GPT-5.2 vs Gemini 3: Pricing, Features and How to Pick the Right AI Model

Estimated reading time: 18–20 minutes

Table of Contents

• Introduction: Why GPT-5.2 Matters
• GPT‑5.2 Model Family Overview
• What’s New in GPT‑5.2 vs GPT‑5.1
• GPT‑5.2 as a Reasoning Model
• GPT‑5.2 Tools and Agent Workflows
• Using GPT‑5.2 with the Responses API
• Migration to GPT‑5.2 from Older Models
• OpenAI GPT‑5.2 Pricing
• Gemini 3 Pro Overview and Pricing
• GPT‑5.2 vs Gemini 3 Pro: Cost Comparison
• When to Choose GPT‑5.2 vs Gemini 3 Pro
• Further Reading: Lyfe AI Articles on GPT‑5
• Conclusion: GPT‑5.2 as the New Baseline
• References
• Frequently Asked Questions

Introduction: Why GPT‑5.2 Matters Right Now

GPT‑5.2 is OpenAI’s newest flagship reasoning model, and it is quickly becoming the default choice for real work: coding, automation, analysis, and complex agent workflows.

Unlike earlier GPT‑5.1 and GPT‑5 models, GPT‑5.2 is tuned not just to answer questions, but to plan, reason, and act. It combines broad world knowledge with powerful tools, long‑running context management, and fine‑grained controls over how hard it “thinks”.

In this article you’ll learn:

• How the GPT‑5.2 model family is structured and when to use each variant.
• What’s new in GPT‑5.2 compared with GPT‑5.1, especially for reasoning and tools.
• How GPT‑5.2’s reasoning.effort and verbosity controls affect quality, speed, and cost.
• How the Responses API helps you reuse chain‑of‑thought across turns.
• How GPT‑5.2 pricing compares directly to Gemini 3 Pro for typical workloads.

https://platform.openai.com/docs/guides/latest-model

GPT‑5.2 Model Family Overview (GPT‑5.2 models)

GPT‑5.2 sits inside the broader GPT‑5 family. Picking the right GPT‑5.2 model up front avoids over‑spending or under‑delivering.

Core GPT‑5.2 models

• gpt‑5.2
  • Best for complex reasoning, broad world knowledge, and multi‑step agentic tasks.
  • Replaces gpt‑5.1 as the main flagship model.
  • Designed for code‑heavy workflows, planning, analysis, and agents.
• gpt‑5.2‑chat‑latest
  • Same core capabilities as GPT‑5.2, tuned for chat UX.
  • Powers the main ChatGPT experience; a routing layer chooses between Instant, Thinking and Pro variants behind the scenes.
• gpt‑5.2‑pro
  • Uses more compute to “think harder” and give more consistent answers on tough problems.
  • You trade higher cost and latency for stronger reliability and depth.

https://platform.openai.com/docs/models/gpt-5.2

Supporting GPT‑5 family models

• gpt‑5‑mini
  • Cost‑optimised GPT‑5 for reasoning and chat.
  • Ideal replacement for o4‑mini or gpt‑4.1‑mini in production.
• gpt‑5‑nano
  • High‑throughput, ultra‑cheap model.
  • Best for simple instruction following, tagging, and large‑scale automation.
• gpt‑5.1‑codex‑max
  • Specialist coding model for Codex and Codex‑like environments.
  • Supports xhigh reasoning, built‑in compaction, and long‑running coding tasks.
  • Use this only for deep, interactive coding agents; use GPT‑5.2 for everything else.

https://platform.openai.com/docs/models/gpt-5-1-mini
https://platform.openai.com/docs/models/gpt-5-nano
https://platform.openai.com/docs/models/gpt-5.1-codex-max

What’s New in GPT‑5.2 vs GPT‑5.1 (GPT‑5.2 features)

GPT‑5.2 is not just a rename of GPT‑5.1. It introduces new features around reasoning effort, verbosity, and context management, and improves performance across core capabilities.

Capability upgrades in GPT‑5.2

• General intelligence: better performance across broad domains.
• Instruction following: more reliable step‑by‑step behaviour.
• Accuracy and token efficiency: similar or better quality with fewer tokens.
• Multimodality: stronger understanding of images and visual inputs.
• Code generation: especially improved for front‑end UI and spreadsheet logic.
• Tool calling and context management: more robust orchestration for agents.
• Spreadsheet understanding and creation: better for analysis and reporting tasks.

https://platform.openai.com/docs/guides/latest-model

New reasoning.effort levels

GPT‑5.2 introduces a new, lower reasoning effort level and an extra high level:

• none – new default, minimal internal reasoning, lowest latency.
• low
• medium
• high
• xhigh – new maximum reasoning effort.

The model uses reasoning.effort to decide how many reasoning tokens to generate before answering. With GPT‑5.2, none is tuned for interactive apps that need speed and still benefit from good prompting.

https://platform.openai.com/docs/guides/latest-model#lower-reasoning-effort

Verbosity controls for GPT‑5.2

You can also control how long and detailed GPT‑5.2’s visible answers are via text.verbosity:

• low – concise, focused outputs with minimal explanation.
• medium – balanced detail (default setting).
• high – long, highly detailed explanations and code.

For code generation, medium/high verbosity leads to longer, more structured code and inline comments; low verbosity keeps responses compact for lower latency and cost.

https://platform.openai.com/docs/guides/latest-model#verbosity

New context management: compaction and summaries

• Compaction: GPT‑5.2 can internally compress long contexts so long‑running tasks are more efficient.
• Concise reasoning summaries: short internal summaries of chain‑of‑thought let the model stay on track without re‑reasoning everything each turn.

These GPT‑5.2 features are critical when building agents that run across many steps or calls.

https://cookbook.openai.com/examples/responses_api/reasoning_items

GPT‑5.2 as a Reasoning Model (GPT‑5.2 reasoning)

GPT‑5.2 is a reasoning model: it “thinks before it speaks” by generating an internal chain‑of‑thought before producing the visible answer.

How GPT‑5.2 reasoning works

• Trained with reinforcement learning to plan, analyse, and reason in multiple steps.
• Excels at complex problem‑solving, coding, scientific reasoning, and multi‑step planning.
• Ideal for agentic workflows that need careful orchestration of tools and actions.

https://platform.openai.com/docs/guides/reasoning

Reasoning tokens vs input and output tokens

GPT‑5.2 reasoning introduces a third token type:

• Input tokens – your prompts, instructions, and context.
• Output tokens – the visible reply text.
• Reasoning tokens – the model’s internal chain‑of‑thought.

Reasoning tokens:

• Are not visible in the main text output.
• Still occupy context window space.
• Are billed as output tokens and appear in usage.output_tokens_details.reasoning_tokens.

https://platform.openai.com/docs/api-reference/responses/object

Managing context and costs for GPT‑5.2 reasoning

Because reasoning tokens share the same context window, you must leave enough space for both reasoning + visible output. For complex tasks, GPT‑5.2 may generate from a few hundred up to tens of thousands of reasoning tokens.

To control cost:

• Set reasoning.effort based on difficulty (none for easy, medium+ for hard problems).
• Use max_output_tokens to cap the sum of reasoning and visible tokens.
• Start with at least 25,000 tokens reserved for reasoning + outputs when experimenting, then tune down once you understand typical usage.

If the model hits the context or max_output_tokens limit, you may receive an incomplete status and even no visible answer, but you still pay for reasoning. When that happens, raise the limit, reduce reasoning effort, or simplify the prompt.

https://platform.openai.com/docs/guides/reasoning#controlling-costs

GPT‑5.2 Tools and Agent Workflows (GPT‑5.2 tools)

GPT‑5.2 tools are what turn the model into a practical agent that can edit files, run commands, and call APIs, rather than just generate text. The tools system is central if you’re building automation or robotic process automation (RPA)‑style workflows.

Custom tools and free‑form inputs

With GPT‑5.2, you can define custom tools using type: "custom", allowing the model to send any raw text as tool input:

• Source code, SQL, and shell commands.
• Configuration files and DSLs.
• Long‑form prose or instructions.

This free‑form input design is ideal for:

• Code execution services.
• Internal scripting engines.
• Domain‑specific languages and automation tools.

https://platform.openai.com/docs/guides/function-calling

Constraining outputs with CFGs

GPT‑5.2 supports context‑free grammars (CFGs) via Lark grammars for custom tools:

• You supply a grammar describing a target syntax (SQL, a config language, a workflow DSL).
• The assistant’s generated text must match that grammar.

This gives you:

• Strictly structured outputs for critical systems.
• Safer tool integration for complex domains (finance, infra, DevOps).

https://platform.openai.com/docs/guides/latest-model#constraining-outputs

Allowed tools for safer agents

The allowed_tools mode under tool_choice lets you:

• Provide a full list of tools once in tools.
• Restrict GPT‑5.2 to a subset per request with tool_choice: { type: "allowed_tools" }.
• Use mode: "auto" (may call one) or mode: "required" (must call one).

This reduces brittle “don’t call X now” prompts and improves both safety and caching.

https://platform.openai.com/docs/guides/function-calling#allowed-tools

The apply_patch tool for code editing

The apply_patch tool is designed for GPT‑5.2 coding agents:

• The model outputs structured diffs describing file changes (create, update, delete).
• Your system applies the patches, then passes updated file states back.
• This allows iterative, multi‑step refactoring and bug‑fixing loops.

Internally, apply_patch uses a free‑form function call. Naming the function clearly reduced failure rates by around 35% in testing.

https://platform.openai.com/docs/guides/tools-apply-patch

Shell tool for local automation

GPT‑5.2 also supports a shell tool, which lets the model interact with a local shell in a tightly controlled way:

• Useful for DevOps, scripting, and RPA‑style workflows.
• Should always be sandboxed and heavily permissioned.

https://platform.openai.com/docs/guides/tools-shell

Preambles: explaining GPT‑5.2 tool calls

Preambles are short, user‑visible explanations GPT‑5.2 generates before a tool call, such as:

“I’m going to call search_docs to get more context on GPT‑5.2 pricing before I answer.”

Benefits:

• Greater transparency and user trust.
• Easier debugging of tool behaviour.
• Better control over how the agent sequences tools.

To enable them, add a system/dev instruction like: “Before you call a tool, explain briefly why.”

https://cookbook.openai.com/examples/gpt-5/gpt-5_prompting_guide#tool-preambles

Using GPT‑5.2 with the Responses API (Responses API)

GPT‑5.2 works with both Chat Completions and the Responses API, but the Responses API is strongly recommended for reasoning models.

Why Responses API is better for GPT‑5.2

• Supports passing chain‑of‑thought (CoT) between turns.
• Reduces repeated reasoning, saving reasoning tokens and cost.
• Improves cache hit rates and latency for multi‑turn workflows.

Most parameters are at parity between APIs, but reasoning.effort and text.verbosity are first‑class in Responses.

https://platform.openai.com/docs/guides/migrate-to-responses
https://platform.openai.com/docs/guides/responses-vs-chat-completions

Keeping reasoning items in context

When using GPT‑5.2 with tools:

• Pass back all reasoning items, tool calls, and tool outputs from the previous response.
• Do this either via previous_response_id or by manually embedding those items in the next input.

This lets GPT‑5.2 continue its prior chain‑of‑thought instead of starting again from scratch, leading to better quality and lower cost.

https://platform.openai.com/docs/guides/reasoning#keeping-reasoning-items-in-context

Migration to GPT‑5.2 from Older Models (GPT‑5.2 migration)

If you’re already on GPT‑5.1, o‑series, or GPT‑4.1, GPT‑5.2 migration is mainly about updating model names and tuning reasoning levels.

Recommended model mappings

• From GPT‑5.1 → use gpt‑5.2 with default settings as a near drop‑in replacement.
• From o3 → use gpt‑5.2 with reasoning.effort: "medium" or "high".
• From GPT‑4.1 → use gpt‑5.2 with reasoning.effort: "none" and good prompts; only increase effort where needed.
• From o4‑mini / GPT‑4.1‑mini → use gpt‑5‑mini plus prompt tuning.
• From GPT‑4.1‑nano → use gpt‑5‑nano plus prompt tuning.

https://platform.openai.com/docs/guides/latest-model#migrating-from-other-models-to-gpt-5-2

Prompting and GPT‑5.2 migration

Treat GPT‑5.2 like a senior co‑worker:

• Give clear goals and constraints, not micro‑instructions.
• For reasoning.effort: "none", ask the model to outline steps explicitly in its answer.
• Use the GPT‑5.2 prompt optimiser in the OpenAI dashboard to auto‑update legacy prompts.

https://cookbook.openai.com/examples/gpt-5/gpt-5-2_prompting_guide
https://platform.openai.com/chat/edit?models=gpt-5.2&optimize=true

GPT‑5.2 parameter compatibility

When reasoning.effort is anything other than "none", GPT‑5.2 does not support:

• temperature
• top_p
• logprobs

For higher reasoning effort, rely on:

• reasoning.effort for depth.
• text.verbosity for explanation length.
• max_output_tokens for cost control.

https://platform.openai.com/docs/guides/latest-model#gpt-5-2-parameter-compatibility

OpenAI GPT‑5.2 Pricing (GPT‑5.2 pricing)

To compare GPT‑5.2 with Gemini 3 Pro, you need the core pricing numbers. All prices below are per 1M tokens (USD).

GPT‑5.2 and related model prices

Model	Input / 1M tokens	Cached Input / 1M	Output / 1M tokens
gpt‑5.2	$1.75	$0.175	$14.00
gpt‑5.2‑chat‑latest	$1.75	$0.175	$14.00
gpt‑5.2‑pro	$21.00	–	$168.00
gpt‑5‑mini	$0.25	$0.025	$2.00
gpt‑5‑nano	$0.05	$0.005	$0.40
gpt‑5.1‑codex‑max	$1.25	$0.125	$10.00

GPT‑5.2’s output price includes both visible tokens and reasoning tokens. Heavy reasoning effort will increase effective output usage.

https://platform.openai.com/docs/pricing

Gemini 3 Pro Overview and Pricing (Gemini 3 Pro)

Gemini 3 Pro (model gemini‑3‑pro‑preview) is Google’s latest flagship multimodal and agentic model, designed to compete directly with GPT‑5.2.

Gemini 3 Pro capabilities

• Strong multimodal understanding (text, images and more).
• Agentic workflows with “thinking tokens” built into the price.
• Tight integration with Google AI Studio and Google Cloud’s Vertex AI.

https://ai.google.dev/gemini-api/docs/pricing#gemini-3-pro-preview

Gemini 3 Pro pricing

For Gemini 3 Pro Preview with standard prompts (≤ 200k tokens):

Item	Price / 1M tokens
Input	$2.00
Output (incl. thinking tokens)	$12.00
Context caching	$0.20

For prompts > 200k tokens:

Item	Price / 1M tokens
Input	$4.00
Output (incl. thinking tokens)	$18.00
Context caching	$0.40

Grounding with Google Search for Gemini 3:

• 5,000 prompts per month free.
• Then $14 / 1,000 search queries (billing begins 5 January 2026).

https://ai.google.dev/gemini-api/docs/pricing

GPT‑5.2 vs Gemini 3 Pro: Cost Comparison (GPT‑5.2 vs Gemini 3)

With both pricing tables in hand, you can now compare GPT‑5.2 vs Gemini 3 Pro on pure token costs.

Token‑level pricing comparison

Model	Input / 1M tokens	Output / 1M tokens	Notes
GPT‑5.2	$1.75	$14.00	Reasoning tokens billed as output
GPT‑5.2‑chat‑latest	$1.75	$14.00	Same pricing as GPT‑5.2
Gemini 3 Pro (≤ 200k)	$2.00	$12.00	Thinking tokens included in output

For most app‑level prompts under 200k tokens:

• GPT‑5.2 is slightly cheaper on input ($1.75 vs $2.00).
• Gemini 3 Pro is slightly cheaper on output ($12.00 vs $14.00).

For very large prompts (>200k tokens), Gemini 3 Pro’s rates increase to $4.00 (input) and $18.00 (output), while GPT‑5.2 keeps its base rates but is bound by its own context window limits.

https://platform.openai.com/docs/pricing
https://ai.google.dev/gemini-api/docs/pricing#gemini-3-pro-preview

Example workload comparison

Imagine a balanced workload:

• 1M input tokens.
• 500k output tokens (including reasoning/thinking tokens).

GPT‑5.2 cost:

• Input: 1M × $1.75 = $1.75
• Output: 0.5M × $14.00 = $7.00
• Total: $8.75

Gemini 3 Pro cost (≤ 200k prompts):

• Input: 1M × $2.00 = $2.00
• Output: 0.5M × $12.00 = $6.00
• Total: $8.00

In this specific scenario, Gemini 3 Pro is slightly cheaper overall. But real‑world costs depend heavily on:

• How verbose your outputs are.
• How much you increase GPT‑5.2’s reasoning.effort.
• How well you use caching in both ecosystems.
• How many external tool calls (like web search) you rely on.

When to Choose GPT‑5.2 vs Gemini 3 Pro (choosing GPT‑5.2)

Both GPT‑5.2 and Gemini 3 Pro are strong multimodal reasoning models. The right choice depends on your stack, use cases, and risk profile.

Choose GPT‑5.2 when

• You’re already invested in the OpenAI ecosystem (Responses API, Codex CLI, GPT‑5 mini/nano tiers).
• You want rich agentic workflows using:
  • Custom tools with free‑form inputs.
  • apply_patch for code editing.
  • Shell integrations for scripted automation.
  • CFG‑constrained outputs for SQL/DSLs.
  • Allowed tools and preambles for safe orchestration.
• You need a clear migration path from GPT‑5.1, o3, or GPT‑4.1.

Evaluate Gemini 3 Pro when

• You live inside the Google ecosystem (Vertex AI, Google Cloud, Workspace).
• You need deep integration with Google Search, Maps, or URL/file tools.
• You want competitive per‑token pricing and don’t mind Google’s model/tooling conventions.

https://ai.google.dev/gemini-api/docs/pricing

Further Reading: Lyfe AI Articles on GPT‑5

If you want broader GPT‑5 context beyond GPT‑5.2 specifically, Lyfe AI has two in‑depth guides you can use as background reading:

• Boost Productivity With GPT‑5: The Ultimate AI Workflow Upgrade – focuses on:
  • 1M‑token context windows and long‑form workflows.
  • Productivity use cases across business, coding, education and RPA.
  • How GPT‑5’s memory turns it into a “digital knowledge worker”.
  https://lyfeai.com.au/gpt-5-productivity-breakthroughs-workflow/
• The Complete Guide To GPT‑5: Features, Migration & Comparison – covers:
  • GPT‑5 features, reasoning, and context windows.
  • Migration from GPT‑4.1, o‑series, and older models.
  • Comparisons with earlier Gemini generations.
  https://lyfeai.com.au/gpt-5-complete-guide-openai-chatgpt-version-5/

Conclusion: GPT‑5.2 as the New Baseline

GPT‑5.2 is now the baseline flagship model in OpenAI’s stack. It blends improved general intelligence, fine‑grained reasoning controls, rich tool support, and strong multimodality into one engine.

For most teams already using OpenAI, migrating to GPT‑5.2 is the logical next step. You gain:

• Better results on complex, multi‑step tasks.
• More control over speed, cost, and explanation length.
• First‑class support for agents via the Responses API and advanced tools.

Gemini 3 Pro is a serious alternative, especially if you’re built on Google Cloud. Pricing is similar in practice; the real decision comes down to which model performs better on your workloads and integrates cleanly with your infrastructure.

The practical next move is simple: run small, realistic benchmarks on both GPT‑5.2 and Gemini 3 Pro, track quality and token usage, then standardise on the stack that gives you the best intelligence per dollar.

References

• Using GPT‑5.2 – OpenAI API
• Reasoning models – OpenAI API
• Pricing – OpenAI API
• Gemini Developer API Pricing – Google AI
• Boost Productivity With GPT‑5 – Lyfe AI
• The Complete Guide To GPT‑5 – Lyfe AI

Frequently Asked Questions: GPT‑5.2 and Gemini 3 Pricing