GPT-5.2 vs Claude Sonnet 4.5: Real-World Benchmark Comparison

Why This Comparison Matters

GPT-5.2 and Claude Sonnet 4.5 are the two most widely used large language models in production today. If you are building an AI-powered product or integrating LLMs into your workflow, you have almost certainly evaluated one or both of them. Choosing the wrong model does not just degrade output quality — it costs you real money on every API call.

Most benchmark comparisons rely on synthetic evaluations: standardized test sets that measure narrow capabilities under controlled conditions. Those results rarely translate to how models perform on the messy, varied requests that real applications generate. This post takes a different approach. We analyze real API usage data across thousands of production requests routed through LLMWise, measuring the metrics that actually matter: output quality, latency, cost, and reliability.

If you want the full side-by-side breakdown with live data, check out our dedicated GPT vs Claude comparison page.

Methodology

Our analysis draws from anonymized, aggregated request data processed through the LLMWise API over a 30-day window. Every request was categorized by task type (coding, writing, reasoning, general Q&A) and measured across four dimensions:

• Quality — Human preference ratings on a subset of paired outputs, blind-evaluated
• Latency — Time to first token (TTFT) and total generation speed (tokens per second)
• Cost — Actual billed cost per request based on token counts
• Reliability — Success rate excluding client errors (4xx), measuring only model-side failures and timeouts

All requests used default parameters (temperature, max tokens) as configured by users. No cherry-picking, no prompt engineering to favor one model.

Coding Tasks

Code generation, refactoring, and debugging represent the single largest category of API requests we see. Here is how the two models compare:

Claude Sonnet 4.5 has a measurable edge in code generation and debugging. It tends to produce more complete solutions on the first attempt, particularly for multi-file changes and complex refactoring tasks that require understanding broader context. GPT-5.2 is marginally faster in raw generation speed and holds its own on straightforward refactoring where the scope is well-defined.

Verdict: Claude Sonnet 4.5 wins for coding, especially for debugging and greenfield code generation. GPT-5.2 is competitive for simpler refactoring tasks. For a deeper breakdown, see our best LLM for coding guide.

Writing and Content

Creative writing, summarization, technical documentation, and marketing copy make up the second-largest request category.

GPT-5.2 produces tighter, more concise creative writing. Claude Sonnet 4.5 excels at summarization and technical documentation, where its longer outputs tend to capture more nuance. Claude also demonstrates stronger instruction adherence — when you ask for a specific format, tone, or structure, it follows through more consistently.

Verdict: Split decision. GPT-5.2 for punchy creative copy; Claude Sonnet 4.5 for technical writing, summarization, and anything that demands strict format compliance. Our best LLM for writing page has task-specific recommendations.

Reasoning and Analysis

Math, logic puzzles, multi-step analysis, and structured problem-solving.

GPT-5.2 holds a slight advantage on pure math problems, while Claude Sonnet 4.5 performs better on multi-step reasoning tasks that require maintaining context across a longer chain of logic. Both models are effectively tied on formal logic. Claude's chain-of-thought outputs tend to be more transparent and easier to audit, which matters when you need to verify the reasoning, not just the answer.

Verdict: Marginal GPT-5.2 advantage for math-heavy workloads. Claude Sonnet 4.5 for multi-step analysis where explainability matters. For most production use cases, the difference is small enough that latency and cost should be your tiebreaker.

Cost Comparison

Token pricing as of February 2026 (per 1M tokens):

GPT-5.2 is roughly 30-35% cheaper per request at current pricing. Over thousands of daily requests, that gap adds up. However, if Claude's higher first-try correctness saves you even one retry per ten requests, the effective cost difference narrows to nearly zero.

For strategies to reduce your API spend regardless of which model you use, read our guide to reducing LLM API costs.

Latency Comparison

Measured across production requests (p50 values):

GPT-5.2 is consistently faster. It starts streaming sooner and generates tokens at a higher rate. For latency-sensitive applications like chatbots and real-time assistants, this difference is perceptible to end users. For batch processing or asynchronous workflows, it rarely matters.

The Verdict: It Depends (And Here's Why That's the Right Answer)

There is no single "best LLM." Saying so is not a cop-out — it is the conclusion the data supports.

GPT-5.2 wins on cost and latency. Claude Sonnet 4.5 wins on coding quality, technical writing, and instruction adherence. On reasoning tasks, they are close enough that other factors should drive your decision.

The real question is not "which model should I pick?" but "why am I limiting myself to one?" The best production architectures route different task types to different models. A coding assistant should lean on Claude. A real-time chat feature benefits from GPT-5.2's speed. Summarization pipelines might favor Claude for accuracy and GPT for cost.

This is exactly why multi-model strategies outperform single-model commitments. You get the best quality for each task type, built-in redundancy when one provider has an outage, and the flexibility to shift traffic as pricing and capabilities evolve.

How to Use Both Without Managing Two Integrations

Running multiple LLM providers in production means maintaining separate API clients, handling different error formats, managing multiple API keys, and building your own fallback logic. Most teams start with one provider and never switch because the migration cost is too high.

LLMWise solves this with a single API endpoint that routes to GPT-5.2, Claude Sonnet 4.5, and seven other models. You send one request; LLMWise handles provider routing, failover, and unified streaming. Switch models by changing a single parameter — no code changes, no new SDKs.

Key capabilities for multi-model workflows:

• Auto routing — LLMWise analyzes your prompt and routes to the optimal model automatically
• Mesh mode — Circuit breaker failover across providers with zero downtime
• Compare mode — Run the same prompt against multiple models side by side
• Unified billing — One credit system across all providers, no separate API keys required
• BYOK support — Bring your own API keys for direct provider routing when you prefer

If you are currently on OpenAI alone and want to start using Claude alongside it, our migration guide walks through the process step by step.

The best model is the one that fits your specific task. With LLMWise, you do not have to choose just one.