ChatGPT vs Claude vs Perplexity vs Grok for Indian CAs in 2026: Context Windows, Memory, Costs, and Real Trade-offs
This is the third post in our AI-in-audit series. The first — Adopting AI in Audit: A Practitioner's Honest Playbook — covered the 7-rule adoption framework. The second — Claude for Indian Audit Work: A 90-Day Practitioner's Guide — went deep on Claude specifically. This post is the head-to-head comparison of the four public LLMs CAs commonly evaluate: ChatGPT, Claude, Perplexity, and Grok.
The question we get most often: Should I subscribe to one of them? Or all four? Which one for which task?
We tested all four on real Indian audit tasks (using anonymised / synthetic data, never client data — see Rule 1 of the 7-rule framework). Here's the honest answer.
The headline comparison (as of May 2026)
| ChatGPT Plus (GPT-4o, o1) | Claude Pro (Opus / Sonnet) | Perplexity Pro | Grok (X Premium+) | |
|---|---|---|---|---|
| Monthly cost (India) | ~₹1,999 | ~₹1,700 | ~₹1,600 | ~₹1,299 (bundled with X) |
| Context window | 128K tokens | 200K tokens | ~32K varying | 131K tokens |
| Persistent memory | Yes (opt-in) | Projects feature (partial) | Limited | Limited |
| Citation quality | Moderate | Moderate | Strong (built-in source citing) | Moderate |
| Live web search | Yes (Browse mode) | No (in standard Pro) | Yes (default behaviour) | Yes (X integration) |
| Code / formula generation | Excellent | Excellent | Good | Good |
| India-hosted | No (US infrastructure) | No (US infrastructure) | No (US infrastructure) | No (US infrastructure) |
| Strongest at | All-rounder; vast tooling | Long-context reasoning, drafting | Citation-anchored research | Current-events + X |
| Weakest at | Some math hallucinations | Limited live search in standard tier | Limited deep reasoning | Audit-specific knowledge |
The headline takeaway: there's no single winner. Each tool has specific strengths. The smart subscription depends on what you do.
Context window — what it actually means for audit work
This is the most-debated and most-misunderstood number. Let's translate it to practical impact.
A "token" is roughly 0.75 of a word in English. So:
- 128K tokens (ChatGPT GPT-4o) = ~96,000 words = ~200 pages of typewritten text
- 200K tokens (Claude Opus / Sonnet) = ~150,000 words = ~300 pages
- 131K tokens (Grok) = ~100,000 words = ~200 pages
- 32K tokens (Perplexity Pro default) = ~24,000 words = ~50 pages
For audit work, what fits in 50 pages vs 300 pages matters in 3 scenarios:
Scenario A: Reading a whole standard
A full SA (e.g., SA 240, SA 315) is 40-80 pages. All four LLMs can ingest a single SA.
Scenario B: Reading multiple SAs together
5-7 SAs together is ~300+ pages. Only Claude Opus / Sonnet fits comfortably. ChatGPT and Grok handle 3-4 SAs at once.
Scenario C: Reading multiple SAs + your firm's methodology + a draft engagement
400+ pages combined. Practically only Claude handles this in one shot.
For most CAs the 128K of ChatGPT or 131K of Grok is sufficient. The 200K of Claude becomes meaningful when you're doing cross-document reasoning across multiple standards or your firm's methodology stack.
Persistent memory — the hidden limitation
This is the issue most CAs don't think about until they hit it.
What "memory" actually means in each tool:
ChatGPT Plus — has a "Memory" feature (opt-in). It retains general user preferences ("I'm a CA in India," "I prefer concise responses") across sessions. It does NOT retain engagement-specific context.
Claude Pro — does NOT have user-level persistent memory. Each conversation starts fresh. BUT Claude has the Projects feature — a workspace where uploaded reference documents and a system prompt persist within the project. You can have multiple conversations within a project, each starting with the same baseline context.
Perplexity Pro — limited memory. Each search is typically standalone.
Grok — limited memory in standard usage.
For audit work, the persistent-memory limitation matters in specific ways:
- "Last week we discussed this client's revenue recognition issues. Can you continue?" — works only if you're in the same Claude Project or have manually re-supplied the context.
- "Apply the framework we built earlier to this new engagement" — same issue. Without explicit re-supply, the LLM has forgotten.
Practical workaround: maintain your own "engagement context" notes. When you start an audit session, paste the relevant context (anonymised) — engagement type, client industry, prior findings, current focus. Treat the LLM as a brilliant analyst with amnesia.
Subscription cost analysis — what you actually pay
For a single user in India (May 2026):
| Tool | Monthly | Annual |
|---|---|---|
| ChatGPT Plus | ₹1,999 | ₹23,988 |
| Claude Pro | ₹1,700 | ₹20,400 |
| Perplexity Pro | ₹1,600 | ₹19,200 |
| Grok via X Premium+ | ₹1,299 | ₹15,588 |
| All four | ₹6,598 | ₹79,176 |
For a 10-person firm subscribing all four for each user: ~₹8 lakh / year without any client-data use.
This is the cost framing that surprises CAs. Public LLM stacks scale linearly with user count. Audit-grade tools like CORAA scale per ENTITY (the client), not per user — typical mid-tier firm pays ₹2.4-4 lakh / year for unlimited users.
ChatGPT (GPT-4o, o1, o3-mini) — strengths and weaknesses
What ChatGPT is best at:
General-purpose research and writing. Largest training corpus. Best-known model. Handles a vast range of tasks acceptably.
Code and formula generation. Excellent at Excel formulas, VBA, Python data analysis. Better than Claude for niche programming tasks.
Multimodal inputs. Can analyse images (charts, screenshots of working papers — but never paste client screenshots). Useful for translating a chart into a text description.
Live web search (with Browse mode). Searches the current web when needed.
ChatGPT Plus Memory feature. Retains user preferences across sessions.
What ChatGPT is weaker at:
Long-context reasoning. At 128K tokens, struggles compared to Claude's 200K.
Following complex multi-step instructions. Sometimes simplifies or skips steps in a long structured prompt.
Citation precision. Hallucinates section numbers and standards references at moderate rate. Always verify.
Math precision in financial calculations. Sometimes makes arithmetic errors on multi-step calculations. Use a calculator (or the CORAA calculators) for actual computation.
Verdict for Indian CAs:
ChatGPT Plus is the safe choice if you're picking only one tool. Strong on general tasks, code, brainstorming, drafting. Subscribe at the manager level for general use.
Claude Pro (Opus, Sonnet) — strengths and weaknesses
What Claude is best at:
Long-context analytical work. 200K context lets you paste multiple SAs, your firm methodology, and a draft working paper in one prompt and ask for cross-document analysis.
Following structured prompts faithfully. When you give Claude a 500-word prompt with 10 instructions, it usually follows all 10. ChatGPT sometimes skips.
Drafting SA-anchored language. CARO 2020 observations, KAM paragraphs, MRL language, Material Uncertainty going concern paragraphs — Claude produces clean drafts more often than ChatGPT in our testing.
Projects feature. Persistent reference documents within a project. Useful for "standing knowledge bases" (SAs, regulations, firm methodology).
What Claude is weaker at:
Live web search in standard Pro. No browse mode equivalent built in. (Anthropic has added some search features, but ChatGPT and Perplexity are stronger here.)
Multimodal inputs. Less developed than ChatGPT for image analysis.
Brand familiarity. Some CAs are still ChatGPT-first by habit. Adopting Claude requires intentional setup.
Verdict for Indian CAs:
Claude Pro is the partner-level tool. The 200K context window and Projects feature make it the strongest for SA-anchored drafting, regulation analysis, and methodology-aware working-paper acceleration.
For the full Claude practitioner guide, see Claude for Indian Audit Work: A 90-Day Practitioner's Guide.
Perplexity Pro — strengths and weaknesses
What Perplexity is best at:
Citation-anchored research. Every answer includes source URLs. For regulatory research where you need to verify against the actual notification, Perplexity is unbeatable.
Current-events queries. "What did SEBI notify about BRSR Core in 2025?" — Perplexity pulls live web sources and cites them.
Quick fact-checking. "What's the current tax rate for domestic companies under Section 115BAA?" — fast, cited answer.
What Perplexity is weaker at:
Long-context analysis. ~32K context (varies by mode) is much smaller than Claude or ChatGPT. Don't paste long documents.
Multi-step reasoning. Better at search-and-cite than at deep analytical reasoning.
Creative drafting. Drafts narrative content less elegantly than Claude or ChatGPT.
Conversation persistence. Each query is somewhat standalone.
Verdict for Indian CAs:
Perplexity Pro is the research tool, not the analytical tool. Useful for verifying citations, finding the latest notifications, and quick fact-checking. Doesn't replace Claude or ChatGPT for drafting work.
The ideal use: open Perplexity in a side tab when researching while drafting in Claude or ChatGPT.
Grok (X Premium+) — strengths and weaknesses
What Grok is best at:
X integration. If you follow audit / regulatory news on X (Twitter), Grok can pull current discussions and surface what's being said about a specific topic.
Current events. Better than ChatGPT or Claude for very recent news (last 48-72 hours).
Bundled pricing. ₹1,299 / month for X Premium+ which includes Grok. If you already pay for X, Grok is "free" within the bundle.
What Grok is weaker at:
Audit-specific knowledge. Less trained on ICAI standards, Companies Act, Indian tax law than ChatGPT or Claude. Hallucinates more on Indian regulatory specifics.
Long-context reasoning. 131K is moderate; not great for multi-document analysis.
Structured prompting. Less reliable at following complex multi-step instructions.
Citation precision. Inconsistent.
Verdict for Indian CAs:
Grok is complementary, not primary. Useful if you're already on X Premium+ for other reasons. Don't subscribe specifically for audit work — ChatGPT, Claude, or Perplexity are stronger.
The "should I subscribe to all four" question
For a partner-level CA running active engagements:
Stack 1: Single tool (₹16K-24K / year): pick Claude Pro if you do a lot of drafting + standards research, pick ChatGPT Plus if you want the broadest general-purpose tool. Skip Perplexity / Grok at this tier.
Stack 2: Two tools (₹40K / year): Claude Pro + Perplexity Pro is the typical partner stack. Claude for drafting and analysis; Perplexity for citation-anchored research.
Stack 3: Three tools (₹55K / year): Add ChatGPT Plus for code generation, brainstorming, and the Memory feature for personal preferences.
Stack 4: All four (~₹80K / year): Only worth it if you have specific use cases for each. Most CAs hit diminishing returns at three tools.
Free tier (₹0): For staff-level / junior tasks, ChatGPT free tier handles Excel formulas, basic research, simple drafting. Claude free tier (with rate limits) is also usable.
For a 10-person firm:
| Stack | Cost (annual, all team) | Use case |
|---|---|---|
| Partners 2× Claude + 1× Perplexity | ~₹50K | Lean — small firm, partner-led |
| 5 partners × (Claude + Perplexity) + 8 managers × ChatGPT | ~₹3.2 lakh | Standard mid-tier |
| Above + 15 staff × ChatGPT | ~₹6.8 lakh | Full team coverage |
For a typical mid-tier firm, ₹3-7 lakh / year on public LLM subscriptions is realistic. Plus an audit-grade tool (CORAA, ~₹2.4-4 lakh / year) for client-data work. Combined: ~₹6-11 lakh / year.
Use the AI ROI Calculator to model your firm's specific cost-benefit.
What about the limitations none of the four solve
All four public LLMs share the same fundamental limits for audit work:
1. None are DPDPA-compliant for client data
All four are US-hosted. None offer India-only hosting in the standard tier. None have contractual no-customer-data-training guarantees in the consumer tier. So: don't paste client data into any of them.
2. None generate an audit trail
When you draft a working paper using Claude, there's no audit-tech record of "the auditor used Claude on 15 May 2026 at 14:32 to draft this CARO observation." For SA 230 documentation purposes, you have to manually note the LLM use.
3. None integrate with your audit workflow
You copy from your audit-tech (or Excel), paste into the LLM, get a response, copy back. The friction is significant — every task involves switching tools.
For these limits, audit-grade alternatives exist. CORAA is one (India-hosted, DPDPA-aligned, audit-trail-by-default, integrated workflow). Caseware, EzAudit and others compete in adjacent spaces.
See the AI Audit Tool Evaluation Checklist — 46 criteria across India compliance, security, audit-grade features, integrations, pricing, vendor quality — to evaluate any audit-grade alternative including CORAA.
Practical recommendation
For most Indian mid-tier CA firms (5-20 partners, 50-300 engagements / year), the smart stack is:
- Claude Pro for partners — drafting, SA-anchored work, methodology-aware analysis (~₹1.7K/month per partner)
- Perplexity Pro for partners — citation-anchored research (~₹1.6K/month per partner)
- ChatGPT Plus for managers — general utility, code, brainstorming (~₹2K/month per manager)
- ChatGPT free / Claude free for staff — basic tasks
- Audit-grade tool (CORAA) for client-data work — India-hosted, audit trail (~₹2.4-4 lakh / year for the firm)
Total cost: ~₹6-11 lakh / year for a typical mid-tier firm.
Net value vs cost: 10-30× ROI in compressed time + improved quality + capacity for new service lines (BRSR Core, DPDP audit, forensic engagements).
The single biggest mistake firms make: subscribing to public LLMs while pasting client data into them. The DPDPA exposure and the absence of audit trail make this approach indefensible at peer review or NFRA inspection. Don't do that.
The smart play is to use the right tool for the right task: public LLMs for drafting / research / brainstorming + audit-grade for client-data work.
Bottom line
Each of the four public LLMs has a specific strength:
- ChatGPT: general-purpose all-rounder, best code generation
- Claude: long-context, structured drafting, partner-level analytical work
- Perplexity: citation-anchored research, regulatory verification
- Grok: current events, X-integration (less differentiated for audit)
None is sufficient on its own for the full audit workflow. None should be used for client data. All make sense in the right role.
A 2-3 tool stack (Claude + Perplexity + optionally ChatGPT) covers the drafting / research / general layer. An audit-grade tool covers the client-data layer. Combined cost ~₹6-11 lakh / year for a mid-tier firm — material ROI against the time savings.
For a firm not yet using any AI tools, the order: start with Claude Pro for 2 partners. Add Perplexity for research. Roll out ChatGPT to managers. Adopt an audit-grade tool for client data. 90-day plan with named owner and quarterly cost-benefit review.
Where to go from here in this series
- Post 1 — Adopting AI in Audit: A Practitioner's Honest Playbook — the 7-rule framework
- Post 2 — Claude for Indian Audit Work: A 90-Day Practitioner's Guide — Claude-specific deep dive
For practical tools mentioned in this post:
- AI ROI Calculator — model your firm's specific cost-benefit
- AI Audit Tool Evaluation Checklist — 46-criterion scorecard for evaluating audit-grade tools
- AI Lab — Tool Guides — Claude / ChatGPT / Perplexity guides for specific audit tasks
- Audit Prompt Library — 30+ tested prompts for Indian audit work
Try CORAA → Audit-grade AI for client-data work, complementing your public-LLM stack. India-hosted, DPDPA-aligned, no customer-data foundation-model training. Per-entity pricing, unlimited users. See pricing · Trust Centre · Browse 23 calculators.