from statistical patterns, a retrieval-grounded system searches an actual database of verified judgments, finds the most relevant ones, and quotes directly from them. The citation is real because it comes from a real document in the database, not from the model’s imagination.

Niyam.ai is built on this architecture. The corpus spans 72,000+ Supreme Court of India judgments. When you ask a legal question, Niyam retrieves the most relevant passages from actual judgments, surfaces the citations, and lets you read the source text. You are not trusting a generated summary - you are reading the court’s own words.

The citator function takes this further. It tracks whether a cited case has been followed, distinguished or overruled in subsequent decisions. This is the kind of service that professional legal databases have charged significant subscription fees to provide. Niyam builds it into the same interface as the research function.

For Indian legal work specifically, this difference matters enormously. The three models compared above are genuinely useful - for drafting, for summarisation, for analysis of documents you paste in directly. But for the task of finding and citing Indian authority, they are unreliable by design. A purpose-built Indian legal AI is not a luxury in this context; it is the appropriate tool for the job.

Read our broader analysis of the best AI tools for Indian lawyers and how to build a workflow that uses general-purpose AI for what it is good at while using grounded tools for citation-dependent research.

Which model should you use for which task

Given the honest assessment above, here is a practical breakdown of where each model earns its keep in an Indian legal practice.

Use general-purpose AI (any of the three) for:

  • Summarising a long judgment, contract or affidavit you have pasted in directly
  • Drafting a first version of a letter, notice or clause from a detailed brief
  • Explaining a legal concept in plain language for a client
  • Checking grammar, clarity and structure in a draft
  • Brainstorming arguments or identifying issues you may have missed
  • Translating legal text between Indian languages (with caution and verification)

Use Niyam.ai for:

  • Finding Indian case law in support of a legal proposition
  • Verifying that a cited case exists and says what you think it says
  • Checking whether a precedent is still good law
  • Research memos where citation accuracy is professionally required
  • Any work product that will be filed in court or submitted to a tribunal

The two categories are genuinely complementary. A workflow that uses Claude or ChatGPT for drafting speed and Niyam for citation grounding is more robust than using either alone.

For more on building a complete AI research workflow, see our guide on AI legal research in India.

Frequently asked questions

Can I use ChatGPT to research Indian case law?

You can use it as a starting point for understanding a legal area, but you should not file any ChatGPT-generated citation without independently verifying that the case exists and that it says what ChatGPT says it says. The model fabricates Indian citations with enough confidence that the error is not always obvious.

Claude tends to handle long documents more effectively and its legal prose quality is generally stronger. It is also more willing to flag uncertainty. But on the specific issue of Indian citation reliability, both models have the same structural limitation - neither is grounded in an Indian judgment corpus, so both can fabricate citations.

Gemini with search enabled can retrieve information from publicly available Indian legal sources, which is an advantage for some tasks. But web retrieval is not the same as searching a curated, verified judgment database. Gemini can still produce inaccurate or fabricated citations, and web-retrieved content may include informal summaries, incorrect headnotes or superseded versions of judgments.

Hallucination is when an AI model generates text that is false but stated with confidence. In legal research, the most common form is fabricated case citations - the model produces a case name, court, year and holding that looks entirely real but corresponds to no actual decision. The term “hallucination” is informal; the technical cause is that the model predicts plausible text rather than retrieving verified facts.

Have Indian courts sanctioned lawyers for using AI-generated citations?

Indian courts have issued warnings and expressed concern in cases where AI-generated content appeared to have been filed without proper verification. Formal sanction orders have not yet reached the level of the US Mata v. Avianca case in terms of public attention, but the professional risk under Bar Council rules regarding misleading the court is real and growing.

Does the model tell you when it is making up a citation?

Generally, no. The model produces fabricated citations with the same confident tone as accurate ones. Some models (Claude in particular) are somewhat more likely to hedge, but you cannot rely on the model to self-identify when it has confabulated a citation. Verification is always the practitioner’s responsibility.

Is there an Indian AI that is grounded in real Indian judgments?

Yes. Niyam.ai indexes 72,000+ Supreme Court of India judgments and retrieves directly from that corpus rather than generating from training data. Cited answers link to source text so you can verify the passage the AI is relying on.

A grounded legal AI system retrieves from an actual database of verified documents rather than generating from statistical patterns. When it produces a citation, that citation exists in its database and the quoted passage is drawn directly from the source document. This is fundamentally different from how general-purpose models work.

Can I use these AI tools for court filings in India?

You can use them to assist with drafting, but any citations included in court filings must be independently verified. No reputable AI tool - general-purpose or specialist - removes the advocate’s professional obligation to verify the accuracy of what is filed. The Bar Council of India’s professional conduct rules require advocates not to mislead the court.

How does Niyam.ai handle data privacy for client matters?

Niyam.ai is designed for professional use by Indian advocates. Queries are not used to train models in the way that consumer AI products may use inputs by default. For specific data-processing terms, see the privacy policy on the Niyam.ai site.

Is it safe to paste client-confidential documents into ChatGPT or Claude?

Under the default consumer versions of both products, inputs may be used to improve future models. For client-confidential work, use an enterprise or API plan with appropriate data-processing controls, or use a purpose-built professional tool that has clear terms around client data. Default consumer AI products are not the appropriate venue for confidential legal information.

Using AI as a tool to assist with research and drafting does not by itself breach professional ethics. What creates risk is filing incorrect information, citing cases that do not exist, or failing to exercise independent professional judgment. The tool is not the problem; the absence of verification is.

Which model is best for drafting Indian contracts?

For pure drafting tasks where you are providing the terms and the AI is drafting the language, Claude tends to produce the most coherent and legally precise prose. ChatGPT is also strong. The important caveat is that any clause referencing Indian statutory provisions or case law should be independently verified - the models’ knowledge of recent amendments or circulars may be incomplete.

Can AI summarise a Supreme Court judgment accurately?

If you paste the full text of the judgment into the model, yes - all three models can produce reasonably accurate summaries of a document you have supplied. The risk arises when you ask the model to recall or describe a judgment without supplying the text, in which case the model may generate a plausible but inaccurate summary from memory.

A general-purpose AI is trained on broad internet data and can assist with almost any writing or reasoning task, but it has no privileged access to legal databases. A purpose-built legal AI is built on top of a curated corpus of verified legal documents and uses retrieval to produce grounded answers. The practical difference is citation reliability: a legal AI retrieves real citations; a general-purpose model generates plausible-looking ones.

Do these AI tools know about recent changes to Indian law?

General-purpose models have a training cutoff date. Changes to Indian statutes, notifications, or judgments decided after that cutoff will not be reflected in the model’s knowledge unless it has web access. Niyam.ai’s corpus is updated to reflect new Supreme Court decisions as they are indexed.

Gemini in Google Docs can help with drafting and editing tasks within the Workspace environment. The same caveats apply as for the standalone Gemini product: strong for general drafting assistance, unreliable for Indian citation research. Verify any legal claims independently.

How should I tell junior associates to use these tools responsibly?

A practical policy: general-purpose AI is approved for drafting assistance, summarisation of documents supplied by the lawyer, and initial research scoping. Any citation to be included in a court filing or opinion letter must be verified against a primary source (the original judgment or a grounded legal research tool). No AI output goes into a filed document without a supervising lawyer’s verification.

Is Niyam.ai suitable for junior advocates with limited research budgets?

Niyam.ai is designed to be accessible. The trial starts at ₹100 with 200 credits, giving you enough runway to run a genuine research task and evaluate whether it fits your workflow. Pricing is credit-based so you pay for what you use rather than a fixed monthly subscription regardless of volume. See pricing for full details.

What happens if I cite a fake AI-generated case in court?

Professional consequences range from judicial rebuke to contempt proceedings to Bar Council disciplinary action, depending on the facts, the court, and whether the error appears to have been negligent or deliberate. Beyond formal sanctions, there is the reputational and professional harm of having misled a court. The risk is not theoretical - it has already materialised in cases in the United States and there are documented instances of Indian courts raising concerns.

No. These tools are genuinely useful for tasks where you are not relying on their knowledge of Indian case law - drafting, summarisation of documents you supply, client-facing explanations, and general analysis. The message is not to avoid them but to use them for what they are good at, and to use a grounded Indian legal research tool like Niyam.ai for citation-dependent work.

The bottom line

Claude, ChatGPT and Gemini are all serious tools with genuine strengths. Claude edges ahead for long-document analysis and legal prose. ChatGPT has the broadest general familiarity and the most mature ecosystem of integrations. Gemini brings live search and strong multimodal handling to the table. Any of the three can meaningfully accelerate the non-citation-dependent parts of legal work.

But none of them is built for Indian legal research. They do not search a verified corpus of Indian judgments. They generate from training data. And when training data runs thin - as it inevitably does across the full breadth of Indian case law - they generate citations that are wrong.

That is not a failure of the models. It is a design reality. General-purpose AI is designed to be useful across everything. Useful across everything means not deep enough in any one domain to be reliable on the specific question of whether this Indian case, decided by this bench, on this date, is good law today.

For that question, you need a purpose-built answer. Niyam.ai indexes 72,000+ Supreme Court judgments, returns cited answers drawn from primary sources, and checks citation status through the citator. Start with ₹100, 200 credits, cancel anytime.

Start for ₹100 - or write to us at [email protected] if you have questions about fit for your practice.

You can also compare Niyam.ai to other options or review how the research and citator features work in detail at /solutions/research.