Claude 4.1 vs GPT-5: AI Model Comparison

Contents

• Claude 4.1 vs GPT-5: Overview
• Summary Comparison
• Model Lineups
• API Pricing
• Context Windows
• Performance and Benchmarks
• Capabilities
• Use Case Recommendations
• FAQ
• Deep Architecture Differences
• Ecosystem and Tooling
• Performance on Specialized Tasks
• Cost Optimization Strategies
• Migration and Switching Costs
• Hybrid Strategies
• Regulatory and Compliance
• Related Resources
• Sources

Claude 4.1 vs GPT-5: Overview

Claude 4.1 vs GPT-5 requires clarity: Anthropic doesn't ship a model explicitly named "Claude 4.1". The company names its flagship Claude Opus 4.1 (200K context, $15/$75 per million tokens) and newer Claude Opus 4.5 and Claude Opus 4.6. OpenAI's GPT-5 family spans from $0.05/M input (GPT-5 Nano) to $15/M output (GPT-5.4 Pro). The comparison really comes down to Anthropic's full lineup versus OpenAI's, with different tiers solving different problems.

Summary Comparison

Data as of March 2026 from DeployBase API and official documentation.

Model Lineups

Anthropic's Claude Family

The Opus 4.6 line is Anthropic's flagship. 1M token context, $5 input / $25 output per million tokens, 35 tok/s throughput. Beats everything else in the Anthropic catalog on capability. Expensive for high-volume work, but for complex reasoning tasks the capability premium justifies it.

Opus 4.5 ($5/$25, 200K context, 39 tok/s) is slightly slower than 4.6 but same cost. Practical difference is nil for most workloads. Sits in the shadow of 4.6.

Opus 4.1 ($15/$75, 200K context, 21 tok/s) was the previous flagship. Three times the cost of 4.6 at lower throughput. Pure legacy now, kept around for teams with existing integrations.

For cost-sensitive work, Sonnet 4.6 ($3/$15, 1M context, 37 tok/s) handles the bulk of production queries. 1 million token context is massive. Cheaper per token than Opus 4.1 at the same output price. Best bang-for-buck in the Anthropic stack.

Haiku is the budget tier. Haiku 4.5 ($1/$5, 200K context, 44 tok/s) moves faster than the flagship despite lower capability. For classification, extraction, simple Q&A: Haiku nails it and costs one-fifth what Opus does. The older Haiku 3 at $0.25/$1.25 is still available but Haiku 4.5 is faster and barely more expensive.

Full Anthropic model pricing at DeployBase.

OpenAI's GPT Family

The GPT-5.4 line is OpenAI's current flagship. $2.50 input / $15.00 output per million tokens, 272K standard context (extends to 1.05M via API at 2x input cost). Throughput hits 45 tok/s. Rolled out March 5, 2026 with native computer use built in. Not the cheapest, but has the largest ecosystem.

GPT-5.4 Pro ($30/$180 per million tokens) is the "unlimited reasoning budget" tier. Same context, different pricing model. Only worth it for tasks where extended reasoning measurably improves output quality, which is narrow.

GPT-5, GPT-5.1, and GPT-5 Codex all live at $1.25/$10.00 with 400K context. Still solid. Older training, but still widely used because they're cheap and the ecosystem around them is mature. GPT-5 Codex specifically targets code generation.

GPT-5 Mini ($0.25/$2.00, 272K context) is the lightweight workhorse. Good enough for most tasks that don't need flagship reasoning. Massively cheaper than the full GPT-5.4.

GPT-5 Nano ($0.05/$0.40, 272K context) undercuts every other major model on input cost alone. At 5 cents per million tokens, it's the cheapest frontier-model input on the market. Output quality drops noticeably, but for high-volume classification and extraction it's unbeatable on cost.

The o-series (o3, o4-mini) uses explicit reasoning chains. Cost more per request, produce better reasoning traces, and solve genuinely hard math and logic problems. Not general-purpose.

There's also GPT-4.1 ($2/$8, 1.05M context) which nobody talks about but exists. Context window larger than GPT-5.4's standard 272K. Useful if mega-context without the 2x surcharge matters.

Full OpenAI model pricing at DeployBase.

API Pricing

Head-to-Head Costs (as of March 2026)

At the budget tier, GPT-5 Nano is 20x cheaper than Haiku 4.5 on input tokens. But Nano maxes out at 32K output per request while Haiku handles 64K. Different tools.

At the flagship tier, Opus 4.6 costs 2.8x more than GPT-5.4. Opus 4.6 has more context and cleaner output (fewer off-topic tangents per user reports). GPT-5.4 has the ecosystem advantage.

Cost at Scale

A team processing 1 billion tokens/month (500M input, 500M output):

• GPT-5 Nano: $25 input + $200 output = $225/month
• Haiku 4.5: $500 input + $2,500 output = $3,000/month
• Sonnet 4.6: $1,500 input + $7,500 output = $9,000/month
• GPT-5.4: $1,250 input + $7,500 output = $8,750/month
• Opus 4.6: $2,500 input + $12,500 output = $15,000/month
• Opus 4.1: $7,500 input + $37,500 output = $45,000/month

The budget models are 200x cheaper than legacy Opus 4.1. Even Sonnet 4.6 undercuts Opus 4.6 by $6,000/month at 1B token scale.

Context Windows

The 1M context window on Claude Opus/Sonnet 4.6 is a game changer for document-heavy work. Full codebases, legal discovery across 100+ files, research synthesis across 50+ papers: it all fits in one pass.

GPT-5.4 reaches 1.05M via API but tokens above 272K bill at 2x the stated input rate. Effective pricing for extended context: $5.00/M input instead of $2.50/M. That eats into the cost advantage fast.

GPT-4.1 has 1.05M context at standard rate, making it appealing for teams that specifically need mega-context without the 2x surcharge. Almost nobody knows this model exists.

Performance and Benchmarks

General Knowledge (MMLU)

Anthropic hasn't published recent MMLU scores for Opus 4.6 or 4.5. Opus 4 historically scored in the low 90s. OpenAI's GPT-5 is estimated in the low-to-mid 90s based on third-party benchmarks. Practical difference is negligible at this level.

Coding (SWE-bench Verified)

GPT-5.1 scored 76.3% on SWE-bench Verified (real GitHub issue resolution). Claude hasn't published a comparable score. User reports suggest Opus 4.6 is competitive on mid-range coding tasks but no formal data exists.

Long-Context Reasoning

Both companies claim superiority on long-context tasks, but independent benchmarks are sparse. In production, Sonnet 4.6's 1M context without surcharge beats GPT-5.4's 272K standard window, full stop. The context window is the benchmark that matters.

Math (AIME 2025)

GPT-5 scored around 94-95%. Anthropic hasn't published Claude numbers on AIME 2025. Not a differentiator until Anthropic publishes.

Capabilities

Anthropic's Strengths

The 1M context window on Opus and Sonnet 4.6 is unmatched at this price point. Long-form document understanding, codebase analysis, and research synthesis all fit into a single request.

Built for function calling and structured output. Anthropic's implementation is cleaner and more predictable than OpenAI's. Teams building production systems report fewer parsing issues.

Extended thinking is built in. Claude reasons through problems step-by-step without extra API calls or model branching. The reasoning is visible in the output for debugging.

OpenAI's Strengths

Canvas is a dedicated editor for code and long-form writing. Real-time collaboration, syntax highlighting, markdown preview. No competing Claude feature yet.

Code execution runs inline. Python environment with package installation (numpy, pandas, matplotlib), persistent state. Code doesn't leave the chat interface. Data scientists save meaningful time here.

Computer use (GPT-5.4) and vision (multi-image reasoning) are deeply integrated. Screenshot understanding, webpage navigation, form filling: all work natively.

Ecosystem is entrenched. GitHub Copilot, ChatGPT plugins, years of CI/CD integration. Switching costs are high.

Use Case Recommendations

Sonnet 4.6 fits better for:

Long-document analysis. 1M context without surcharge. Entire codebases fit in memory. Full regulatory filings, patent searches spanning dozens of documents, multi-paper research synthesis all land here. Split the documents across multiple API calls and teams lose cross-reference context.

Cost-sensitive work at scale. Sonnet 4.6 at $3/$15 per million tokens processes 1B tokens/month for $9,000. GPT-5.4 at 1B tokens is $8,750. Nearly identical cost. But Sonnet has 1M context vs GPT-5.4's 272K standard. Choose Sonnet unless the ecosystem matters.

Production systems with high precision requirements. Anthropic's structured output and function calling are more predictable. Fewer parsing failures in production means less exception handling code.

GPT-5.4 fits better for:

Dev teams in the OpenAI ecosystem. Canvas, code execution, computer use, existing CI/CD pipelines, GitHub Copilot integration. The toolchain advantage outweighs marginal capability differences. Switching costs are real.

Vision-heavy tasks. Multi-image reasoning, screenshot understanding, diagram interpretation. Claude has vision but OpenAI's implementation is more mature and integrated.

Computer use requirements. Automating webpage navigation, filling forms, reading screen content. GPT-5.4 ships with this built in.

Coding work with immediate execution needs. The inline Python environment with persistent state is faster than copy-pasting code elsewhere. Canvas makes the experience smoother.

Opus 4.6 fits better for:

Complex research and reasoning. Opus 4.6's extra thinking capacity justifies 2.8x the cost of GPT-5.4 for tasks where output quality is the only metric. Multi-step analysis, novel problem-solving, expert-level reasoning.

Budget is unlimited and timeline is tight. Opus 4.6 produces better output faster for hard problems. Time savings beat token savings.

Budget Work (any model tier):

Classification, extraction, simple Q&A: GPT-5 Nano at $0.05/M input. Haiku 4.5 is slower but doesn't matter for batch jobs. Nano costs less.

FAQ

Is Claude 4.1 the same as Claude Opus 4.1?

Yes. Anthropic calls it "Claude Opus 4.1" formally. The "4.1" refers to the model version. Anthropic doesn't have a "Claude 4.1" without the "Opus" designation. The company uses Opus, Sonnet, and Haiku as tier names.

Which is cheaper overall?

Depends on volume and context needs. GPT-5 Nano is cheaper on input ($0.05 vs $1.00 for Haiku 4.5). Sonnet 4.6 and GPT-5.4 cost nearly the same at scale. Opus 4.6 is 2.8x more expensive than GPT-5.4.

Which has longer context?

Opus/Sonnet 4.6 at 1M tokens, no surcharge. GPT-5.4 reaches 1.05M via API but charges 2x for anything above 272K. For long documents, Anthropic wins on both capacity and cost.

Can both be used together?

Yes. Route long-context work to Sonnet 4.6, vision and computer use to GPT-5.4, budget extraction to GPT-5 Nano, and hard reasoning to Opus 4.6. Both expose standard REST APIs.

Which is better for coding?

OpenAI, because of Canvas and code execution. Neither model is objectively better at code generation, but the integrated development experience matters in production.

Deep Architecture Differences

Context Implementation

Claude's 1M context window (Opus/Sonnet 4.6) is native. No surcharge for using full window. The model processes all 1M tokens at the standard input rate.

GPT-5.4's 1.05M context is available via API, but tokens above 272K tokens cost 2x. This effectively penalizes mega-context usage. A 1M token query costs $2.50 × 0.272M + $5.00 × 0.728M = $4,295 input cost. That's expensive.

For teams frequently hitting mega-context limits (full codebase analysis, legal discovery across 100+ documents), Claude's flat-rate 1M window is dramatically cheaper.

Throughput and Latency

Claude Opus 4.1 processes 21 tokens/second. Sonnet 4.6 does 37 tok/s. GPT-5.4 does 45 tok/s.

For a 10K token output, Claude Opus needs 480 seconds (8 minutes). GPT-5.4 needs 222 seconds (3.7 minutes). The difference is significant for long-form output (reports, code generation, detailed analysis).

But most queries don't hit output limits. For 1K output, all models finish sub-second.

Token Counting

Anthropic and OpenAI count tokens differently. A 1,000 word document might be 1,200 tokens in Claude and 1,350 in GPT-5.4.

Token counting differences matter at scale. 1B documents × 150 token difference = 150B extra tokens billed on OpenAI. Significant cost difference.

Always test token counting with your actual data before comparing cost projections.

Ecosystem and Tooling

Claude's Strength: Simplicity

Anthropic's API is straightforward. Function calling works. Structured output works. No surprises.

The trade-off: Claude doesn't have Canvas, code execution, or vision integrations at the depth OpenAI offers.

For backend systems (API, classification, extraction, generation), Claude is excellent. For user-facing applications needing rich interaction, OpenAI edges ahead.

GPT's Strength: Integration Depth

GPT-5.4 has Canvas (real-time collaborative editing for code and long-form writing). Code execution with persistent Python state. Vision with multi-image reasoning. Computer use (screen understanding and automation).

Teams building applications that showcase these features will struggle to replicate them on Claude.

But these features come with complexity. More moving parts. More edge cases. More debugging.

Performance on Specialized Tasks

Long Document Analysis

Claude Opus 4.6 wins. 1M context without surcharge. Analyze a 50,000-line codebase in one query. Cross-reference everything. No context switching.

GPT-5.4 can hit 1.05M but at 2x input cost above 272K. The question shifts from "can I?" to "is it worth the cost?"

Math and Logic

No published benchmarks for Opus 4.6 on math tasks. Historical Claude performance is strong but not SOTA.

GPT-5 is strong on math (AIME 2025 at 94-95%).

Reasoning models (o3, DeepSeek R1) beat both on hard math.

Code Generation

GPT-5.4 has Canvas and code execution, making the UX smoother. Coupled with GitHub Copilot integration, the experience is frictionless.

Claude Opus produces competent code but requires copy-pasting to an editor. Slower workflow.

For professional developers, GPT-5.4's tooling matters more than capability difference.

Content Generation and Writing

Both models are strong. Claude is slightly cleaner on long-form output (fewer tangents). GPT is slightly more creative.

The difference is small. Either works for writing tasks.

Cost Optimization Strategies

Sonnet 4.6 as the Workhorse

Sonnet is 1/2 the cost of Opus 4.6 and handles 90% of tasks. Reserve Opus only for the hardest reasoning problems.

Typical usage: 80% Sonnet, 15% budget models (Haiku), 5% Opus.

This mix costs $5K/month for 1B tokens vs $15K on all-Opus.

GPT-5 Mini and Nano for Scale

GPT-5 Nano at $0.05/$0.40 is unbeatable on cost. Process 1B tokens for $250/month if all tokens are nano-tier.

Obviously output quality drops. But for classification, extraction, and simple Q&A, Nano is sufficient.

Typical usage: 70% Nano, 20% Mini, 10% full GPT-5.4.

Batch Processing

Anthropic offers batch pricing (up to 50% off for async processing). OpenAI also offers batch API discounts on prompt tokens. DeepSeek offers additional discounts for non-real-time workloads. If batch processing at lowest cost is critical, compare all three providers.

Migration and Switching Costs

Migrating from Claude to GPT

Required changes:

• API endpoint swap
• Model parameter change
• Prompt adjustment (different models respond to prompts differently)
• Output parsing adjustment (format may change)

Estimated effort: 1-2 days for a typical application.

Migrating from GPT to Claude

Required changes:

• API endpoint swap
• Model parameter change
• Restructure function calling (Claude's implementation differs)
• Adjust output formatting expectations
• Retrain any fine-tuned models (can't transfer from OpenAI to Anthropic)

Estimated effort: 2-3 days for a typical application.

Fine-tuned models are not portable. If you've built production fine-tuning on OpenAI, switching to Claude requires retraining from scratch.

Hybrid Strategies

Route by Capability

Complex reasoning: Claude Opus 4.6 (capability worth the cost) Standard workload: Sonnet 4.6 (good balance) Speed-critical: GPT-5.4 (throughput advantage) Budget work: GPT-5 Nano (cost only) Real-time: Groq (latency only)

This hybrid approach requires:

• Routing logic (which task goes where)
• Multiple API keys
• Monitoring and observability

Worth it for teams processing >10B tokens/month.

A/B Testing Models

For uncertain tasks, run both models in parallel and compare output quality. Cost: 2x tokens for evaluation period.

Insight: Maybe Sonnet handles 95% of your queries acceptably. Reserve Opus for the hard 5%.

This empirical approach beats theoretical optimization.

Regulatory and Compliance

Anthropic's Approach

SOC 2 Type II certification. Data residency optionality (EU, US). No HIPAA BAA published yet but possible.

Anthropic is smaller, so dedicated support is more personal. Sales team is responsive.

OpenAI's Approach

SOC 2 Type II, HIPAA BAA, FedRAMP authorization. Compliance certifications are extensive.

Higher-tier support is institutional. Standard processes, but less personalized.

For teams in healthcare or government, OpenAI is the default. Anthropic is catching up but isn't there yet.

Related Resources

• LLM Pricing Comparison
• Anthropic Claude Models and Pricing
• OpenAI GPT Models and Pricing
• GPT-4.1 vs GPT-4o Comparison
• Claude Sonnet 3.5 vs GPT-4.1

Sources

• Anthropic Models and Pricing
• OpenAI API Pricing
• DeployBase LLM Pricing Tracker (as of March 21, 2026)