개요
GPT-5.2, released December 11, 2025, represented OpenAI's aggressive response to competitive pressure from Google's Gemini 3 Pro and Anthropic's Claude Opus 4.5 — internally referred to as a 'Code Red' effort. The model introduced a three-tier architecture (Instant, Thinking, Pro) with adaptive reasoning capabilities that dynamically allocate compute based on query complexity. It achieved several milestone benchmarks: the first model to exceed 90% on ARC-AGI-1 (Pro tier), a perfect 100% on AIME 2025, and 70.9% on GDPval — making it the first AI to outperform human industry professionals across 44 occupations on OpenAI's proprietary knowledge-work benchmark.
The model's strongest positioning was in professional knowledge work, scientific reasoning (GPQA Diamond: 92.4%), long-context analysis (near-perfect retrieval at 256K tokens), and coding (SWE-Bench Verified: 80.0%). It delivered a 30-38% reduction in error-containing responses versus GPT-5.1, making it significantly more reliable for production workflows. However, it showed notable weaknesses in agentic tasks (ranked #29 on BenchLM), and its multimodal capabilities lagged behind Gemini 3 Pro on comprehensive vision benchmarks. The model also came at a 40% price premium over its predecessor.
Within the GPT-5 family's rapid evolution, GPT-5.2 served as the 'reasoning milestone' that set new benchmarks before being progressively superseded by GPT-5.3 (March 2026), GPT-5.4 (March 2026, adding 1M context and computer use), and GPT-5.5 (April 2026). As of May 2026, GPT-5.2 Thinking is scheduled for retirement on June 3, 2026, with GPT-5.4+ as the recommended upgrade path. It remains available for budget-conscious users who need strong reasoning at lower cost than the newest frontier models.
벤치마크 및 성능
### Comprehensive Benchmark Scores
| Benchmark | GPT-5.2 Score | Category | Notes |
|---|---|---|---|
| **GPQA Diamond** | 92.4% (Thinking) / 93.2% (Pro) | Science | PhD-level physics, chemistry, biology |
| **AIME 2025** | 100% | Math | Perfect score without external tools |
| **SWE-Bench Verified** | 80.0% | Coding | Manually verified GitHub issues |
| **SWE-Bench Pro** | 55.6% | Coding | Multi-language, contamination-resistant |
| **GDPval** | 70.9% | Knowledge Work | 44 occupations; first to beat human experts |
| **ARC-AGI-1** | 86.2% (Thinking) / 90.5% (Pro) | Abstract Reasoning | First model to exceed 90% |
| **ARC-AGI-2** | 52.9% (Thinking) / 54.2% (Pro) | Abstract Reasoning | +200% vs GPT-5.1's 17.6% |
| **FrontierMath T1-3** | 40.3% | Math | Expert-level research mathematics |
| **CharXiv Reasoning** | 88.7% | Vision | Scientific figure interpretation |
| **ScreenSpot-Pro** | 86.3% | Vision | UI element recognition (+22 pts vs 5.1) |
| **MMMU-Pro** | ~76-79.5% | Multimodal | Comprehensive multimodal understanding |
| **Tau2-bench Telecom** | 98.7% | Agentic/Tool Use | Near-perfect multi-tool orchestration |
| **MRCRv2 (256K)** | ~100% | Long Context | 4-needle retrieval accuracy |
### BenchLM Category Rankings (out of 117 models)
| Category | Rank | Score (0-100) |
|---|---|---|
| Knowledge | #6 | 91.7 |
| Multilingual | #7 | 99.0 |
| Reasoning | #12 | 83.8 |
| Multimodal | #15 | 81.9 |
| Coding | #18 | 80.2 |
| Math | #22 | 81.0 |
| Inst. Following | #21 | 84.9 |
| Agentic | #29 | 61.0 |
### Chatbot Arena Performance
| Arena Category | Elo Rating | Confidence | Votes |
|---|---|---|---|
| Text Overall | 1436 | ±3.8 | 39,304 |
| Coding | 1486 | ±6.7 | 9,063 |
| Hard Prompts | 1460 | ±4.8 | 22,638 |
| Multi-turn | 1446 | ±7.4 | 7,390 |
| Longer Query | 1442 | ±6.1 | 12,035 |
| Instruction Following | 1423 | ±6.0 | 11,500 |
| Math | 1433 | ±12.1 | 2,384 |
| Creative Writing | 1390 | ±8.2 | 6,111 |
### Error Rate Improvement
- Responses with ≥1 error: 6.2% (vs GPT-5.1's 8.8%) — **30% reduction**
- Overall error density: **38% fewer total errors** than GPT-5.1
- Hallucination frequency: significantly reduced across knowledge work tasks
상세 비교
### GPT-5.2 vs Claude Opus 4.5
| Dimension | GPT-5.2 Thinking | Claude Opus 4.5 | Winner |
|---|---|---|---|
| GPQA Diamond | 92.4% | 87.0% | GPT-5.2 |
| SWE-Bench Verified | 80.0% | 80.9% | Claude (marginal) |
| SWE-Bench Pro | 55.6% | 52.0% | GPT-5.2 |
| GDPval | 70.9% | 59.6% | GPT-5.2 |
| ARC-AGI-2 | 52.9% | 37.6% | GPT-5.2 |
| Context Window | 400K | 200K | GPT-5.2 |
| Input Price | $1.75/1M | $5.00/1M | GPT-5.2 |
| Output Price | $14/1M | $25/1M | GPT-5.2 |
**Analysis:** GPT-5.2 outperforms Claude Opus 4.5 on nearly every benchmark, most dramatically on knowledge work (+11.3 pts GDPval), abstract reasoning (+15.3 pts ARC-AGI-2), and science knowledge (+5.4 pts GPQA). It's also significantly cheaper. However, Claude Opus 4.5 retains a slight edge on SWE-Bench Verified (80.9% vs 80.0%) and is widely preferred by developers for coding consistency, safety alignment, and terminal-based agentic tasks (Terminal-bench). Claude's writing quality and tone are also often preferred by practitioners.
---
### GPT-5.2 vs Gemini 3 Pro
| Dimension | GPT-5.2 Thinking | Gemini 3 Pro | Winner |
|---|---|---|---|
| GPQA Diamond | 92.4% | 91.9% | GPT-5.2 (marginal) |
| SWE-Bench Pro | 55.6% | 43.3% | GPT-5.2 |
| GDPval | 70.9% | 53.5% | GPT-5.2 |
| ARC-AGI-2 | 52.9% | 31.1% | GPT-5.2 |
| MMMU-Pro | ~76% | 81.0% | Gemini |
| Context Window | 400K | 1M | Gemini |
| Video Understanding | N/A | 87.6% (Video-MMMU) | Gemini |
| Input Price | $1.75/1M | $2.00/1M | GPT-5.2 |
| Output Price | $14/1M | $12/1M | Gemini |
**Analysis:** GPT-5.2 dominates Gemini 3 Pro on professional knowledge work (+17.4 pts GDPval), coding (+12.3 pts SWE-Bench Pro), and abstract reasoning (+21.8 pts ARC-AGI-2). Gemini maintains clear advantages in multimodal understanding (+5 pts MMMU-Pro), video processing (unique capability), and raw context window size (1M vs 400K). For text-heavy professional work, GPT-5.2 is the stronger choice; for multimedia and massive-document workflows, Gemini leads.
---
### GPT-5.2 vs GPT-5.4 (Successor)
| Dimension | GPT-5.2 Thinking | GPT-5.4 Standard | Delta |
|---|---|---|---|
| GPQA Diamond | 92.4% | 92.8% | +0.4 |
| SWE-Bench Pro | 55.6% | 57.7% | +2.1 |
| GDPval | 70.9% | 83.0% | +12.1 |
| ARC-AGI-2 | 52.9% | 73.3% | +20.4 |
| OSWorld-Verified | 47.3% | 75.0% | +27.7 |
| Context Window | 400K | 1M | +600K |
| Computer Use | None | Native (75.0% OSWorld) | New capability |
| Price (Input) | $1.75/1M | $2.50/1M | +43% |
| Price (Output) | $14/1M | $15/1M | +7% |
**Analysis:** GPT-5.4 represents a meaningful upgrade, particularly in agentic capabilities (native computer use at 75.0% on OSWorld-Verified vs GPT-5.2's 47.3%), knowledge work (+12.1 pts GDPval), and abstract reasoning (+20.4 pts ARC-AGI-2). The 1M context window doubles available memory. The price increase is modest ($2.50/$15 vs $1.75/$14). For users still on GPT-5.2, the upgrade to GPT-5.4 is well-justified for most professional use cases.
커뮤니티 평가
The developer and research community has had a notably mixed reception of GPT-5.2. While benchmark numbers impressed, real-world testing revealed a more nuanced picture.
**Positive Reception:** OpenAI's GDPval results generated significant enterprise interest, particularly in professional services (legal, finance, consulting). Companies like Box reported 40% faster document extraction and 40% accuracy improvements on life sciences tasks. Investment banking teams saw 9.3% improvements in financial modeling accuracy. The long-context capabilities were widely praised — the near-perfect retrieval at 256K tokens addressed a long-standing pain point for RAG-heavy enterprise workflows. Developers appreciated the 30-38% error reduction as a meaningful reliability improvement.
**Critical Reception:** The Turing College review captured the community sentiment well: 'This isn't a clean #1 model story. Gemini 3 Pro still feels like the most natural multimodal model. Claude Opus 4.5 still feels like the safe bet for coding. However, GPT-5.2 closes the gap in most areas to the point where it's now a three-way race.' The 40% price increase drew criticism, particularly since some benchmark improvements were marginal over GPT-5.1. The 'Code Red' narrative — GPT-5.2 shipping less than a month after GPT-5.1 in response to competitive pressure — led to skepticism about whether improvements were genuine capability gains or benchmark optimization.
**Coding Community:** Web development practitioners noted GPT-5.2 could be 'rougher around the edges' in visual output compared to Gemini 3 Pro, which consistently avoids the purple 'AI look' in Tailwind outputs. On LMArena's WebDev leaderboard, GPT-5.2 sits just under Claude Opus 4.5, with all three frontier models separated by single digits. The consensus: all models are close enough that 'good prompt engineering and a lucky seed can put any model on top.'
**Speed Complaints:** Early users reported significant latency issues due to demand surges at launch. Leaderboards tracked GPT-5.2 as notably slower than expected, though this was partly attributed to infrastructure scaling rather than inherent model characteristics. BenchLM measured average throughput at 73 tok/s, while Artificial Analysis measured 62.6 tok/s.
**Adoption Pattern:** GPT-5.2 saw strong enterprise adoption for knowledge work and document analysis but did not dramatically shift developer preference away from Claude for coding tasks. Its rapid obsolescence (superseded by GPT-5.4 and GPT-5.5 within months) frustrated early adopters who invested in prompt engineering and workflow integration.
활용 사례
### 1. Professional Knowledge Work & Document Analysis
**When to choose GPT-5.2:** For tasks involving analysis of spreadsheets, financial models, legal contracts, research papers, and corporate presentations across its 400K context window. GPT-5.2's GDPval performance (70.9%) makes it the first model to reliably produce professional-grade deliverables.
**Example:** An investment banking analyst uploads a 200-page company financial history and asks GPT-5.2 to build a three-statement financial model. The model maintains coherence across all pages, produces structured output, and scores 68.4% accuracy — a 9.3-point improvement over GPT-5.1.
**Why not alternatives:** Gemini 3 Pro scores only 53.5% on GDPval and Claude Opus 4.5 scores 59.6%, both significantly trailing GPT-5.2 for this workload.
### 2. Scientific Research & Graduate-Level Analysis
**When to choose GPT-5.2:** For PhD-level science questions, research paper interpretation, and complex figure/chart analysis. The combination of GPQA Diamond (92.4%), CharXiv (88.7%), and FrontierMath (40.3%) makes it the strongest all-around science assistant of its generation.
**Example:** A biology researcher feeds GPT-5.2 a paper with complex immunology pathway diagrams and asks it to identify unanswered questions and propose follow-up experiments. The model correctly interprets the scientific figures and generates novel, testable hypotheses — a capability specifically noted by early enterprise users.
**Why not alternatives:** While Gemini 3 Pro's GPQA score (91.9%) is close, GPT-5.2's combination of science knowledge + scientific figure reasoning is unmatched. Claude Opus 4.5 trails significantly at 87.0% on GPQA.
### 3. Large Codebase Analysis & Software Engineering
**When to choose GPT-5.2:** For multi-file code refactoring, code reviews, debugging production codebases, and SWE-Bench-style tasks. Its 400K context window can hold entire medium-sized repositories.
**Example:** A developer pastes a buggy TypeScript codebase and asks GPT-5.2 to identify the root cause of a race condition across 15 files, propose a fix, and suggest unit tests. GPT-5.2's 80.0% on SWE-Bench Verified means it successfully resolves approximately 4 out of 5 verified issues on the first attempt.
**Why not alternatives:** Claude Opus 4.5 achieves a slightly higher SWE-Bench Verified score (80.9%) and is often preferred for its cleaner code style and better terminal proficiency. For pure coding, Claude remains the slightly better choice; for coding + science context, GPT-5.2 wins.
### 4. Long-Context Research & Contract Analysis
**When to choose GPT-5.2:** When analyzing documents exceeding 100K tokens — entire books, large legal contracts, comprehensive research reviews, or multi-day chat histories. GPT-5.2 maintains near-perfect accuracy at 256K tokens on MRCRv2, dramatically outperforming GPT-5.1 which degrades sharply with context length.
**Example:** A legal team uploads a 500-page merger agreement (approximately 200K tokens) and asks GPT-5.2 to identify all indemnification clauses, cross-reference them with closing conditions, and flag inconsistencies. The model correctly retrieves all buried clauses without the 'lost in the middle' failures common in earlier models.
**Why not alternatives:** Gemini 3 Pro offers a 1M context window (vs GPT-5.2's 400K) for truly massive documents, but at similar context lengths, GPT-5.2's retrieval accuracy is superior. Choose Gemini when the document genuinely exceeds 400K tokens.
최신 뉴스
- **GPT-5.2 Thinking Retirement (June 3, 2026):** OpenAI has announced that GPT-5.2 Thinking will be retired on June 3, 2026. Users are advised to migrate to GPT-5.4 Thinking or GPT-5.5 for analytical workloads.
- **GPT-5.4 Superseded GPT-5.2 (March 5, 2026):** OpenAI released GPT-5.4 with native computer use (75.0% on OSWorld-Verified), a 1M-token context window (API only), and 83.0% on GDPval (+12.1 pts over GPT-5.2). Pricing: $2.50/$15 per 1M tokens.
- **GPT-5.5 Released (April 23, 2026):** The current frontier model pushed Terminal-Bench 2.0 to 82.7%, OSWorld-Verified to 78.7%, and SWE-Bench Pro to 58.6%, further widening the gap with GPT-5.2. Pricing: $5/$30 per 1M tokens (2x GPT-5.4).
- **GPT-5.3 Instant (March 3, 2026):** Released as a cheaper everyday alternative at ~$0.30/$1.20 per 1M tokens with 26.8% fewer hallucinations than GPT-5.2 with web search enabled, positioning GPT-5.2 as neither the cheapest nor the most capable option.
- **GPT-5.2-Codex Variant (January 14, 2026):** OpenAI released a specialized agentic coding variant purpose-built for planning and executing multi-step engineering tasks autonomously.
- **BenchLM Provisional Ranking:** As of May 2026, GPT-5.2 ranks #21 out of 117 models on BenchLM's provisional leaderboard with an overall score of 80, reflecting its position as a strong but no longer frontier-tier model.