Overall LLM Rankings: February 2026
Comprehensive ranking of the top large language models in February 2026, combining multiple benchmarks including reasoning, coding, knowledge, and multimodal capabilities.
The AI landscape in early 2026 is more competitive than ever. With multiple frontier labs shipping major updates in rapid succession, choosing the right model for your use case requires understanding how they stack up across a broad range of capabilities. This overall ranking combines performance across reasoning, coding, knowledge, multimodal understanding, and real-world usability to give you one comprehensive view of the state of the art.
The February 2026 Overall Rankings
| Rank | Model | Provider | MMLU-Pro | GPQA Diamond | SWE-Bench Verified | Chatbot Arena Elo | Price (Input/Output per 1M tokens) |
|---|---|---|---|---|---|---|---|
| 1 | GPT-5.2 Pro | OpenAI | 88.7% | 93.2% | 65.8% | 1402 | $10.00 / $30.00 |
| 2 | Claude Opus 4.6 | Anthropic | 88.2% | 89.0% | 72.5% | 1398 | $15.00 / $75.00 |
| 3 | Gemini 3 Pro | Google DeepMind | 89.8% | 87.5% | 63.2% | 1389 | $1.25 / $5.00 |
| 4 | Grok 4 Heavy | xAI | 86.4% | 88.9% | 61.0% | 1375 | $3.00 / $15.00 |
| 5 | DeepSeek V3.2-Speciale | DeepSeek | 85.9% | 85.3% | 77.8% | 1361 | $0.28 / $1.10 |
| 6 | Claude Opus 4.5 | Anthropic | 87.1% | 86.5% | 68.4% | 1370 | $12.00 / $60.00 |
| 7 | Qwen 3.5 | Alibaba | 84.6% | 82.1% | 62.5% | 1342 | $0.50 / $2.00 |
| 8 | GPT-5.2 | OpenAI | 86.3% | 88.0% | 58.2% | 1380 | $2.50 / $10.00 |
| 9 | Llama 4 Maverick | Meta | 83.2% | 78.5% | 55.8% | 1320 | Free (open-weight) |
| 10 | Mistral 3 | Mistral AI | 82.8% | 79.3% | 54.1% | 1315 | $1.00 / $3.00 |
What Makes These Rankings Different
There is no single benchmark that tells you which model is "best." A model that dominates graduate-level science questions (GPQA Diamond) might stumble on real-world software engineering tasks (SWE-Bench). One that wins human preference polls (Chatbot Arena) might cost ten times more than a competitor that scores nearly as well.
Our overall rankings weigh five major dimensions equally: knowledge breadth (MMLU-Pro), expert reasoning (GPQA Diamond), coding ability (SWE-Bench Verified), human preference (Chatbot Arena Elo), and cost-adjusted value. This gives a balanced picture rather than letting any single metric dominate.
The Top Tier Breakdown
GPT-5.2 Pro claims the top spot with the highest reasoning scores we have ever seen from a production model. Its 93.2% on GPQA Diamond and perfect AIME 2025 score reflect a genuine leap in mathematical and scientific reasoning. The tradeoff is price: at $10/$30 per million tokens, it is firmly positioned as a premium offering for tasks where accuracy justifies the cost.
Claude Opus 4.6 is a remarkably close second, and many developers will find it the better practical choice. It leads the pack on SWE-Bench Verified at 72.5%, making it the strongest coder among frontier models by a significant margin. Its strength on Humanity's Last Exam among frontier models also signals depth in novel problem-solving. The higher output token cost reflects its extended thinking capabilities.
Gemini 3 Pro deserves attention for its exceptional knowledge breadth. It tops our chart on MMLU-Pro at 89.8% and brings class-leading multimodal capabilities that do not appear in this table but matter enormously for production applications. At $1.25/$5.00 per million tokens, it offers arguably the best price-to-performance ratio among truly frontier models.
Grok 4 Heavy has made a dramatic entrance. Its 50% score on Humanity's Last Exam (the hardest AI benchmark ever constructed) is a standout achievement. The model excels at extended reasoning chains and has proven particularly strong on problems that require integrating knowledge across multiple domains.
DeepSeek V3.2-Speciale continues to be the story that reshapes the industry. Achieving near-frontier performance at roughly one-thirtieth the cost of GPT-5.2 Pro, it has forced every lab to reconsider pricing strategies. Its 77.8% on SWE-Bench Verified is the highest of any model, though it achieves this on the more structured verified subset rather than the open-ended SWE-Bench Pro.
Key Trends to Watch
The gap is narrowing. The difference between the number one and number ten models on MMLU-Pro is just 6.9 percentage points. Two years ago, that spread was closer to 25 points. Frontier capability is becoming a commodity.
Coding is the new differentiator. As knowledge benchmarks saturate, the ability to write, debug, and reason about real-world code has become the clearest separator between models. SWE-Bench scores show the widest variance among top models.
Open-weight models are competitive. Llama 4 Maverick and DeepSeek V3.2 both crack the top ten, and their free or near-free pricing means that for many applications, the best model is no longer locked behind an API paywall.
Pricing varies by 50x. The most expensive model on our list costs roughly fifty times more per token than the cheapest. For many applications, the marginal improvement does not justify the cost difference, which makes our cost-efficiency leaderboard an essential companion to these rankings.
How to Use These Rankings
No single ranking can tell you which model to use. Consider what matters most for your application: if you need the absolute best reasoning, GPT-5.2 Pro leads. If coding is your priority, Claude Opus 4.6 and DeepSeek V3.2-Speciale dominate. If you need multimodal understanding, Gemini 3 Pro is the clear choice. And if cost matters, DeepSeek V3.2 delivers remarkable performance at a fraction of the price.
We update these rankings monthly as new models and benchmark results emerge. Check back in March for the next edition.