Most "Claude vs GPT" articles you'll find are benchmarking Claude 3.5 against GPT-4o — a full generation behind. This comparison uses the actual 2026 models: Claude Sonnet 4.6 and Opus 4.6 against GPT-5. Plus the angle no global review covers: what Indian developers actually pay in ₹ and which models they can access without an international card.
The lineup
| Model | Maker | Context | Input $/MTok | Input ₹/MTok | Int'l card needed? |
|---|---|---|---|---|---|
| Claude Sonnet 4.6 | Anthropic | 1M tokens | $3 | ~₹331 (via AICredits.in) | No — UPI works |
| Claude Opus 4.6 | Anthropic | 1M tokens | $5 | ~₹552 (via AICredits.in) | No — UPI works |
| GPT-5 | OpenAI | 128K tokens | ~$10 (est.) | — | Yes, USD card required |
| GPT-5 Mini | OpenAI | 128K tokens | ~$0.40 (est.) | — | Yes, USD card required |
GPT-5 pricing is Anthropic's estimate based on OpenAI's published ranges — actual figures may vary. Claude pricing is per Anthropic's published rates as of April 2026.
The context window difference is the first thing worth noting: Claude 4.6 offers 1M tokens GA (no beta header required), while GPT-5 tops out at 128K. For anything involving large codebases, legal document sets, or research corpora, this isn't a marginal difference — it determines whether a task is possible at all with a single API call.
Benchmark results (the numbers that matter)
| Benchmark | Claude Sonnet 4.6 | Claude Opus 4.6 | GPT-5 (est.) |
|---|---|---|---|
| SWE-bench Verified | 79.6% | 80.8% | ~78% |
| ARC-AGI-2 | 58.3% | 58%+ | ~40% (est.) |
| OSWorld computer use | 72%+ | 72%+ | ~65% (est.) |
| MRCR v2 (1M long context) | — | 76% | Not applicable (128K limit) |
The ARC-AGI-2 number deserves attention. Sonnet 4.6 jumped from 13.6% to 58.3% in a single generation — a 4.3x improvement. ARC-AGI-2 tests novel reasoning: tasks the model can't have memorised from training data. It has to actually reason through problems it's never seen. That jump represents a genuine capability change, not benchmark gaming.
SWE-bench Verified is real GitHub issues from real codebases. Claude and GPT-5 are close here (80.8% vs ~78%), which means for routine software engineering tasks, you're getting similar quality. The differentiation shows up in instruction following, context handling, and cost — not raw code quality.
Coding quality — real task comparison
Task 1: FastAPI endpoint with Pydantic v2 validation and pytest tests
Both models produce working code. The difference shows up in constraint-following. Give both models a spec that says "don't use model_validator, use field_validator instead" — Claude follows this constraint reliably. GPT-5 frequently reverts to the more familiar pattern anyway, especially if the spec has multiple constraints.
Claude's output tends to be leaner. GPT-5 produces more boilerplate and more comments explaining what it's doing. If you want verbose and documented, GPT-5. If you want tight production code that follows your spec exactly, Claude.
Task 2: Debug a memory leak in Python asyncio
This is where Claude's interleaved thinking (reasoning at each tool call step, not just once upfront) shows a real advantage. When I gave both models a 400-line async codebase with a subtle task cancellation bug, Claude's debugging path was iterative — it formed a hypothesis, checked it, revised, checked again. GPT-5 frequently commits to a diagnosis after the first read and doesn't revise even when its fix doesn't resolve the issue.
The interleaved thinking isn't magic — it's just that real debugging is iterative, and a model that reasons at each step is better suited to it.
Task 3: Razorpay webhook handler with idempotency
Indian-specific test. Both handle it reasonably well — they know the Razorpay API shape. Claude's instruction following means it doesn't hallucinate webhook event types or invent API fields that don't exist. GPT-5 occasionally produces a handler that looks correct but references payment.captured instead of payment.captured (correct) — subtle inventory of the actual event names matters.
For anything involving Indian payment infrastructure (Razorpay, PhonePe, Cashfree), test both models against the actual API docs. Neither has seen as much Indian fintech code as US/EU fintech.
Instruction following — where Claude wins clearly
Multi-part instructions with constraints is where the gap is most visible. A prompt like "write a REST API in FastAPI — no SQLAlchemy, no ORMs, use raw psycopg3, don't add authentication middleware, keep each function under 30 lines" — Claude follows all five constraints. GPT-5 frequently drops one or two, particularly the negative constraints ("no X").
XML tags and structured output formatting is Claude-native. If your pipeline depends on parsing structured output from Claude, the format adherence is more consistent. Tool use precision is also higher on Opus 4.6 — when Claude calls a tool, it fills the parameters correctly more often, which matters for agent reliability.
Speed comparison
GPT-5 is generally faster for short outputs. First-token latency on a simple prompt is slightly better with OpenAI's infrastructure.
Claude Opus 4.6 has a Fast Mode: 2.5x faster at 6x the standard price ($30/$150 per MTok input/output, or roughly ₹2,520/₹12,600 per MTok via AICredits.in). That's not for general use — it's for latency-critical paths where you're already using Opus because of quality requirements and speed matters too.
Claude Sonnet 4.6 at effort="medium" has competitive latency for the tasks it's suited for. The effort parameter sheds thinking time, so medium-effort Sonnet responses come back faster than high-effort Opus responses. For most application use cases, the speed difference between Claude and GPT-5 is not a decision-making factor.
The India-specific comparison
This is what the global benchmarks miss entirely.
| Factor | Claude 4.6 | GPT-5 |
|---|---|---|
| UPI / INR payment | Yes — via AICredits.in | No — USD card required |
| Minimum spend to start | ₹100 (AICredits.in) | N/A |
| GST invoice | Yes (AICredits.in provides) | No (foreign transaction) |
| Latency from India | ~1.5–2s first token | ~1.5–2s first token |
| Hindi / Hinglish support | Good | Good |
| 1M context window | Yes (Sonnet + Opus) | No (128K only) |
| Model retiring frequency | Medium — 4.5 models retired | High — GPT-4 variants churned frequently |
The payment access gap is significant. OpenAI's API requires an international USD credit card — something many Indian developers and founders don't have or don't want to set up for a side project or early-stage product. AICredits.in provides API access to Claude (and GPT-4o, Gemini, and 300+ other models) with UPI payment in ₹. ₹100 gets you started.
Try it now with AICredits.in
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Which to use when
Use Claude 4.6 when:
- Instruction following precision matters (constraint-heavy specs, structured output)
- You need 200K+ context (large codebases, legal documents, long research)
- Building multi-step agents with tool use
- You're in India without an international card
- You need 1M context for any reason
Use GPT-5 when:
- Your team has deep OpenAI integration already in production
- You need specific OpenAI API features (fine-tuning, Assistants API v2)
- Speed is the primary constraint and quality is secondary
Use both:
Different tasks warrant different models. AICredits.in gives you API keys for both Claude and GPT-4o under a single billing account with one UPI-linked wallet. You can route classification tasks to Sonnet 4.6 at effort="low" and complex reasoning to Opus 4.6 at effort="high" without managing multiple billing relationships.
Next steps
- Set up Claude 4.6 properly — Claude Opus 4.6 prompting guide
- Get API access in India — AICredits.in review
- Deepseek vs Claude for India — comparison
