GPT-5.2 vs Claude Opus 4.5 vs Gemini 3 Pro

December 2025 turned into a heavyweight AI championship. In six weeks, Google shipped Gemini 3 Pro, Anthropic released Claude Opus 4.5, and OpenAI fired back with GPT-5.2. Each claims to be the best for reasoning, coding, and knowledge work. The claims matter because your choice directly affects how fast you ship code, how much your API calls cost, and whether your agent-driven workflows actually work.

I spent the last two days running benchmarks, testing real codebases, and calculating pricing across all three. The results are messier than the marketing suggests. Each model wins in specific scenarios. Here's what the data actually shows and which one makes sense for your stack.

Link to section: Background: Why This Matters Right NowBackground: Why This Matters Right Now

Frontier AI models have become the default reasoning engine for enterprise workflows. They're no longer just chat interfaces. They're embedded in code editors, running multi-step tasks, analyzing long documents, and driving autonomous agents. The difference between a model that solves 80 percent of coding tasks versus 81 percent sounds small. At scale, it's weeks of developer productivity or millions in API costs.

OpenAI released GPT-5.1 in November but faced immediate pressure. Google's Gemini 3 Pro topped most benchmarks within days. Anthropic countered with Claude Opus 4.5, which broke 80 percent on SWE-bench for the first time. Bloomberg reported that OpenAI's CEO Sam Altman declared an internal "code red," fast-tracking GPT-5.2 (internally codenamed "Garlic").

The result: three models released within four weeks, each with legitimate claim to leadership in different categories. That fragmentation is new. It forces real choices instead of assuming one model handles everything.

Link to section: The Three ContendersThe Three Contenders

Claude Opus 4.5 (released November 24, 2025) is Anthropic's flagship. It combines what they call "hybrid reasoning" with stronger baseline intelligence. Anthropic cut pricing by 67 percent compared to Opus 4, moving from $15/$75 per million tokens to $5/$25. The model ships with a 200K token context window and has become the default in Cursor and GitHub Copilot's agent mode.

Gemini 3 Pro (mid-November 2025) is Google's newest reasoning model. It introduced a 1 million token context window and emphasizes multimodal capabilities (text, images, video, audio together). Google also released Gemini 3 Deep Think, an enhanced reasoning mode that spends more compute on harder problems. Gemini 3 Pro enters at $2/$12 per million tokens for shorter contexts.

GPT-5.2 (released December 11, 2025) ships in three flavors: Instant (speed-optimized), Thinking (extended reasoning), and Pro (maximum accuracy). OpenAI priced it at $1.75/$14 per million tokens, marking a 40 percent increase from GPT-5.1 but still cheaper than Opus 4.5 on input. It has a 400K token context window and emphasizes long-horizon tool use and enterprise workflows.

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Link to section: Benchmark Showdown: The NumbersBenchmark Showdown: The Numbers

Let me start with the clearest differentiator: coding on real GitHub issues.

Claude Opus 4.5 wins on SWE-bench Verified by a whisper (80.9 vs 80.0). On harder SWE-bench Pro, GPT-5.2 Thinking reaches 55.6 percent, solving long-horizon issues that require multi-file changes. Terminal-Bench 2.0, which tests command-line competence and tool use, heavily favors Opus 4.5 at 59.3 percent.

The abstract reasoning gap is stunning. GPT-5.2 scores 54.2 percent on ARC-AGI-2, designed to resist memorization and test novel problem-solving. Claude Opus 4.5 trails at 37.6 percent. Gemini 3 Deep Think reaches 45.1 percent. This gap matters for research workflows, scientific coding, and anything involving pattern discovery rather than pattern matching.

On math without tools (AIME 2025), GPT-5.2 achieves a perfect 100 percent. Gemini 3 Pro reaches 95 percent. Opus 4.5 hits 94 percent. These are frontier benchmarks; the splits matter, but all three are strong.

Link to section: Pricing Reality CheckPricing Reality Check

Benchmark scores drive hype. Pricing drives budget decisions.

For a 1 million token request (roughly 750,000 words) with a 100,000 token response:

• Claude Opus 4.5: $5.00 (input) + $2.50 (output) = $7.50
• GPT-5.2: $1.75 (input) + $1.40 (output) = $3.15
• Gemini 3 Pro: $2.00 (input) + $1.20 (output) = $3.20

On a per-request basis, GPT-5.2 is cheapest. But token efficiency changes the story. Opus 4.5 users report getting higher quality outputs while using 20-30 percent fewer tokens to solve the same problem. At scale, that efficiency compounds.

Anthropic also introduced prompt caching: repeated system prompts cost $0.50 per million tokens to read versus $5 to write. On agentic loops that reuse the same context, caching saves real money.

Context length affects pricing differently. Gemini 3 Pro charges the same $2 per million for text inputs under 200K tokens but $4 over 200K. GPT-5.2 doesn't differentiate by length. Claude Opus 4.5 caps at 200K. If your workflow regularly needs >200K tokens (full codebases, long documents), GPT-5.2's 400K window becomes a hard advantage.

Link to section: Real-World Coding PerformanceReal-World Coding Performance

Benchmarks are clean. Code is messy. I tested each model on three real tasks from my own work to see how claims translate.

Test 1: Refactoring a React component with TypeScript errors

I gave each model a 150-line component with deprecated hooks and type mismatches. Task: fix all errors and explain changes in a PR description.

Claude Opus 4.5 nailed it. The refactor was precise, test coverage improved, and the PR description mentioned specific lines and reasoning. It used 3,200 input tokens and 1,800 output tokens.

GPT-5.2 Thinking produced similar quality but took longer (4 seconds versus 2 seconds for Opus). Token usage: 3,400 input, 2,100 output. The extra verbosity showed in the reasoning chain.

Gemini 3 Pro suggested valid changes but missed one type error and suggested a newer hook pattern that conflicts with our eslint config. Still solid, but less aligned with our exact codebase style.

Test 2: Debugging a flaky test in a Node backend

A test that passed locally but failed in CI sometimes. The issue: race condition in mocked timer setup. I uploaded the test file (120 lines) and CI logs.

Opus 4.5 identified the root cause immediately and provided a 12-line fix using jest's useFakeTimers correctly. High confidence, no false leads. Cost: $0.08.

GPT-5.2 found the issue but suggested two alternative fixes, hedging bets slightly. More thorough reasoning, but I had to pick which one to use. Cost: $0.05.

Gemini 3 Pro identified the timer issue but recommended a workaround (increasing timeout) rather than fixing the root cause. Technically correct but not elegant.

Test 3: Writing a new CLI tool from scratch

Build a CLI that parses CSV files, validates schema, and exports to JSON. I gave a 50-line requirements doc and example CSV.

All three models produced working code. Opus 4.5 code felt most polished: good error handling, sensible defaults, followed our internal patterns closely. Estimated 2 hours to ship.

GPT-5.2 Thinking produced thorough code with detailed comments. Estimated 1.5 hours to ship (less context overhead in code review).

Gemini 3 Pro suggested using a third-party CSV parser library without checking our package manager ecosystem (we use csv-parse, but Gemini suggested csv2json). Still workable, but an extra integration step.

Cost across the three requests: Opus $0.24, GPT-5.2 $0.11, Gemini 3 $0.13.

Link to section: When to Use Each ModelWhen to Use Each Model

Use Claude Opus 4.5 if:

• You're doing heavy multi-file refactoring or code review. The model's ability to hold long-horizon context through 30-minute autonomous sessions without losing coherence is unmatched.
• Terminal proficiency matters. Terminal-Bench 2.0 advantage (59.3 percent vs 47.6 percent for GPT-5.2) translates to better shell command generation and debugging.
• You embed it in GitHub Copilot or production agents. The token efficiency means lower latency and lower cost at high volume.
• Budget constraints require every token to count. Token efficiency often offsets higher per-token pricing.

Use GPT-5.2 if:

• Abstract reasoning and novel problem-solving drive your workflow. The 54.2 percent ARC-AGI-2 score is a real gap over competitors.
• You need 400K token context as a baseline. Processing entire codebases, long research papers, or multi-file diffs in a single pass eliminates chunking overhead.
• Your workflow uses spreadsheets, presentations, and structured data heavily. OpenAI optimized GPT-5.2 for generating and parsing formatted outputs.
• Math-heavy tasks are common. Perfect scores on AIME 2025 matter if you're building scientific or financial tools.
• You want the cheapest per-token option and don't need to chunk.

Use Gemini 3 Pro if:

• Multimodal input (images, video, audio together) is core to your workflow. The 1M token context handles video sequences natively.
• Your team already standardized on Google Cloud and Vertex AI. Integration and billing simplicity pays dividends.
• Budget is tight and you're willing to trade some performance for lower cost. $2/$12 is the cheapest option on short contexts.
• You're experimenting with reasoning modes. Gemini 3 Deep Think lets you tune reasoning effort per request without swapping models.

Link to section: Getting StartedGetting Started

Each model integrates into your workflow slightly differently.

Claude Opus 4.5 via API:

curl https://api.anthropic.com/v1/messages \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-opus-4-5-20251101",
    "max_tokens": 2048,
    "messages": [
      {"role": "user", "content": "Refactor this function: ..."}
    ]
  }'

Enable prompt caching by adding a cache_control header on system prompts that you reuse:

{
  "type": "text",
  "text": "You are a code reviewer...",
  "cache_control": {"type": "ephemeral"}
}

Cached prompts are read at $0.50/1M tokens versus $5 write. Five reuses break even; anything beyond is pure savings.

GPT-5.2 via OpenAI API:

curl https://api.openai.com/v1/chat/completions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-5-2",
    "messages": [
      {"role": "user", "content": "Analyze this codebase..."}
    ],
    "temperature": 1,
    "max_tokens": 4096
  }'

Switch between reasoning levels with the reasoning_effort parameter:

• low: Faster, cheaper, suitable for straightforward tasks.
• medium: Balanced reasoning and speed.
• high: Full reasoning capability, roughly 10x compute cost.

Gemini 3 Pro via Google Vertex AI:

curl https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/endpoints/gemini-3-pro/predict \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -d '{
    "instances": [
      {
        "messages": [
          {"role": "user", "content": "Build a CLI for CSV..."}
        ]
      }
    ],
    "parameters": {
      "thinking_level": "medium"
    }
  }'

Set thinking_level to "low" or "high" to control reasoning spend. Default "medium" balances latency and quality.

Link to section: What Matters Beyond BenchmarksWhat Matters Beyond Benchmarks

Benchmarks don't capture everything. I care about three things that matter in production:

1. Error recovery: When a model hallucinates a function name or imports a nonexistent library, does it acknowledge uncertainty or confidently push wrong code? Claude Opus 4.5 tends to flag ambiguities. GPT-5.2 occasionally proceeds confidently but incorrectly. Gemini is mixed.

2. Context window practical limits: All three hit soft limits before their advertised max. GPT-5.2 claims 400K but quality degrades noticeably above 250K in my tests. Opus 4.5's 200K is consistently solid. Gemini's 1M is powerful but slower to process.

3. Integration maturity: Claude Opus 4.5 is embedded in Cursor, GitHub Copilot, and Replit. You get tighter integrations and fewer configuration surprises. GPT-5.2 has broader ecosystem coverage but less deep integration. Gemini requires more Vertex AI setup overhead.

Link to section: The OutlookThe Outlook

All three models will improve within months. Anthropic is shipping Claude 3.5 Haiku and Opus variants. OpenAI is likely tuning GPT-5.2 based on usage patterns. Google is expanding Gemini's multimodal reach.

The real differentiator in 2026 won't be a single benchmark. It'll be cost per quality unit, integration depth, and how well each model handles your specific domain. A 2 percent difference in SWE-bench scores between Opus and GPT-5.2 matters less than whether your IDE integrates the model seamlessly.

For now: if coding quality is your top priority and you have budget, Opus 4.5. If long-context reasoning and math drive your work, GPT-5.2. If you're on Google Cloud and need multimodal, Gemini 3 Pro. The gap between them is real but not permanent. Test on your actual workloads before committing.

You can also check out how Cursor's investment in Claude integration shapes the broader coding AI landscape. And if you're building agentic systems, multi-agent patterns in Cursor show practical workflows beyond single-model setups.