Four frontier AI models dropped in late 2025, each claiming to be the best at something. Gemini 3 Pro topped leaderboards on November 18. Claude Opus 4.5 launched November 24 calling itself "the best coding model in the world." Claude Sonnet 4.5 had already staked that claim in September. And GLM-4.7 arrived December 22 as the dark horse open-source contender.

Based on verified benchmarks, technical specifications, and real-world performance data from December 2025, here's what these models actually do well—and where they fall short.

The Benchmark Reality Check (December 2025)

Intelligence Rankings (Artificial Analysis Intelligence Index):

• Gemini 3 Pro: 73 (highest overall)
• Claude Opus 4.5: 70 (tied with GPT-5.1)
• Claude Sonnet 4.5: 63
• GLM-4.7: Not independently rated

But aggregate scores hide the real story. Each model dominates specific domains while struggling in others.

Gemini 3 Pro: The Knowledge Heavyweight

Released: November 18, 2025
Context window: 1 million tokens
Pricing: $2/$12 per million input/output tokens

What Gemini 3 Pro actually dominates:

Gemini 3 Pro isn't trying to be a coding specialist. It's built for breadth and reasoning depth.

On Humanity's Last Exam (measuring PhD-level reasoning), Gemini 3 Pro scores 37.5% without tools—crushing GPT-5.1's 26.5% and Opus 4.5's 30.8%. With Deep Think mode enabled, it reaches 41%.

On GPQA Diamond (graduate-level science reasoning), it hits 91.9%. On ARC-AGI-2 (novel reasoning challenges), it achieves 31.1% base, 45.1% with Deep Think—the highest score recorded.

Mathematical reasoning? Gemini 3 Pro scored 23.4% on MathArena Apex, over 20x better than previous top models on this difficult benchmark.

The multimodal performance is equally strong: 81% on MMMU-Pro, 87.6% on Video-MMMU. It processes video, images, audio, and text seamlessly across its 1 million token context window.

Where Gemini 3 Pro falls short:

Coding. On SWE-bench Verified (the gold standard for real-world software engineering), Gemini 3 Pro scores 76.2%. That's respectable but trails both Claude models significantly.

On LiveCodeBench, it lags behind specialized coding models. On OSWorld (computer use), it doesn't report results at all—Claude dominates this benchmark unchallenged.

Best for: Research, complex reasoning, multimodal analysis, knowledge-intensive tasks, video understanding, tasks requiring massive context windows

Claude Opus 4.5: The Agentic Powerhouse

Released: November 24, 2025
Context window: 200K tokens (1M experimental)
Pricing: $5/$25 per million input/output tokens

What Claude Opus 4.5 actually dominates:

Opus 4.5 is built for one thing: getting work done autonomously over extended periods.

On SWE-bench Verified, it scores 80.9%—the highest of any model tested. It beats Gemini 3 Pro (76.2%), GPT-5.1 (77.9%), and its sibling Sonnet 4.5 (77.2%).

On Terminal-Bench Hard, which measures command-line mastery and multi-step terminal workflows, Opus 4.5 achieves a substantial lead. For τ²-Bench (tool use across retail, airline, telecom scenarios), Opus 4.5 dominates, especially in Telecom where it approaches perfect scores.

The MCP Atlas benchmark (tool orchestration) shows Opus 4.5 at 62.3% vs 43.8% for the next best model. OSWorld (computer use—clicking, filling forms, navigating) shows Opus 4.5 as "the best model in the world" for this task according to Anthropic.

What makes Opus 4.5 different is sustained performance. Internal testing shows it maintains focus and performance on complex, multi-step tasks for over 30 hours. It doesn't just start strong—it finishes.

Where Claude Opus 4.5 falls short:

Pure knowledge and reasoning breadth. On Humanity's Last Exam, it scores 30.8%/43.2% (without/with tools)—behind Gemini 3 Pro's 37.5%/45.8%.

On GPQA Diamond, it doesn't match Gemini's depth. And while its coding scores are excellent, there's a

catch: it uses 60% more tokens than its predecessor to achieve these results. The headline price drop (66% cheaper than Opus 4.1) doesn't translate to 66% cheaper at runtime.

Best for: Autonomous agents, long-horizon coding tasks, complex multi-step workflows, production coding, computer automation, tasks requiring sustained focus

Claude Sonnet 4.5: The Balanced Workhorse

Released: September 29, 2025
Context window: 200K tokens
Pricing: $3/$15 per million input/output tokens

What Claude Sonnet 4.5 actually dominates:

Sonnet 4.5 is the middle child that doesn't get enough credit. It scores 77.2% on SWE-bench Verified (82% with parallel compute)—higher than Gemini 3 Pro and very close to Opus 4.5, at 40% of the cost.

On Terminal-Bench, it hits 50%—ahead of GPT-5 (43.8%) and substantially ahead of earlier models. On AIME 2025 (advanced high school math competition), it achieves 100% with Python tools, 87% without—matching or beating much more expensive models.

The tool use scores are strong across the board: 86.2% on τ²-Bench Retail, 70% on Airline, 98% on Telecom. On OSWorld, it reaches 61.4%—no other model reports comparable results.

What makes Sonnet 4.5 compelling is reliability. Users report it "just works" for complex coding tasks. Error rates on code editing benchmarks dropped from 9% (Sonnet 4) to 0% for Sonnet 4.5 according to Replit's internal testing.

Where Claude Sonnet 4.5 falls short:

It's not the absolute best at anything. Opus 4.5 beats it on coding. Gemini 3 Pro beats it on reasoning breadth. But being second-best at multiple things while costing significantly less is its own advantage.

Security analysis from SonarQube shows Sonnet 4.5 produces 198 blocker-severity vulnerabilities per million lines of code—higher than Opus 4.5 Thinking (44 per MLOC). Resource management leaks are also more common (195 per MLOC vs GPT-5.1's 51).

Best for: Production coding at scale, cost-sensitive workflows, balanced performance across reasoning and coding, teams that need "good enough at everything"

GLM-4.7: The Open-Source Challenger

Released: December 22, 2025
Context window: 200K tokens
Pricing: ~$0.60/$2.20 per million tokens (API), plus open weights

What GLM-4.7 actually dominates:

GLM-4.7 is the wild card. It's open-source, runs locally, costs a fraction of proprietary models, and on some benchmarks, beats them all.

On AIME 2025, GLM-4.7 scores 95.7%—higher than Gemini 3 Pro (95%) and GPT-5.1 (94%). On LiveCodeBench V6, it reaches 84.9%—surpassing Claude Sonnet 4.5.

On SWE-bench Verified, it achieves 73.8%. On SWE-bench Multilingual, it hits 66.7% (+12.9% improvement over GLM-4.6). On Terminal Bench 2.0, it scores 41% (+16.5% improvement).

The tool use benchmarks show even stronger results: 84.7% on τ²-Bench (surpassing Claude Sonnet 4.5), 67% on BrowseComp web tasks.

What makes GLM-4.7 unique is "Vibe Coding"—it generates noticeably cleaner, more modern UI code than competitors. Developers report fewer broken layouts, better spacing, and slides that don't look AI-generated.

The model introduces three thinking modes:

• Interleaved Thinking: Reasons before every action
• Preserved Thinking: Maintains reasoning context across turns
• Turn-level Thinking: Toggle reasoning on/off per request for speed vs accuracy

And it's open. Weights available on HuggingFace and ModelScope. Runs on vLLM and SGLang. Integrates with Claude Code, Cline, Roo Code, Kilo Code.

Where GLM-4.7 falls short:

Humanity's Last Exam: 24.8% without tools, 42.8% with tools. Competitive but not leading.

It lacks the massive context window of Gemini 3 Pro (200K vs 1M). It doesn't have Gemini's multimodal video understanding. It trails both Claude models on OSWorld computer use benchmarks.

And it's newer—less battle-tested in production than models from Anthropic and Google.

Best for: Cost-sensitive production deployments, teams that need local deployment, UI/frontend generation, multilingual coding, developers who want model control without vendor lock-in

The Direct Comparisons That Matter

Coding (SWE-bench Verified):

1. Claude Opus 4.5: 80.9%
2. Claude Sonnet 4.5: 77.2%
3. Gemini 3 Pro: 76.2%
4. GLM-4.7: 73.8%

PhD-Level Reasoning (GPQA Diamond):

1. Gemini 3 Pro: 91.9%
2. Claude Sonnet 4.5: 83.4%
3. Claude Opus 4.5: Not separately reported
4. GLM-4.7: Not separately reported

Advanced Math Competition (AIME 2025 with tools):

1. Claude Sonnet 4.5: 100%
2. GLM-4.7: 95.7%
3. Gemini 3 Pro: 95%
4. Claude Opus 4.5: Not reported

Tool Use - Complex Workflows (τ²-Bench Telecom):

1. Claude Sonnet 4.5: 98%
2. GLM-4.7: 84.7%
3. Claude Opus 4.5: Not separately reported
4. Gemini 3 Pro: Not reported

Computer Use (OSWorld):

1. Claude models dominate (61.4% for Sonnet 4.5)
2. Gemini 3 Pro: No results reported
3. GLM-4.7: No results reported

The Pricing Reality

Per million tokens (input/output):

• Gemini 3 Pro: $2/$12
• GLM-4.7: $0.60/$2.20
• Claude Sonnet 4.5: $3/$15
• Claude Opus 4.5: $5/$25

But pricing isn't just about tokens. Opus 4.5 uses 60% more tokens to complete tasks than Sonnet 4.5. Gemini 3 Pro's 1M context window means you can process more in one request. GLM-4.7's speed on specialized hardware (1,000+ tokens/second on Cerebras) changes cost calculations entirely.

The real cost calculation: (tokens used × price per token) + (developer time saved) - (errors introduced).

Bottom Line: Which Model for What

The honest answer: you probably need more than one.

Use Gemini 3 Pro when:

• You need to process video or massive documents
• The task requires PhD-level reasoning
• Knowledge breadth matters more than code execution
• Multimodal understanding is critical
• You're doing deep research, not production coding

Use Claude Opus 4.5 when:

• You're building autonomous agents
• Tasks require sustained focus beyond 30 minutes
• Computer automation is the goal
• Cost isn't the primary constraint
• You need the absolute best at complex coding

Use Claude Sonnet 4.5 when:

• You need strong coding at reasonable cost
• Balanced performance across tasks matters
• You're shipping production code at scale
• Tool use and reliability are critical
• You want proven, battle-tested performance

Use GLM-4.7 when:

• Budget is constrained
• Local deployment is required
• UI/frontend generation is important
• You work with non-English codebases
• You want model ownership without vendor dependency

The four models represent different design philosophies. Gemini 3 Pro optimized for breadth and reasoning. Opus 4.5 optimized for autonomous completion. Sonnet 4.5 optimized for balanced production use. GLM-4.7 optimized for accessibility and cost.

None is "best." Each is best at specific things. The question isn't which model wins—it's which combination of models solves your actual problems.

Test them on your workflows. Measure what matters to you. Build hybrid systems that use each model where it's strongest. The benchmark wars aremarketing. Your production results are reality.