What Is GPT-5.2? OpenAI's Most Powerful Model Yet
GPT-5.2 is OpenAI's latest flagship model, released in December 2025 and positioned as "the best model for coding and agentic tasks across industries." It represents a significant leap over GPT-5.1, with configurable reasoning effort levels ranging from none to xhigh, a massive 400,000-token context window, and 128,000 max output tokens. Alongside it, OpenAI launched GPT-5.2 Codex — a specialized variant optimized for long-horizon, agentic coding tasks.
But does GPT-5.2 actually deliver? We dug into the benchmarks, pricing, reasoning tiers, and real-world performance to give you a complete picture of where this model stands — and how it compares to Claude Opus 4.6 and Google's Gemini 3 Pro.
GPT-5.2 Key Specifications
Here's what GPT-5.2 brings to the table:
| Specification | GPT-5.2 | GPT-5.2 Codex |
|---|---|---|
| Context Window | 400,000 tokens | 400,000 tokens |
| Max Output | 128,000 tokens | 128,000 tokens |
| Knowledge Cutoff | August 31, 2025 | August 31, 2025 |
| Input Price | $1.75 / 1M tokens | $1.75 / 1M tokens |
| Cached Input | $0.175 / 1M tokens | $0.175 / 1M tokens |
| Output Price | $14.00 / 1M tokens | $14.00 / 1M tokens |
| Reasoning Effort | none, low, medium, high, xhigh | low, medium, high, xhigh |
| Input Modalities | Text, Image | Text, Image |
| MCP Support | Yes | — |
| Function Calling | Yes | Yes |
| Structured Outputs | Yes | Yes |
The pricing is notably competitive at $1.75 input / $14.00 output per million tokens — a modest bump from GPT-5.1's $1.25 input price but with substantially better performance across the board.
Reasoning Tiers: From None to Xhigh
One of GPT-5.2's most distinctive features is its configurable reasoning effort. Unlike previous models that had a single reasoning mode, GPT-5.2 lets developers dial reasoning up or down based on their use case:
- None (default): No reasoning tokens — fastest responses, lowest cost. Best for simple queries, classification, and formatting tasks.
- Low: Minimal reasoning overhead. Good for straightforward coding tasks and general Q&A.
- Medium: Balanced reasoning. Suitable for most production workloads where you need quality without blowing through tokens.
- High: Deep reasoning for complex problem-solving, multi-step logic, and advanced coding.
- Xhigh: Maximum reasoning compute. This is where GPT-5.2 competes with the absolute best models on the planet — and where it scores 51.24 on the Artificial Analysis Intelligence Index.
This tiered approach is genuinely useful. You're not paying for deep reasoning on a simple "summarize this email" task. But when you need the model to debug a complex distributed system or reason through a multi-step math proof, xhigh reasoning is there.
Benchmark Performance: How GPT-5.2 Stacks Up
According to the Artificial Analysis Intelligence Index v4.0 — which incorporates 10 independent evaluations including GDPval-AA, Terminal-Bench Hard, SciCode, Humanity's Last Exam, and GPQA Diamond — here's how GPT-5.2 ranks against the competition:
| Model | Intelligence Index | Reasoning Tier |
|---|---|---|
| Claude Opus 4.6 (Adaptive) | 53.03 | Adaptive |
| GPT-5.2 (xhigh) | 51.24 | xhigh |
| Claude Opus 4.5 | 49.69 | Thinking |
| GPT-5.2 Codex (xhigh) | 48.98 | xhigh |
| Gemini 3 Pro Preview (high) | 48.44 | high |
| GPT-5.1 (high) | 47.56 | high |
| GPT-5.2 (medium) | 46.58 | medium |
| Claude Opus 4.6 | 46.39 | Non-thinking |
| GPT-5.2 (no reasoning) | 33.53 | none |
The key takeaway: GPT-5.2 at xhigh reasoning is the second-best model in the world, trailing only Claude Opus 4.6 in Adaptive mode (53.03 vs. 51.24). That's a remarkably close race. At medium reasoning, GPT-5.2 scores 46.58 — roughly on par with Claude Opus 4.6 without thinking enabled (46.39) and beating Gemini 3 Flash (46.4).
Perhaps most interesting is the spread: GPT-5.2 without reasoning (33.53) is dramatically worse than GPT-5.2 at xhigh (51.24). That's a 53% performance jump just by turning on maximum reasoning — showing how much the reasoning tokens matter.
GPT-5.2 Codex: Built for Agentic Coding
GPT-5.2 Codex is a specialized variant optimized for what OpenAI calls "long-horizon, agentic coding tasks." This isn't just a rebrand — it's a model fine-tuned for the specific demands of autonomous coding agents that need to:
- Navigate large codebases across multiple files
- Plan and execute multi-step implementations
- Handle iterative debugging loops
- Work within sandboxed environments like OpenAI's Codex platform
At xhigh reasoning, GPT-5.2 Codex scores 48.98 on the Artificial Analysis Intelligence Index — placing it above Gemini 3 Pro Preview (48.44) and just below Claude Opus 4.5 (49.69). For a coding-specialized model, that's exceptional general intelligence.
The Codex variant supports reasoning effort levels from low to xhigh (no "none" option — reasoning is always on to some degree, which makes sense for coding tasks). It shares the same pricing as the base GPT-5.2: $1.75 input / $14.00 output per million tokens.
For developers deciding between Claude Code and Codex CLI, GPT-5.2 Codex represents OpenAI's strongest entry yet in the agentic coding space.
GPT-5.2 vs. Claude Opus 4.6 vs. Gemini 3 Pro
The top of the AI leaderboard in early 2026 is a three-way race. Here's how the flagship models compare:
| Feature | GPT-5.2 | Claude Opus 4.6 | Gemini 3 Pro |
|---|---|---|---|
| Top Benchmark Score | 51.24 (xhigh) | 53.03 (Adaptive) | 48.44 (high) |
| Context Window | 400K tokens | 1M tokens | 1M+ tokens |
| Max Output | 128K tokens | 128K tokens | 65K tokens |
| Reasoning Control | 5 levels (none–xhigh) | Adaptive + manual | low/high |
| Coding Variant | GPT-5.2 Codex | Claude Code | — |
| MCP Support | Yes | Yes | Limited |
| Input Pricing | $1.75/M tokens | $15/M tokens | ~$1.25/M tokens |
| Open-Source Option | gpt-oss (120B, 20B) | No | Gemma models |
Claude Opus 4.6 still leads on raw intelligence, but GPT-5.2 is significantly cheaper at the API level ($1.75 vs. $15 per million input tokens). That pricing difference is massive for high-volume production workloads. Gemini 3 Pro trails both on benchmarks but offers Google's infrastructure advantages and competitive pricing.
For a deeper dive into how these models compare for real coding tasks, check out our GPT Codex vs. Claude Opus 4.6 comparison.
OpenAI's Open-Source Play: gpt-oss-120B and gpt-oss-20B
In a move that surprised the AI community, OpenAI also released open-weight models: gpt-oss-120B and gpt-oss-20B. These are available under the permissive Apache 2.0 license on HuggingFace.
The gpt-oss-120B is particularly interesting. Despite its name suggesting 120 billion parameters, it uses a mixture-of-experts architecture with only 5.1 billion active parameters — meaning it fits on a single H100 GPU. Key features include:
- Apache 2.0 license: Full commercial use, no copyleft restrictions
- Configurable reasoning: Low, medium, and high reasoning effort levels
- Full chain-of-thought: Complete access to the model's reasoning process
- Fine-tunable: Full parameter fine-tuning support
- Agentic capabilities: Native function calling, web browsing, code execution, and structured outputs
On the Artificial Analysis leaderboard, gpt-oss-120B (high) scores 33.25 — comparable to o4-mini (33.05) and DeepSeek V3.2 non-reasoning (32.06). It's not going to compete with the flagship closed models, but for a self-hosted, fine-tunable model, it's remarkably capable. The smaller gpt-oss-20B targets low-latency applications where you need a lighter-weight model.
This is OpenAI directly competing with Meta's Llama, Alibaba's Qwen, and DeepSeek in the open-weight space — a strategic shift that gives developers more options for on-premise and custom deployments.
Who Should Use GPT-5.2?
GPT-5.2 isn't a one-size-fits-all recommendation. The right choice depends on your use case:
- High-volume API workloads: GPT-5.2 at medium reasoning offers excellent price-performance. At $1.75 input, it's dramatically cheaper than Claude Opus 4.6 while delivering competitive quality.
- Maximum intelligence tasks: GPT-5.2 at xhigh reasoning is your best option if you want to stay in the OpenAI ecosystem. But Claude Opus 4.6 Adaptive still edges it out on benchmarks.
- Agentic coding: GPT-5.2 Codex is purpose-built for this. If you're using OpenAI's Codex platform or building coding agents, this is the model to use.
- Budget-conscious teams: GPT-5.2 at low or no reasoning is fast and affordable for simpler tasks. For even lower costs, consider GPT-5 mini or GPT-5 nano.
- Self-hosted deployments: The gpt-oss models give you open-weight options with Apache 2.0 licensing.
The Bottom Line
GPT-5.2 is a genuinely excellent model that closes the gap with Claude Opus 4.6 to within two points on independent benchmarks. Its configurable reasoning tiers give developers unprecedented control over the cost-performance tradeoff, and its pricing makes it the most cost-effective frontier model available today.
GPT-5.2 Codex pushes the coding capabilities further, and the gpt-oss open-weight models signal that OpenAI is serious about competing across the entire spectrum — from API to self-hosted.
The AI model landscape in early 2026 is more competitive than ever. Whether you choose GPT-5.2, Claude Opus 4.6, or Gemini 3 Pro depends on your specific needs around pricing, context length, reasoning depth, and ecosystem preferences. At Serenities AI, we track these models closely so you can make informed decisions — check out our latest Claude Opus 4.6 guide for the other side of this comparison.
Frequently Asked Questions
What is the difference between GPT-5.2 and GPT-5.2 Codex?
GPT-5.2 is OpenAI's general-purpose flagship model for coding and agentic tasks across all industries. GPT-5.2 Codex is a specialized variant of the same model, fine-tuned specifically for long-horizon, agentic coding tasks in sandboxed environments like OpenAI's Codex platform. Both share the same pricing ($1.75/$14.00 per million tokens) and context window (400K), but Codex always uses some level of reasoning (low through xhigh) while GPT-5.2 can run with reasoning disabled.
How does GPT-5.2 compare to Claude Opus 4.6?
On the Artificial Analysis Intelligence Index, Claude Opus 4.6 (Adaptive) scores 53.03 versus GPT-5.2 (xhigh) at 51.24 — making Claude slightly more capable at peak performance. However, GPT-5.2 is dramatically cheaper at $1.75 per million input tokens compared to Claude Opus 4.6's $15 per million. For most production workloads, GPT-5.2 offers better value, while Claude Opus 4.6 remains the top pick when absolute intelligence matters most.
What are the GPT-5.2 reasoning effort levels and when should I use each?
GPT-5.2 supports five reasoning levels: none (fastest, cheapest — good for simple tasks), low (light reasoning for basic coding), medium (balanced for production workloads), high (deep reasoning for complex problems), and xhigh (maximum compute for the hardest tasks). Start with medium for most use cases and only scale up to xhigh when you need peak performance on challenging problems.
What are gpt-oss-120B and gpt-oss-20B?
These are OpenAI's first open-weight models, released under the Apache 2.0 license. The gpt-oss-120B has 117B total parameters but only 5.1B active (mixture-of-experts), fitting on a single H100 GPU. The gpt-oss-20B is a smaller variant for low-latency use cases. Both support configurable reasoning, function calling, and fine-tuning. They're available for free download on HuggingFace.
Is GPT-5.2 worth upgrading to from GPT-5.1?
Yes, for most use cases. GPT-5.2 at xhigh reasoning scores 51.24 on the AA Intelligence Index versus GPT-5.1 at high scoring 47.56 — a meaningful 8% improvement. The price increase is modest ($1.75 vs. $1.25 per million input tokens). The addition of the xhigh reasoning tier and the new "none" reasoning option also give you more flexibility. If you're on GPT-5.1 Codex, upgrading to GPT-5.2 Codex is similarly worthwhile for the performance bump.