OpenAI Model Comparison: GPT-5, GPT-4.1, o3, o4-mini — Full Pricing and Benchmark Table

The OpenAI Model Lineup in 2026

OpenAI's model catalog has grown significantly. The GPT-5 family is the current flagship line, spanning from GPT-5 nano (high-volume, cost-sensitive workloads) through GPT-5 (general-purpose default) up to GPT-5.2 (highest benchmark scores). The GPT-4.1 series remains available and still widely used in production, particularly GPT-4.1 mini for teams that validated on it and prefer stability over switching. The o-series (o3, o4-mini) are reasoning models that use chain-of-thought processing — they excel at math, logic, and coding but consume more output tokens per request. Codex models are optimized for code generation and software engineering tasks.

What makes this catalog difficult to navigate is that OpenAI now offers multiple compute tiers for the same base model. GPT-5 can appear as GPT-5 (minimal), (low), (medium), (high), and (xhigh) — each with different pricing and quality characteristics. The tier you select can change costs by an order of magnitude.

The practical challenge is no longer just "which model" but "which model at which tier for which feature." A customer-facing chatbot might use GPT-5 (medium), a background summarization pipeline GPT-5 nano (low), and a complex analysis feature o3. Each combination has a different cost profile, and the aggregate bill hides the per-feature and per-customer breakdown that actually matters for margin.

Reasoning Models (o-series) vs Standard Models (GPT series)

OpenAI's reasoning models — o3 and o4-mini — work fundamentally differently from the GPT series. When you send a prompt to o3, it generates a chain of intermediate reasoning steps before producing the final answer. This "thinking" process happens in the output, which means reasoning models produce significantly more output tokens per request than a standard GPT model for the same prompt.

This has direct cost implications. Even though o3's per-token pricing ($2.00/$8.00 per million tokens) is comparable to GPT-5's ($1.25/$10.00), the actual cost per request can be 3-5x higher because o3 generates more output tokens during reasoning. On math benchmarks (AIME), logic puzzles, and complex coding tasks, reasoning models score substantially higher. On simple tasks like classification or extraction, the reasoning overhead adds cost without improving results.

A factor teams overlook is that chain-of-thought tokens are not user-facing. The final answer may be short, but the model generated hundreds or thousands of reasoning tokens to get there — all billed at the output token rate. For tasks where a standard GPT model already produces correct answers, those reasoning tokens are pure waste.

The practical rule: if a competent human could answer the question in under five seconds without writing anything down, a reasoning model is overkill. If they would need a scratch pad or several minutes of focused thought, a reasoning model will produce a better answer. o4-mini offers a budget-friendly entry point — less capable than o3 on the hardest problems, but significantly cheaper and still better than standard models on logic-heavy tasks.

OpenAI Pricing Tiers Explained: minimal, low, medium, high, xhigh

OpenAI now offers many models at multiple compute tiers: minimal, low, medium, high, and xhigh. The same underlying model is available at each tier, but higher tiers apply more compute per request — producing better responses at higher per-token pricing. This gives engineering teams a lever to optimize costs without switching models entirely. For many production workloads with structured prompts and well-defined output formats, a lower tier performs identically to a higher tier.

The pricing differences between tiers can be substantial. Moving from GPT-5 (xhigh) to GPT-5 (minimal) reduces per-token costs significantly while keeping the same model architecture. This is lower-risk than switching models entirely, which may change behavior in ways that break your prompts.

The practical approach: default to a middle tier (medium or high) during development, then A/B test lower tiers in production. Track accuracy, user satisfaction, and task completion rate alongside cost per request. If quality holds, drop the tier. If not, you have the data to justify the higher spend.

OpenAI Model Deprecation and Migration

OpenAI regularly deprecates older models. The typical pattern is an announcement followed by a grace period before the old model stops accepting requests. Teams with hardcoded model names and no migration plan can find themselves scrambling to update, test, and deploy on a tight timeline.

Migrations are not search-and-replace. A newer model may produce longer or shorter outputs, interpret ambiguous instructions differently, or change structured response formats. Testing prompts against the new model and validating output quality is essential — skipping this step is how teams end up with subtle quality regressions in production.

Deprecation also affects costs. Per-token pricing almost always changes with a newer model — sometimes cheaper, sometimes more expensive. OpenAI has historically dropped prices over time, but newer, more capable models sometimes cost more. Teams that track cost per model and per feature can predict the financial impact of a migration before it happens, rather than discovering it on the next invoice.