What are the current top-performing AI models as of May 2026? Compare their benchmark scores across key metrics.
Answer
AI Model Benchmarks — May 2026
The AI model landscape in May 2026 is more competitive than ever. OpenAI, Anthropic, Google, xAI, and emerging Chinese labs (DeepSeek, Qwen, Kimi) all ship frontier models with distinct strengths. This guide compares the top models across intelligence, speed, price, and coding benchmarks.
Top Models by Intelligence
The Artificial Analysis Intelligence Index aggregates multiple benchmarks into a single score (0–60 scale).
| Rank | Model | Company | Intelligence Score | Price (per 1M tokens) |
|---|---|---|---|---|
| 1 | GPT-5.5 (xhigh) | OpenAI | 60 | $11.25 |
| 2 | GPT-5.5 (high) | OpenAI | 59 | $11.25 |
| 3 | Claude Opus 4.7 (max) | Anthropic | 57 | $10.94 |
| 4 | Gemini 3.1 Pro Preview | 57 | $4.50 | |
| 5 | GPT-5.5 (medium) | OpenAI | 57 | $11.25 |
| 6 | Kimi K2.6 | Moonshot AI | 54 | $1.71 |
| 7 | MiMo-V2.5-Pro | Xiaomi | 54 | $1.50 |
| 8 | GPT-5.3 Codex (xhigh) | OpenAI | 54 | $4.81 |
| 9 | Grok 4.3 | xAI | 53 | $1.56 |
| 10 | Muse Spark | Meta | 52 | — |
Key insight: GPT-5.5 leads raw intelligence, but Gemini 3.1 Pro offers near-identical performance at less than half the price.
Chatbot Arena Rankings (Human Preference)
The LMSYS Chatbot Arena uses blind head-to-head comparisons where humans vote on which response they prefer. This measures real-world usefulness, not just benchmark scores.
| Rank | Model | Company | Arena Score |
|---|---|---|---|
| 1 | Claude Opus 4.7 (thinking) | Anthropic | 1503 |
| 2 | Claude Opus 4.6 (thinking) | Anthropic | 1502 |
| 3 | Claude Opus 4.6 | Anthropic | 1497 |
| 4 | Gemini 3.1 Pro Preview | 1493 | |
| 5 | Claude Opus 4.7 | Anthropic | 1491 |
| 6 | Muse Spark | Meta | 1491 |
| 7 | GPT-5.5 (high) | OpenAI | 1488 |
| 8 | Gemini 3 Pro | 1486 | |
| 9 | Grok 4.20 beta1 | xAI | 1480 |
| 10 | Grok 4.20 reasoning | xAI | 1477 |
Key insight: Anthropic dominates human preference rankings — Claude models hold 5 of the top 6 spots.
Top Models by Speed
Speed matters for real-time applications, autocomplete, and agentic workflows.
| Rank | Model | Company | Speed (tokens/sec) |
|---|---|---|---|
| 1 | Mercury 2 | Inception | 902 |
| 2 | gpt-oss-120B | OpenAI | 234 |
| 3 | NVIDIA Nemotron 3 Super | NVIDIA | 216 |
| 4 | Qwen3.6 35B A3B | Alibaba | 198 |
| 5 | Gemini 3.1 Flash-Lite | 347 | |
| 6 | Gemini 3 Flash | 183–188 | |
| 7 | Mistral Medium 3.5 | Mistral | 173 |
| 8 | Step 3.5 Flash | StepFun | 171 |
| 9 | Qwen3.5 35B A3B | Alibaba | 168 |
| 10 | GPT-5.4 nano | OpenAI | 154–160 |
Top Models by Price (Cheapest per 1M Tokens)
| Model | Company | Price (per 1M tokens) | Intelligence Score |
|---|---|---|---|
| Qwen3.5 0.8B | Alibaba | ~$0.02 | Low |
| Gemma 3n E4B | ~$0.05 | Low | |
| MiMo-V2-Flash | Xiaomi | $0.15 | 30–41 |
| DeepSeek V4 Flash | DeepSeek | $0.18 | 36–47 |
| gpt-oss-120B | OpenAI | $0.26 | 33 |
| Grok 4.1 Fast | xAI | $0.28 | 39 |
| Mistral Small 4 | Mistral | $0.26 | 28 |
| MiniMax-M2.7 | MiniMax | $0.52 | 50 |
Best value: DeepSeek V4 Flash (0.15/1M) offer strong performance at rock-bottom prices.
Top Coding Models
Based on Chatbot Arena Code rankings and industry benchmarks:
| Rank | Model | Company | Code Arena Score | Best For |
|---|---|---|---|---|
| 1 | Claude Opus 4.7 (thinking) | Anthropic | 1571 | Complex architecture, debugging |
| 2 | Claude Opus 4.7 | Anthropic | 1561 | Agentic coding, multi-file edits |
| 3 | Claude Opus 4.6 (thinking) | Anthropic | 1548 | Long-context code reasoning |
| 4 | Claude Opus 4.6 | Anthropic | 1543 | Production code generation |
| 5 | GLM-5.1 | Zhipu AI | 1534 | General coding |
| 6 | Claude Sonnet 4.6 | Anthropic | 1527 | Daily coding (best balance) |
| 7 | Kimi K2.6 | Moonshot AI | 1526 | Cost-effective coding |
| 8 | Muse Spark | Meta | 1509 | Open-source coding |
| 9 | GPT-5.5 (high) | OpenAI | 1492 | Codex integration |
| 10 | Claude Opus 4.5 (thinking) | Anthropic | 1491 | Legacy code understanding |
Coding Model Recommendations
| Use Case | Recommended Model | Why |
|---|---|---|
| Daily coding | Claude Sonnet 4.6 | Best quality/cost balance |
| Complex debugging | Claude Opus 4.7 (thinking) | Deep reasoning on large codebases |
| Fast autocomplete | GPT-5.4 mini or Gemini 3 Flash | Low latency for inline suggestions |
| Budget coding | DeepSeek V4 Flash ($0.18/1M) | Strong coding at lowest cost |
| Open-source local | Qwen3.5 397B or Llama 4 Scout | Run on your own hardware |
Average Tokens for Day-to-Day Coding
How many tokens does a typical developer consume per day with AI assistance?
| Activity | Tokens per Interaction | Interactions/Day | Daily Total |
|---|---|---|---|
| Code autocomplete | 50–200 tokens | 100–300 | 5K–60K |
| Chat questions | 500–2K tokens | 10–30 | 5K–60K |
| Code review | 1K–5K tokens | 5–10 | 5K–50K |
| Debugging sessions | 2K–10K tokens | 3–5 | 6K–50K |
| Architecture planning | 3K–15K tokens | 1–3 | 3K–45K |
Daily Token Budget Estimates
| Developer Type | Estimated Daily Tokens | Monthly Tokens |
|---|---|---|
| Light user (autocomplete only) | 10K–30K | 200K–600K |
| Moderate user (chat + autocomplete) | 50K–150K | 1M–3M |
| Heavy user (full AI-assisted dev) | 150K–500K | 3M–10M |
| Power user (agentic workflows) | 500K–2M | 10M–40M |
Cost Estimates (at $3/1M tokens average)
| Usage Level | Monthly Tokens | Monthly Cost |
|---|---|---|
| Light | 200K–600K | 1.80 |
| Moderate | 1M–3M | 9 |
| Heavy | 3M–10M | 30 |
| Power | 10M–40M | 120 |
Practical tip: Most developers are well-served by 50K–150K tokens/day (1M–3M/month). This covers autocomplete, chat questions, and occasional code review. Budget-conscious developers can use cheaper models like DeepSeek V4 Flash for routine tasks and reserve frontier models for complex problems.
Category Leaders
| Category | Best Model | Runner-Up |
|---|---|---|
| Overall Intelligence | GPT-5.5 (xhigh) | Claude Opus 4.7 (max) |
| Human Preference | Claude Opus 4.7 (thinking) | Claude Opus 4.6 (thinking) |
| Coding | Claude Opus 4.7 (thinking) | Claude Opus 4.7 |
| Vision | Claude Opus 4.6 (thinking) | Claude Opus 4.7 (thinking) |
| Speed | Mercury 2 (902 tok/s) | Gemini 3.1 Flash-Lite (347 tok/s) |
| Best Value | Gemini 3.1 Pro Preview | DeepSeek V4 Pro |
| Cheapest | Qwen3.5 0.8B | MiMo-V2-Flash |
| Longest Context | Llama 4 Scout / Grok 4.1 Fast (2M) | Gemini 2.0 Pro (1M+) |
| Open Source | Muse Spark (Meta) | Qwen3.5 397B (Alibaba) |
Key Takeaways
- No single "best" model — choice depends on your use case, budget, and latency requirements
- Anthropic dominates coding — Claude holds 7 of the top 10 coding spots
- OpenAI leads raw intelligence — GPT-5.5 (xhigh) scores highest on benchmarks
- Google offers best value — Gemini 3.1 Pro matches top models at half the price
- Chinese models are competitive — DeepSeek, Qwen, and Kimi offer strong performance at 10–50× lower cost
- Context windows are massive — 1M+ tokens is now standard for frontier models
- Speed varies wildly — from 900+ tokens/sec (Mercury) to 30 tokens/sec (reasoning models)
- Budget 50K–150K tokens/day for typical AI-assisted development
Interview tip: When discussing model selection, emphasize the trade-offs between intelligence, speed, cost, and specific capabilities (coding, vision, reasoning). There's no universal winner — the best choice depends on the specific use case.
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