AI Model Benchmarks 2026: MMLU, HumanEval, MATH & Arena Elo Scores Compared
We compiled benchmark scores for all 34 LLMs across 4 major benchmarks. Here's which models actually perform best — and which give you the most capability per dollar.
Choosing an AI model based solely on pricing is like choosing a car based on gas mileage alone — you might save at the pump, but you could end up with a vehicle that can't handle the highway. Benchmark scores tell you what a model can actually do.
We analyzed benchmark data for 34 LLMs across 10 providers, covering MMLU, HumanEval, MATH, and Arena Elo. Here's what we found.
The Top 10 Models by Composite Score
Our composite score weights MMLU, HumanEval, and MATH at 60% (equally) and Arena Elo at 40% — reflecting that real-world conversational quality matters as much as raw capability.
| # | Model | Provider | MMLU | HumanEval | MATH | Arena Elo | Composite | Cost/1M avg |
|---|---|---|---|---|---|---|---|---|
| 1 | GPT-5.5 Pro | OpenAI | 96.5 | 96.8 | 98.1 | 1405 | 96.2 | $105.00 |
| 2 | GPT-5.5 | OpenAI | 94.8 | 94.2 | 96.2 | 1380 | 93.8 | $17.50 |
| 3 | Claude Opus 4.8 | Anthropic | 94.2 | 95.8 | 93.5 | 1370 | 93.2 | $15.00 |
| 4 | Claude Opus 4.7 | Anthropic | 93.5 | 94.5 | 92.0 | 1355 | 92.1 | $15.00 |
| 5 | GPT-5 | OpenAI | 92.1 | 91.0 | 92.5 | 1350 | 91.0 | $5.63 |
| 6 | Gemini 3.1 Pro | 91.8 | 88.5 | 91.2 | 1340 | 89.8 | $7.00 | |
| 7 | Claude Sonnet 4.6 | Anthropic | 90.5 | 90.2 | 87.8 | 1325 | 88.6 | $9.00 |
| 8 | GPT-5.3 Codex | OpenAI | 90.2 | 93.5 | 88.4 | 1320 | 88.5 | $7.88 |
| 9 | GPT-4o | OpenAI | 88.7 | 87.2 | 84.0 | 1310 | 86.2 | $6.25 |
| 10 | Gemini 2.5 Pro | 89.2 | 85.0 | 86.8 | 1300 | 85.8 | $5.63 |
Key insight: GPT-5.5 Pro dominates every benchmark but costs $105/1M tokens average. GPT-5 ($5.63 avg) delivers 94% of the composite score at 5% of the cost. For most use cases, GPT-5 or Claude Opus 4.8 is the sweet spot.
Benchmark Breakdown: What Each Score Means
MMLU — General Knowledge
MMLU (Massive Multitask Language Understanding) tests knowledge across 57 subjects: math, science, law, history, ethics, and more. It's the most widely cited benchmark for general AI capability.
What the scores mean:
- 94+: Frontier territory. GPT-5.5, Claude Opus 4.8, GPT-5. Only needed for expert-level knowledge tasks.
- 88-94: Production-ready. GPT-5, Gemini 3.1 Pro, Claude Sonnet 4.6. Handles most real-world tasks well.
- 80-88: Solid mid-tier. DeepSeek V4 Pro, Mistral Large 3, Llama 4 Maverick. Good for chatbots, content, and standard queries.
- 65-80: Budget tier. Fine for simple tasks, FAQs, and data extraction where deep knowledge isn't critical.
HumanEval — Code Generation
HumanEval measures a model's ability to generate correct Python functions from docstring descriptions. It's the standard benchmark for coding capability.
Top 5 for code:
- GPT-5.5 Pro: 96.8 — Near-perfect code generation
- Claude Opus 4.8: 95.8 — Best code quality among non-OpenAI models
- GPT-5.5: 94.2 — Excellent at complex functions
- GPT-5.3 Codex: 93.5 — Purpose-built for code, strong value
- Claude Opus 4.7: 94.5 — Consistent code generation
For developers: If code quality is your priority, Claude Opus 4.8 ($5/$25 per 1M tokens) outperforms GPT-5 ($1.25/$10) on HumanEval. But GPT-5 costs 66% less. For code assistants and autocomplete, GPT-5 mini or DeepSeek V4 Pro offer better value.
MATH — Mathematical Reasoning
The MATH benchmark tests ability to solve competition-level math problems. Strong MATH scores correlate with better reasoning on complex, multi-step tasks.
Standout: GPT-5.5 Pro scores 98.1 on MATH — the highest of any model. But at $105/1M tokens, it's 19x more expensive than GPT-5 (92.5 MATH). For most math-heavy workloads, GPT-5 or Gemini 3.1 Pro (91.2) offer 95%+ of the capability at a fraction of the cost.
Arena Elo — Human Preference
Arena Elo is the most "real-world" benchmark. It's an ELO rating from blind human comparisons in the Chatbot Arena — people use both models and pick the one they prefer. Higher ELO = more humans prefer this model's responses.
Key takeaway: Arena Elo captures things benchmarks miss — helpfulness, tone, instruction-following, and overall user experience. A model with a slightly lower MMLU but higher Arena Elo may actually be better for your users.
Budget Models That Punch Above Their Weight
Not every task needs a frontier model. Here are the budget models with the best benchmark scores per dollar:
| Model | MMLU | HumanEval | Composite | Cost/1M avg | Value Ratio |
|---|---|---|---|---|---|
| DeepSeek V4 Pro | 88.2 | 86.5 | 84.5 | $0.65 | 130x |
| Mistral Large 3 | 85.5 | 82.8 | 81.2 | $1.00 | 81x |
| DeepSeek V4 Flash | 81.0 | 74.0 | 75.8 | $0.21 | 361x |
| Gemini 2.0 Flash | 82.5 | 76.0 | 77.2 | $0.25 | 309x |
| Llama 4 Maverick | 85.0 | 81.2 | 80.5 | $0.56 | 144x |
Value Ratio = Composite Score / Average Cost (higher = more capability per dollar). DeepSeek V4 Flash delivers 361x more capability per dollar than the average frontier model.
Choosing the Right Model: A Decision Framework
Step 1: Set your quality floor
What minimum benchmark score does your use case require? For production chatbots, we recommend 85+ MMLU and 80+ Arena Elo. For code generation, aim for 85+ HumanEval.
Step 2: Filter by your priority benchmark
If you're building a code assistant, sort by HumanEval. For a knowledge base, sort by MMLU. For a math tutor, sort by MATH. For a chatbot, sort by Arena Elo.
Step 3: Factor in cost
Among models above your quality floor, the cheapest one is usually the right choice. Use our benchmark comparison tool to see side-by-side scores and costs.
Step 4: Test with your actual workload
Benchmarks are a starting point, not a guarantee. Send 50-100 real prompts to your top 2-3 candidates and evaluate the output quality yourself.
Provider Strengths by Benchmark
- OpenAI: Dominates MATH (96-98 on flagship models) and has the highest Arena Elo scores. Best for reasoning-heavy and math-intensive tasks.
- Anthropic: Leads HumanEval (95.8 on Opus 4.8) — best for code generation and technical writing. Strong Arena Elo, indicating high conversational quality.
- Google: Gemini 3.1 Pro offers balanced scores across all benchmarks with the largest context window (1M tokens). Best for long-document analysis.
- DeepSeek: Best benchmark-to-cost ratio. V4 Pro scores 88.2 MMLU at $0.65/1M — 10-15x cheaper than comparable models.
- Meta (Together.ai): Llama 4 Maverick (85 MMLU) at $0.56/1M is the best open-source option. Good for self-hosting and custom fine-tuning.
The Bottom Line
Benchmark scores are the best objective measure we have for comparing AI models, but they're not the only factor. Consider:
- Cost: A model that's 5% better but 10x more expensive may not be worth it
- Context window: Gemini's 1M context vs GPT-5's 272K matters for long documents
- Rate limits: Some providers throttle at high volumes
- Latency: Budget models are often 2-5x faster
- Reliability: Uptime and consistency matter for production
Use our interactive benchmark comparison tool to explore all 34 models, compare scores side-by-side, and find the best model for your specific needs.
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