Frontier AI Benchmarks & Performance (April 2026)¶
Comprehensive benchmark data for comparing frontier models across reasoning, coding, multimodal, and real-world tasks.
The Artificial Analysis Intelligence Index¶
The most comprehensive model ranking as of April 2026.
Comparative ranking of major models (April 2026)
Top Models Ranked¶
| Rank | Model | Score | Release | Status |
|---|---|---|---|---|
| #1 | Gemini 3.1 Pro | 57.0 | Feb 19, 2026 | Active |
| #2 | GPT-5.4 Standard | 56.8 | Mar 5, 2026 | Active |
| #3 | Claude Opus 4.6 | 56.5 | Feb 5, 2026 | Active |
| #4 | Llama 4 Maverick | 54.2 | 2026 Q1 | Active |
| #5 | GPT-5.4 Mini | 53.1 | Mar 17, 2026 | Active |
| #6 | Llama 4 Scout | 52.8 | 2026 Q1 | Active |
| #7 | Claude Sonnet 4.6 | 55.8 | Feb 17, 2026 | Active |
Methodology: Weighted average of AIME 2025, SWE-bench, MMLU, MultiWoZ, and other benchmarks
Key insight: Within 4 points, all top models are frontier-class
Reasoning Benchmarks¶
AIME 2025 (Mathematical Reasoning)¶
| Model | Score | Percentile | Notes |
|---|---|---|---|
| Gemini 3.1 Pro | 95.6% | 99.8th | Math reasoning leader |
| Gemini 3.1 Thinking | 97.2% | 99.9th | With extended reasoning |
| GPT-5.4 Standard | 91.2% | 99.5th | Strong but behind |
| GPT-5.4 Thinking | 93.8% | 99.7th | Better with thinking |
| Claude Opus 4.6 | 89.4% | 99.2nd | Solid reasoning |
| Claude Sonnet 4.6 | 87.1% | 99.0th | Good reasoning |
| Llama 4 Maverick | 82.5% | 98.5th | Respectable |
AIME: 30 problems, advanced high school + olympiad math
Interpretation: Score > 50% is exceptional (human average ~30%)
Logic Reasoning¶
| Model | Logic Puzzles | Constraint Solving | Deduction | Average |
|---|---|---|---|---|
| Gemini 3.1 | 98% | 96% | 97% | 97% |
| Claude Opus | 96% | 94% | 96% | 95% |
| GPT-5.4 | 94% | 92% | 94% | 93% |
| Llama 4 | 88% | 86% | 88% | 87% |
Coding Benchmarks¶
SWE-bench Pro (Software Engineering)¶
| Model | Score | Interpretation | Viable? |
|---|---|---|---|
| Claude Opus 4.6 | 54% | Handles 5 of 10 real GitHub issues | ✅ Production |
| GPT-5.4 Standard | 57.7% | Solves real engineering problems | ✅ Production |
| GPT-5.4 Thinking | 62% | Better reasoning on hard issues | ✅ Strong |
| Gemini 3.1 | 56% | Competitive coding performance | ✅ Production |
| Claude Sonnet 4.6 | 51% | Good but not leading | ⚠️ Backup |
| GPT-5.4 Mini | 54.38% | 95% of Standard, huge cost savings | ✅ Recommended |
| Llama 4 Maverick | 48% | Respectable, free deployment | ✅ Budget |
SWE-bench: Real GitHub issues, must write code to fix them
Practical insight: > 50% means can realistically help developers
HumanEval (Programming)¶
| Model | Python | JavaScript | Java | C++ | Average |
|---|---|---|---|---|---|
| GPT-5.4 | 91% | 89% | 87% | 85% | 88% |
| Claude Opus | 89% | 88% | 85% | 83% | 86% |
| Gemini 3.1 | 90% | 88% | 86% | 84% | 87% |
| Llama 4 | 84% | 82% | 79% | 76% | 80% |
HumanEval: Simple programming tasks (120 problems)
All > 80%: All frontier models competent at coding
Knowledge & Facts (MMLU)¶
Multiple-choice knowledge across 57 disciplines¶
| Model | Score | Top Domains | Weak Domains |
|---|---|---|---|
| GPT-5.4 | 91% | Physics, CS, Law | Medicine |
| Claude Opus | 89% | Humanities, Law, Ethics | Medicine |
| Gemini 3.1 | 90% | Science, Math, CS | History |
| Llama 4 | 84% | General coverage | Medicine |
MMLU: Factual knowledge across domains
Interpretation: > 85% is "expert-level" knowledge
Instruction Following & Safety¶
Instruction Following Accuracy¶
| Model | Exact Match | Semantic Match | Notes |
|---|---|---|---|
| Claude Opus | 96% | 98% | Best at following instructions |
| Claude Sonnet | 94% | 97% | Very reliable |
| Gemini 3.1 | 93% | 96% | Good compliance |
| GPT-5.4 | 91% | 94% | Reliable but more creative |
Test: Can model follow detailed, complex instructions exactly?
Safety Benchmark¶
| Model | Harmful Content | Bias Detection | Privacy Respect | Truthfulness |
|---|---|---|---|---|
| Claude Opus | 99% | 98% | 99% | 96% |
| Gemini 3.1 | 98% | 97% | 98% | 95% |
| GPT-5.4 | 96% | 95% | 96% | 93% |
| Llama 4 | 94% | 92% | 94% | 91% |
Interpretation: All frontier models are quite safe, Claude leads
Agentic Capabilities¶
OSWorld (Computer Use)¶
| Model | Score | Interpretation | Notes |
|---|---|---|---|
| GPT-5.4 Standard | 75% | Near-human desktop navigation | Agentic leader |
| GPT-5.4 Mini | 62% | 82% of Standard, much cheaper | Good value |
| GPT-5.4 Thinking | 78% | Better reasoning on complex tasks | Best agentic |
| Claude Opus | 71% | Strong but not as focused | Good alternative |
| Gemini 3.1 | 68% | Not designed for automation | Weak point |
| Llama 4 | 55% | Limited desktop automation | Not agentic |
OSWorld: Tests ability to use desktop/browser autonomously (click, scroll, type)
Practical: > 70% means can reliably automate office tasks
Multimodal Benchmarks¶
Image Understanding¶
| Model | Spatial Reasoning | Visual QA | Scene Description | OCR |
|---|---|---|---|---|
| Gemini 3.1 | 98% | 95% | 96% | 99% |
| Llama 4 Maverick | 92% | 88% | 91% | 97% |
| Claude 4.6 | 90% | 87% | 89% | 96% |
| GPT-5.4 | N/A | N/A | N/A | N/A |
Note: GPT-5.4 has no vision (text-only)
Video Understanding¶
| Task | Gemini 3.1 | Llama 4 | Others |
|---|---|---|---|
| 45-min video summary | ✅ Excellent | ✅ Good | ❌ Cannot ingest |
| Action recognition | 97% | 94% | - |
| Temporal reasoning | 96% | 93% | - |
| Event detection | 95% | 91% | - |
Latency & Speed¶
Inference Latency (Tokens per Second)¶
| Model | Hardware | Tokens/s | Notes |
|---|---|---|---|
| GPT-5.4 Mini | API | 50-100 | Very fast |
| GPT-5.4 Standard | API | 30-60 | Fast |
| Claude Sonnet | API | 40-80 | Fast |
| Gemini 3.1 | API | 20-40 | Moderate (multimodal overhead) |
| Llama 4 Scout | H100 | 1-2 | Reasonable for 109B |
| Llama 4 Maverick | H100 | 1-2 | Same as Scout (17B active) |
| GPT-5.4 Thinking | API | 5-10 | Slow (reasoning phase) |
Context: Human typing ~8 tokens/second
End-to-End Latency¶
| Task | GPT-5.4 | Claude | Gemini | Llama 4 |
|---|---|---|---|---|
| Simple question | 200ms | 300ms | 400ms | 500ms |
| Code generation | 2s | 3s | 3s | 4s |
| Long analysis | 5-10s | 5-10s | 8-15s | 6-10s |
| With thinking | 5-15s | 5-15s | 10-20s | N/A |
Cost Analysis¶
Cost per 1M Tokens¶
| Model | Input $/M | Output $/M | Ratio |
|---|---|---|---|
| GPT-5.4 Mini | $0.15 | $0.60 | 1x (baseline) |
| Llama 4 | $0.00 | $0.00 | Free |
| Claude Haiku | $0.80 | $4.00 | 5x |
| GPT-5.4 Standard | $2.50 | $15.00 | 16x |
| Claude Sonnet | $3.00 | $15.00 | 20x |
| Gemini 3.1 | $3.50 | $14.00 | 23x |
| Claude Opus | $3.00 | $15.00 | 20x |
| GPT-5.4 Thinking | $5.00 | $30.00 | 33x |
Cost per High-Quality Output¶
Better metric: Cost to get reliable answer
| Model | Cost to Get 95% Correct | ROI |
|---|---|---|
| GPT-5.4 Standard | $0.50 | 1x |
| GPT-5.4 Mini | $0.30 | 1.7x |
| Claude Sonnet | $0.45 | 1.1x |
| Gemini 3.1 | $0.55 | 0.9x |
| Llama 4 | $0.00 | ∞ |
Benchmark Selection Guide¶
| Question | Best Benchmark | What It Tests |
|---|---|---|
| Best overall? | Artificial Analysis Intelligence Index | Aggregate reasoning |
| Best at math? | AIME 2025 | Mathematical thinking |
| Best at coding? | SWE-bench Pro | Real-world software |
| Best at facts? | MMLU | Knowledge retention |
| Best at automation? | OSWorld | Agentic behavior |
| Best at images? | Vision benchmarks | Spatial reasoning |
| Best at video? | Video benchmarks | Temporal reasoning |
How to Interpret Benchmarks¶
Score of 90% Means:¶
- ✅ State-of-the-art capability
- ✅ Exceeds most humans
- ✅ Reliable for production
- ✅ Can handle complex tasks
Score of 70-80% Means:¶
- ⚠️ Competent but not perfect
- ⚠️ Requires human review
- ⚠️ Good for assistance, not automation
- ⚠️ Cost-effective alternative
Score of 50-70% Means:¶
- ❌ Useful for brainstorming
- ❌ Not reliable for critical tasks
- ❌ Requires human validation
- ❌ Budget-conscious choice
Score < 50% Means:¶
- ❌ Limited capability
- ❌ Use only for simple tasks
- ❌ High error rate expected
- ❌ Not recommended for production
Benchmark Limitations¶
What benchmarks measure well: - ✅ Factual knowledge - ✅ Reasoning on closed-world problems - ✅ Code generation - ✅ Math ability - ✅ Following instructions
What benchmarks miss: - ❌ Creativity - ❌ Real-world robustness - ❌ Ethical judgment - ❌ User satisfaction - ❌ Edge cases - ❌ Long-term consistency
Practical insight: Benchmarks are guides, not gospel. Test on your use case.
Summary Table¶
| Capability | Best Model | Score | Alternative |
|---|---|---|---|
| Overall intelligence | Gemini 3.1 | 57.0 | GPT-5.4 (56.8) |
| Math/reasoning | Gemini 3.1 | 95.6% | GPT-5.4 Thinking (93.8%) |
| Coding | GPT-5.4 Std | 57.7% | GPT-5.4 Thinking (62%) |
| Knowledge | GPT-5.4 | 91% | Gemini (90%) |
| Safety | Claude Opus | 99% | Gemini (98%) |
| Agentic | GPT-5.4 Std | 75% | GPT-5.4 Thinking (78%) |
| Multimodal | Gemini 3.1 | #1 | Llama 4 (competitive) |
| Cost | Llama 4 | Free | GPT-5.4 Mini (0.15/M) |
Last Updated¶
April 8, 2026