Three AI models dominate the December 2025 landscape: Anthropic’s Claude Opus 4.5, OpenAI’s GPT-5.2, and Google’s Gemini 3 Pro. Each represents the pinnacle of their respective companies’ research, yet they excel in fundamentally different ways.
This is not a simple “which one is best” article. After researching official benchmarks, analyzing API documentation, reading hundreds of Reddit and developer forum discussions, and testing real-world scenarios, the answer is clear: the “best” model depends entirely on what you’re trying to do.
This guide breaks down everything: benchmarks, pricing, context windows, hallucination rates, coding ability, multimodal performance, and the things nobody talks about—like how these models actually perform when you push them to their limits.
Table of Contents
- Model Overview and Release Timeline
- Comprehensive Benchmark Comparison
- Coding and Software Engineering
- Reasoning and Mathematics
- Multimodal and Vision Capabilities
- Context Window and Long-Form Performance
- Hallucination Rates and Accuracy
- Pricing and API Costs
- Real Developer Experiences
- The Verdict: Which Model for Which Task
Model Overview and Release Timeline
| Model | Company | Release Date | Core Strength |
|---|---|---|---|
| Claude Opus 4.5 | Anthropic | November 2025 | Coding, long-horizon agentic tasks |
| GPT-5.2 | OpenAI | December 11, 2025 | Tool-calling, autonomous agents, math |
| Gemini 3 Pro | November 18, 2025 (preview) | Multimodal vision, video understanding |
Claude Opus 4.5
Anthropic positioned Opus 4.5 as their
most intelligent and efficient model, specifically optimized for deep research, handling complex multi-system bugs, and working with office applications. According to Anthropic’s announcement, key architectural claims include:
- First model to break 80% on SWE-bench Verified
- Leads in 7 out of 8 programming languages on SWE-bench Multilingual
- 89.4% on Aider Polyglot Coding benchmark
- Three times cheaper than previous Opus-class models
- Strong prompt injection resistance
Anthropic describes Opus 4.5 as designed for “reliability in complex, tool-rich environments, high-difficulty bug-fixing, and long-horizon agentic workflows.”
GPT-5.2
OpenAI released GPT-5.2 on December 11, 2025,
calling it their “most advanced model for professional knowledge work.” According to their official announcement, the release includes three variants:
- GPT-5.2 Instant: Optimized for speed and cost-efficiency
- GPT-5.2 Thinking: Extended reasoning for complex problems
- GPT-5.2 Pro: Maximum quality for enterprise use
Key claims from OpenAI’s research Include:
- First model to achieve 100% on AIME 2025 without tools
- 98.7% tool-calling accuracy on Tau2-bench Telecom
- Substantially improved long-context understanding up to 1.5 million tokens
- Lower hallucination rates than GPT-5.1
Gemini 3 Pro
Google DeepMind launched Gemini 3 Pro in preview on November 18, 2025, emphasizing multimodal capabilities and vision AI. According to the Google AI Blog, the model is designed to understand and process images, video, and audio alongside text.
- 1 million token context window (claimed industry-leading)
- State-of-the-art on medical and biomedical imaging benchmarks
- Strong video understanding capabilities
- Native integration with Google Cloud services
Comprehensive Benchmark Comparison
The following table compiles results from official announcements, Artificial Analysis, and independent testing:
Software Engineering Benchmarks
| Benchmark | Claude Opus 4.5 | GPT-5.2 | Gemini 3 Pro | What It Measures |
|---|---|---|---|---|
| SWE-bench Verified | 80.9% | 80.0% | 76.2% | Resolving real GitHub issues |
| SWE-bench Pro | N/A | 55.6% | N/A | Harder variant of SWE-bench |
| Aider Polyglot | 89.4% | N/A | N/A | Multi-language coding |
| Tau2-bench Telecom | ~90% (est.) | 98.7% | ~88% (est.) | Tool-calling accuracy |
Analysis: Claude Opus 4.5 leads on the standard SWE-bench Verified benchmark, making it technically the best for fixing real bugs in existing codebases. However, GPT-5.2’s dominance on Tau2-bench Telecom (tool-calling) suggests it’s superior for agentic workflows that require reliable external tool usage.
Mathematical and Scientific Reasoning
| Benchmark | Claude Opus 4.5 | GPT-5.2 | Gemini 3 Pro | What It Measures |
|---|---|---|---|---|
| AIME 2025 | N/A | 100% (no tools) | N/A | Contest-level math |
| GPQA Diamond | ~85% (est.) | 93.2% | 93.8% | PhD-level science |
| MMLU | ~90% | ~92% | ~92% | Broad knowledge |
| MMLU-Pro | 90% | N/A | N/A | Enhanced MMLU |
| Humanity’s Last Exam | N/A | N/A | 41.0% | Extremely hard questions |
Analysis: GPT-5.2 achieved a perfect 100% on AIME 2025, the first major model to do so. This makes it the clear leader for mathematical reasoning. On scientific knowledge (GPQA Diamond), GPT-5.2 Pro and Gemini 3 Deep Think are effectively tied at 93%.
Multimodal and Vision Benchmarks
| Benchmark | Claude Opus 4.5 | GPT-5.2 | Gemini 3 Pro | What It Measures |
|---|---|---|---|---|
| MMMU-Pro | N/A | 76% | 81% | Multimodal understanding |
| Video-MMMU | N/A | 80.4% | 87.6% | Video comprehension |
| ScreenSpot-Pro | N/A | 3.5% | 72.7% | Screen understanding |
| CharXiv Reasoning | N/A | 69.5% | 81.4% | Chart interpretation |
Analysis: Gemini 3 Pro dominates every multimodal benchmark. The ScreenSpot-Pro gap is particularly striking: 72.7% vs 3.5%. If your primary use case involves images, video, or visual understanding, Gemini 3 Pro is the clear winner.
Coding and Software Engineering: Deep Dive
Beyond benchmarks, how do these models actually perform in day-to-day coding? Developer forums reveal significant differences in coding style, architecture decisions, and practical usability.
Code Quality and Style
Claude Opus 4.5 produces what developers describe as “clean, maintainable, and human-like code.” From r/ClaudeAI discussions:
“Opus 4.5 delivered the most complete refactor with consistent naming, updated dependencies, and
documentation. It handles real repo issues effectively.”
Users report that Opus 4.5 excels at:
- Architecture-level refactoring
- Maintaining consistent naming conventions across large codebases
- Generating code that requires minimal cleanup
- Understanding context across multiple files
GPT-5.2 tends to generate code that adheres to common conventions and patterns, which benefits team environments. From r/ChatGPT:
“GPT-5.2 produces more complete and polished solutions with better UI/interaction design and better handling
of edge cases and security patterns.”
Strengths include:
- Following established patterns and conventions
- Better handling of edge cases
- Security-conscious code generation
- Superior for planning and architectural discussions
Gemini 3 Pro shows mixed results in coding contexts. From r/Bard:
“Gemini can be too creative or inconsistent, sometimes optimizing or simplifying decisions explicitly
constrained… may introduce more issues into existing codebases.”
Users noted that Gemini 3 Pro:
- Provides good baseline code for new components
- Strong for creative and experimental solutions
- Can be inconsistent with existing codebase patterns
- Better for making individual components than connecting systems
Token Efficiency in Coding Tasks
A critical factor that few discuss: token efficiency directly impacts cost and latency.
According to Anthropic’s
documentation, Claude Opus 4.5 achieves “higher pass rates while potentially using up to 65% fewer
tokens” for long-horizon coding tasks. Independent testing appears to confirm this:
| Model | Test Completion Time | Cost | Score |
|---|---|---|---|
| Claude Opus 4.5 | 7 minutes | $1.68 | High |
| GPT-5.2 Pro | 82 minutes | $23.99 | High |
Source: Independent developer testing reported on Kilo.AI. While both achieved similar accuracy, Opus 4.5 was dramatically faster and
cheaper in this specific test.
Language-Specific Performance
Not all languages are equal across models. From aggregated user reports on r/LocalLLaMA:
| Language | Best Model | Notes |
|---|---|---|
| Python | Claude Opus 4.5 / GPT-5.2 (tie) | Both excellent |
| TypeScript/JavaScript | Claude Opus 4.5 | Better type inference |
| Rust | GPT-5.2 | More idiomatic patterns |
| Go | Claude Opus 4.5 | Cleaner architecture |
| Niche languages | Claude Opus 4.5 | Better generalization |
| Popular stack (React, Node) | GPT-5.2 | More examples in training |
A recurring theme from Reddit: GPT-5.2 excels in “popular tech stacks” while Claude Opus 4.5 shows better
generalization to unique or niche platforms.
Reasoning and Mathematics
Mathematical Reasoning
GPT-5.2’s perfect 100% score on AIME 2025 (without tools) is unprecedented. This benchmark includes contest-level problems that previously challenged even the best models.
For context:
- GPT-5.1: 94.0% on AIME 2025
- GPT-5.2 Thinking: 100% on AIME 2025
OpenAI notes that GPT-5.2 is “the first major model to exhaust the signal in this contest-level math benchmark.”
Abstract Reasoning (ARC-AGI-2)
ARC-AGI-2 measures a model’s ability to solve
novel visual puzzles without prior training. Results show GPT-5.2 with substantial gains over previous GPT
versions, though the absolute numbers remain low (abstract reasoning remains challenging for all models).
Scientific Reasoning
On GPQA Diamond (PhD-level scienc questions):
- Gemini 3 Deep Think: 93.8%
- GPT-5.2 Pro: 93.2%
- Claude Opus 4.5: ~85% (estimated)
The models are effectively tied at the frontier of scientific reasoning.
Reasoning Style Differences
Beyond benchmarks, the models reason differently:
GPT-5.2: Structured, systematic reasoning. The “Thinking” variant explicitly shows its work
through extended chain-of-thought. Better for mathematical derivations and formal logic.
Claude Opus 4.5: More cautious and narrative reasoning. Users describe it as “more careful” and
less likely to make confident leaps. Enhances stability but may reduce peak problem-solving speed.
Gemini 3 Pro: Good logic and common sense, but users report occasional confident
misrepresentations. One Reddit comment noted it can “misrepresent case law or statutes confidently,” making it
less reliable for high-stakes legal or scientific applications without verification.
Multimodal and Vision Capabilities
Image Understanding
Gemini 3 Pro leads decisively according to Google DeepMind’s benchmarks:
- MMMU-Pro (multimodal understanding): 81% vs GPT-5.2’s 76%
- CharXiv Reasoning (chart interpretation): 81.4% vs GPT-5.2’s 69.5%
- ScreenSpot-Pro (screen understanding): 72.7% vs GPT-5.2’s 3.5%
The ScreenSpot-Pro gap is remarkable. Gemini 3 Pro is genuinely better at understanding screenshots, UI elements,
and visual layouts—critical for tasks like web automation or UI testing.
Video Understanding
Video-MMMU results:
- Gemini 3 Pro: 87.6%
- GPT-5.2: 80.4%
Gemini 3 Pro’s video capabilities extend to medical and biomedical imaging, where Google reports state-of-the-art
performance on MedXpertQA-MM, VQA-RAD, and MicroVQA benchmarks.
Claude’s Limitation
Claude Opus 4.5 cannot generate images. While it can analyze images, if image generation is part of your workflow, you’ll need either GPT-5.2 (via DALL-E integration) or a separate tool like Midjourney.
Context Window and Long-Form Performance
Stated Context Windows
| Model | Context Window | Notes |
|---|---|---|
| Claude Opus 4.5 | 200K tokens | Beta access to 1M for Sonnet 4.5 |
| GPT-5.2 | 400K tokens (API) | Up to 1.5M tokens claimed |
| Gemini 3 Pro | 1M tokens | “Industry-leading” per Google |
Reality Check: Performance Degradation
Stated context windows and actual useful context are different things. All models experience performance degradation as context length increases—a phenomenon researchers call “context rot.”
GPT-5.2:
- GPT-5.1 showed sharp accuracy drops to 29.6% in the 128K-256K token range on certain benchmarks
- GPT-5.2 substantially improved, achieving nearly 100% accuracy on multi-round co-reference resolution tasks
out to 256K tokens - Performance remains “relatively flat” near its stated 400K limit
Claude Opus 4.5:
- Anthropic claims it “excels in long-context storytelling and maintains consistency over extended coding
sessions” - Uses context compaction to summarize older conversation parts
- User reports on r/ClaudeAI
suggest performance issues emerge with very long conversations, including “context collapse” where earlier
information is forgotten
Gemini 3 Pro:
- Google claims “industry-leading long context performance”
- User reports are mixed. Some find the 1M window “game-changing” for story writing
- Others report on r/Bard that
Gemini 3 Pro performs “substantially worse than Gemini 2.5 Pro” in long-context interactions, particularly
with large file uploads - Hallucinations and context forgetting reported in extended sessions
Practical Recommendations
For long documents (100K+ tokens):
- Test with your specific use case—benchmarks don’t capture all scenarios
- Use Retrieval-Augmented Generation (RAG) for very long contexts rather than relying
solely on the context window - Consider Claude Opus 4.5 for extended coding sessions where it maintains consistency
- GPT-5.2 shows the most reliable long-context performance in recent testing
Hallucination Rates and Accuracy
Hallucination—generating confident but incorrect information—remains a challenge for all models. Rates vary
significantly by task, model version, and evaluation methodology.
Reported Hallucination Rates
| Model | Hallucination Rate | Source/Notes |
|---|---|---|
| GPT-5.2 Thinking | 10.9% (5.8% with web) | OpenAI testing |
| GPT-5.2 Thinking (browsing) | <1% (5 domains) | OpenAI testing |
| Claude 3.7 Sonnet | 4.4% | Independent benchmark |
| Claude 4 Sonnet | 4.5% | Independent benchmark |
| Gemini 3 Pro | 13.6% (grounded) | Independent benchmark |
| Gemini 3 Pro | 88% (Omniscience Index) | Independent benchmark |
Important caveat: The 88% Omniscience Index score for Gemini 3 Pro measures something specific—how often the model provides incorrect answers when it should indicate uncertainty. This is different from overall accuracy.
Task-Dependent Accuracy
Hallucination rates vary dramatically by task:
- General knowledge questions: 0.8% (best models)
- Legal information: 6.4%
- Scientific paper summarization: Variable (see below)
A comparison study of scientific paper summarization found:
- GPT-5.2 Thinking: Greater factual fidelity and scientific caution, preserving qualifiers
and granular results - Gemini 3 Pro: Introduced “hallucination-like behavior” and “concrete factual errors,”
including unsupported claims and interpretive drift
Mitigation Strategies
To reduce hallucinations:
- Enable web access—GPT-5.2’s hallucination rate drops from 10.9% to under 1% with browsing
enabled - Use RAG—Retrieval-Augmented Generation grounds responses in your documents
- Request citations—Ask the model to cite sources; verify them
- Temperature settings—Lower temperature reduces creative hallucinations
Pricing and API Costs
Consumer Subscription Pricing
| Tier | Claude | ChatGPT (GPT-5.2) | Gemini |
|---|---|---|---|
| Free | Limited Haiku | GPT-5.2 Instant | Limited Gemini |
| Pro/Plus ($20/mo) | Full Sonnet 4.5 | GPT-5.2 Thinking | Gemini Advanced |
| Premium | Opus 4.5 Access | $200/mo (Pro) | N/A |
API Pricing (per million tokens)
Claude Models (Anthropic Pricing)
| Model | Input | Output |
|---|---|---|
| Claude Opus 4.5 | $5.00 | $25.00 |
| Claude Sonnet 4.5 | $3.00 | $15.00 |
| Claude Sonnet 4.5 (>200K) | $6.00 | $22.50 |
| Claude Haiku 3.5 | $0.80 | $4.00 |
Note: Claude Opus 4.5 is 66% cheaper than Opus 4.1 ($15 input / $75 output).
GPT-5.2 Models (OpenAI Pricing)
| Model | Input | Output | Cached Input |
|---|---|---|---|
| GPT-5.2 Pro | $21.00 | $168.00 | N/A |
| GPT-5.2 | $1.75 | $14.00 | N/A |
| GPT-5.1 | $1.25 | $10.00 | $0.125 |
| GPT-5 mini | $0.25 | $2.00 | $0.025 |
Cost Comparison for Common Tasks
Estimated cost for processing 100,000 tokens input / 10,000 tokens output:
| Model | Cost |
|---|---|
| GPT-5 mini | $0.045 |
| Claude Haiku 3.5 | $0.12 |
| GPT-5.2 | $0.315 |
| Claude Sonnet 4.5 | $0.45 |
| Claude Opus 4.5 | $0.75 |
| GPT-5.2 Pro | $3.78 |
Cost Optimization Strategies
- Use caching: GPT models offer 90% discounts on cached input tokens
- Batch processing: Claude Sonnet 4.5 batch API is $1.50/$7.50 (50% savings)
- Right-size your model: GPT-5 mini at $0.25/$2.00 handles many tasks
- Prompt caching: Claude offers write at $3.75/M, read at $0.30/M
Real Developer Experiences
The following insights are synthesized from Reddit, X (Twitter), GitHub discussions, and developer forums from
November-December 2025.
Common Developer Workflow
A pattern emerged from multiple discussions: using different models for different phases of work.
“I use GPT for planning and brainstorming, then Claude for implementation. GPT gives better strategic
insights; Claude writes better code.” — r/LocalLLaMA
“Opus 4.5 for planning and implementation… Gemini is good for making components, but Opus is better for
wiring them up.” — r/ClaudeAI
Model-Specific Observations
Claude Opus 4.5
Positive:
- Produces clean, maintainable code
- Handles “real repo issues” effectively
- Better for refactoring and architecture
- Fast and efficient, generates “light code”
- Low hallucination rates in specific scenarios
Negative:
- Some users report performance degradation after updates (“lobotomized”)
- May ignore explicit instructions in some cases
- Context collapse in very long conversations
- More expensive than GPT for coding agents
GPT-5.2
Positive:
- Significant improvement over 5.1 for coding
- Better adherence to specifications
- Superior for planning and deeper reasoning
- Consistently accurate (though sometimes slow in Thinking mode)
- Lower hallucination rates than previous versions
- Better VS Code integration
Negative:
- Some users “hate its writing style”
- GPT-5.2 Pro is expensive ($200/month consumer, premium API)
- Thinking mode can be slow
Gemini 3 Pro
Positive:
- “Warmer” and more human-like writing tone
- Game-changing 1M context for story writing
- Strong multimodal performance
- Good for creative and mixed-media workflows
Negative:
- Inconsistent in long-context interactions
- Can introduce issues into existing codebases
- Hallucinations reported, especially in legal/scientific contexts
- Some users report it performs worse than Gemini 2.5 Pro for coding
Writing Style Preferences
Writing style is subjective but matters for user experience:
- Gemini: Preferred for creative writing due to “warmer” and more human-like tone
- GPT-5.2: More formal and structured; aims for “warmer, more conversational tone” in 5.2
- Claude: “Warmer” style, especially as a learning partner; nudges towards answers rather
than providing them directly
The Verdict: Which Model for Which Task
Quick Reference
| Use Case | Best Model | Why |
|---|---|---|
| Software engineering (fixing bugs) | Claude Opus 4.5 | 80.9% SWE-bench, clean code |
| Building AI agents with tools | GPT-5.2 | 98.7% tool-calling accuracy |
| Mathematical reasoning | GPT-5.2 Thinking | 100% AIME 2025 |
| Image/video analysis | Gemini 3 Pro | Leads all multimodal benchmarks |
| UI/screen understanding | Gemini 3 Pro | 72.7% ScreenSpot-Pro |
| Long document analysis | GPT-5.2 or Claude | Best long-context retention |
| Creative writing | Gemini 3 Pro | Warmer tone, 1M context |
| Budget-conscious development | Claude Sonnet 4.5 | Good balance of cost/quality |
| Enterprise (maximum quality) | GPT-5.2 Pro | Highest benchmark scores |
The Multi-Model Approach
The most sophisticated users are not choosing one model—they’re using multiple models for different parts of
their workflow:
- Research/planning: GPT-5.2 (strategic insights, structured reasoning)
- Implementation: Claude Opus 4.5 (clean code, architecture)
- Visual tasks: Gemini 3 Pro (image/video analysis)
- Quick tasks: GPT-5 mini or Claude Haiku (cost efficiency)
The Honest Summary
There is no single “best” model in December 2025. The landscape is genuinely competitive:
- Claude Opus 4.5 leads coding benchmarks and produces the cleanest code
- GPT-5.2 leads mathematical reasoning and tool-calling, essential for agents
- Gemini 3 Pro leads multimodal and vision tasks by a significant margin
The AI monoculture is over. Welcome to the multi-model era.