GPT-5.2
OpenAI's GPT-5 series model with a 400K context window, native vision input, and three execution modes — Instant, Thinking, and Pro. Available on Renas AI as part of every paid plan.
Model Specs
- Context window
- 400K tokens
- Max output
- 128K tokens
- Released
- Dec 2025
- Modalities
- textvision
- Capabilities
- reasoningmultimodalfunction-callingjson-mode
About this model
GPT-5.2 is part of OpenAI's GPT-5 series, released on December 11, 2025. It pairs a 400,000-token context window with strong multimodal vision input and reliable function calling. The model ships in three modes — Instant for fast responses, Thinking for hard reasoning, and Pro for the most complex tasks — so the same model identity scales from quick replies to long-running agentic workflows.
On the public benchmarks OpenAI reports, GPT-5.2 scored 93.2% on GPQA Diamond (graduate-level science reasoning), 100% on AIME 2025 (math olympiad), and 70.9% on GDPval — a benchmark that measures real economic value, where the model beats human experts on 7 out of 10 domain tasks at roughly 11x the speed and under 1% the cost. It also reaches near-perfect 4-needle MRCR retrieval at full context, meaning it actually uses the long window rather than degrading near the end.
On Renas AI, GPT-5.2 powers chat, blog writing, AI editing, and content generation flows. You don't manage API keys or rate limits — you spend credits per word, and you can switch between GPT-5.2 and other top models inside the same workspace. That makes it easy to A/B different models on the same prompt without swapping platforms.
Key Strengths
400K-token context, used reliably
400K window with near-perfect 4-needle MRCR retrieval at full length. Unlike many long-context models that degrade past 50% of the window, GPT-5.2 actually uses the full range — useful for whole codebases, long documents, and multi-document synthesis.
Frontier reasoning scores
93.2% on GPQA Diamond (graduate-level science) and 100% on AIME 2025 (math olympiad). Among the strongest models for problems that need multi-step logical or mathematical reasoning.
Real-world expert performance (GDPval)
70.9% on GDPval — beats human domain experts on 7 of 10 economic tasks (engineering, law, medicine, design) at 11x the speed and under 1% the cost. The benchmark closest to actual workplace value.
Native vision input
Drop in screenshots, diagrams, charts, or scanned documents alongside text. The model reads images natively — describing UI bugs, extracting data from tables, or reviewing design mockups in plain English.
Three execution modes
Instant for fast everyday replies, Thinking for hard problems with explicit reasoning, Pro for the largest projects. Same model identity, different speed/depth tradeoffs picked per task.
Reliable structured output and tool use
Strict JSON mode, parallel function calling, and well-formed tool arguments make GPT-5.2 production-grade for agents, automations, and integrations.
Benchmarks
How it compares
GPT-5.2 sits among the top frontier models alongside Claude Opus 4.1, Gemini 1.5 Pro, and Grok 3. The right choice depends on what you value most: raw reasoning, context length, cost efficiency, or real-time information access.
Claude Opus 4.1 has a more polished long-form writing voice and stronger SWE-bench (real software engineering) results, but costs roughly 5x more per word (0.35 vs 0.07 credits). GPT-5.2 wins on price-per-quality for most general work; pick Opus when you specifically need its tone or top-end coding agent reliability.
Gemini 1.5 Pro offers a 2M-token context window — about 5x larger than GPT-5.2's 400K — and costs less (0.05 vs 0.07 credits per word). For very long documents, video understanding, or audio analysis, Gemini wins on capacity and price. GPT-5.2 leads on reasoning-heavy tasks (GPQA, AIME) where Gemini is well behind.
Grok 3 matches GPT-5.2 on price (0.07 credits per word) and offers real-time information access via X data plus a 1M context window. Grok 3 (Think) scores 93.3% on AIME 2025, very close to GPT-5.2's 100%. GPT-5.2 has the edge on GPQA Diamond and GDPval; Grok wins for current-events research and long-context retrieval.
Pros
- Frontier scores on discriminating benchmarks (GPQA Diamond 93.2, AIME 100, GDPval 70.9)
- 400K context that actually works at full length (near-perfect 4-needle MRCR)
- Native vision input with no extra credit cost
- Three execution modes (Instant / Thinking / Pro) for different speed/depth tradeoffs
- Reliable function calling and strict JSON mode
- Available across every Renas AI surface (chat, blog wizard, editor, WordPress)
Things to consider
- More expensive than mid-tier models (0.07 vs 0.002–0.025 credits per word)
- Thinking mode adds latency on simple tasks where Instant or GPT-5 Mini would suffice
- Knowledge cutoff means it lacks real-time information (use Grok 3 for current events)
- Overkill for short-form writing where GPT-5 Mini or Claude Haiku 4.5 deliver similar quality at a fraction of the cost
Best use cases
Long-form content and research
Whitepapers, market analyses, in-depth blog posts, and technical guides where you need the model to keep facts straight across thousands of words. The reliable long context means no chunking required for most realistic inputs.
Complex code generation and refactoring
Bug fixes that require reading large portions of a codebase, architecture proposals, and writing new modules that integrate cleanly with existing patterns. Pair it with the full-codebase-paste pattern for best results.
Document analysis and summarization
Legal contracts, financial filings, scientific papers, and meeting transcripts. Feed the entire document — no chunking required — and ask targeted questions or request structured summaries.
Strategic and analytical reasoning
Pros-and-cons analyses, scenario planning, and decision frameworks where the answer depends on weighing several variables. GPT-5.2's reasoning trace makes it easier to audit how it reached a conclusion.
Multimodal workflows with images
Reviewing UI screenshots for accessibility issues, extracting structured data from photos of forms, or interpreting charts and diagrams. Vision input is included with no extra credit cost beyond the per-word price.
Agentic workflows and tool calling
Customer support assistants, internal copilots, and research agents that need to call tools, query databases, and produce reliably formatted responses. Function calling stays consistent across long sessions.
How to use it on Renas AI
- 1
Step 1
Pick the surface that fits the task
GPT-5.2 is available across the Renas AI platform — AI Chat for conversational work, Blog Wizard for long-form articles, AI Editor for inline document editing, and the WordPress plugin for direct in-CMS generation. Pick the surface that matches the job; the model behaves identically across all of them.
- 2
Step 2
Select GPT-5.2 from the model picker
Every tool that supports text generation has a model picker. Choose GPT-5.2 when the task is hard enough to justify the per-word cost — long documents, complex code, multi-step reasoning, or anything requiring vision input. For shorter or simpler work, GPT-5 Mini is far cheaper and almost as good.
- 3
Step 3
Provide context and constraints
Paste long documents, attach images, or describe your task in detail. The 400K context window means most realistic inputs fit in a single message — entire books, codebases, or year-long meeting archives. The more constraints you give upfront (style, audience, output format), the less iteration you'll need.
- 4
Step 4
Iterate, export, or hand off
Read the response, ask follow-up questions in the same conversation, then export to Markdown, Word, or directly to a connected WordPress site. For repeating workflows, save your prompt as a Persona or wire it into the Blog Wizard pipeline.
Pricing
Pricing on Renas AI
Pay-as-you-go credits, no API keys, no rate limits.
~142,857 words on a 10,000-credit Spark plan
Frequently asked questions
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