Claude Opus 4.6 leads three of eight major benchmarks while OpenAI's o3 dominates math reasoning. We break down MMLU, HumanEval, SWE-bench, and five more tests with full scores and pricing.
Claude Opus 4.6 leads three out of eight major LLM benchmarks. OpenAI's o3 leads four. And yet, calling either one "the best AI model of 2026" would be misleading. The full LLM benchmark comparison for 2026 tells a more complicated story, one where the right model depends entirely on what you're building.
Which LLM has the best benchmark scores in 2026? Based on data from Papers with Code, SWE-bench, LMSYS Chatbot Arena, and ARC Prize, Claude Opus 4.6 leads in three out of eight major benchmarks (MMLU, HumanEval, and SWE-bench Verified), while OpenAI's o3 dominates math and reasoning tasks (MATH, GPQA Diamond, GSM8K, and ARC-AGI). GPT-4o leads in user preference (LMSYS Chatbot Arena). No single model wins everything.
All benchmark scores referenced in this article come from publicly reported results on Papers with Code, SWE-bench, LMSYS Chatbot Arena, and ARC Prize. We're aggregating the best available data, not running proprietary evals. Note: benchmark scores can vary significantly depending on evaluation methodology (e.g., with or without extended thinking/chain-of-thought, few-shot vs. zero-shot, specific benchmark version). Scores listed here are from publicly reported results and may differ from other sources. Importantly, models like o3 use extended chain-of-thought reasoning by default, which dramatically boosts scores on reasoning benchmarks. Claude Opus 4.6 scores listed here are without extended thinking enabled — with it, scores on MATH and GPQA Diamond would be significantly higher. Keep this asymmetry in mind when comparing.
Different benchmarks test very different capabilities. MMLU measures broad knowledge across dozens of academic subjects. HumanEval and SWE-bench test coding ability at different levels of complexity. MATH and GSM8K evaluate mathematical reasoning. GPQA Diamond probes graduate-level science. ARC-AGI attempts to measure genuine novel reasoning, the kind that can't be memorized. And LMSYS Chatbot Arena captures real user preferences through blind head-to-head votes.
That range matters. A model that aces one category can completely fall apart in another.
| Model | Score |
|---|---|
| Claude Opus 4.6 | 92.3% |
| Gemini 2.5 Pro | N/A |
| DeepSeek R1 | 90.8% |
| Claude Sonnet 4.6 | 89.5% |
| GPT-4o | 88.7% |
Claude Opus 4.6 leads by 1.5 points. That's a meaningful gap at this performance level, where top models typically cluster within a couple of percentage points. GPT-4o sitting last among these five is a bit surprising.
| Model | Score |
|---|---|
| Claude Opus 4.6 | 93.7% |
| GPT-4o | 90.2% |
| DeepSeek V3 | 89.8% |
| Gemini 2.5 Pro | N/A |
| Claude Sonnet 4.6 | 88.0% |
Pretty clear-cut. Claude Opus 4.6 crushes HumanEval with a 3.5-point lead over GPT-4o. For developers choosing a model for code generation, this benchmark (along with SWE-bench) matters most.
| Model | Score |
|---|---|
| o3 | 96.7% |
| o1 | 94.8% |
| Claude Opus 4.6 | 85.1% |
| Gemini 2.5 Pro | N/A |
| DeepSeek R1 | 83.5% |
And this is where OpenAI's reasoning-specialized models completely dominate. The o3 model scores 96.7%, a full 11.6 points ahead of Claude Opus 4.6. Not even close. If your primary use case involves complex mathematics, o3 is in a league of its own.
| Model | Score |
|---|---|
| o3 | 87.7% |
| o1 | 78.0% |
| Claude Opus 4.6 | 74.9% |
| Gemini 2.5 Pro | N/A |
| DeepSeek R1 | 71.5% |
Similar story. OpenAI's reasoning models lead, with o3 nearly 13 points ahead of Claude Opus 4.6. GPQA Diamond is genuinely difficult, testing PhD-level science questions. The o-series models' extended chain-of-thought approach gives them a massive advantage on this type of problem.
| Model | Score |
|---|---|
| Claude Opus 4.6 + Scaffold | 72.0% |
| o3 + Scaffold | 69.1% |
| Claude Sonnet 4.6 | 55.3% |
| GPT-4.1 | 54.6% |
| DeepSeek R1 | 49.2% |
SWE-bench Verified is arguably the most practical benchmark on this list. It tests whether models can actually fix real GitHub issues in real codebases. Claude Opus 4.6 leads at 72%, and even Claude Sonnet 4.6 outperforms GPT-4.1 without scaffolding. This tracks with Claude's growing reputation as the go-to for agentic coding workflows.
| Model | Elo |
|---|---|
| GPT-4o | 1287 |
| Claude Opus 4.6 | 1280 |
| Gemini 2.5 Pro | N/A |
| Grok 3 | 1268 |
| Claude Sonnet 4.6 | 1260 |
The Chatbot Arena tells a different story. When real users vote on which response they prefer in blind comparisons, GPT-4o edges ahead. But look at how tight this is: only 27 Elo points separate GPT-4o from Claude Sonnet 4.6. In practice, the top models are nearly indistinguishable to most users in casual conversation. Note: Elo scores are dynamic and change as users submit new votes.
| Model | Score |
|---|---|
| o3 | 99.2% |
| Claude Opus 4.6 | 97.8% |
| Gemini 2.5 Pro | N/A |
| GPT-4o | 95.8% |
| DeepSeek V3 | 95.0% |
GSM8K is essentially saturated. When five different models all score above 95%, the benchmark has done its job and we need harder tests. The o3's 99.2% is impressive, but the practical difference between these scores is minimal.
| Model | Score |
|---|---|
| o3 (high compute) | 87.5% |
| o3-mini | 77.0% |
| Claude Opus 4.6 | 53.0% |
| DeepSeek R1 | 42.0% |
| Gemini 2.5 Pro | N/A |
ARC-AGI is where things get really interesting. This benchmark tests the ability to solve novel visual reasoning puzzles, the kind that require genuine abstraction rather than pattern matching from training data. The o3 model at 87.5% with high compute obliterates everything else. Claude Opus 4.6's 53% is respectable but far behind. Most other frontier models haven't published official ARC-AGI scores yet, which limits direct comparison.
Three patterns jump out from this data.
Pattern 1: The reasoning gap is real. OpenAI's o-series models (o3, o1) are substantially better at mathematical and scientific reasoning. On MATH and GPQA Diamond, the gap between o3 and everything else is enormous. If you need a model for complex proofs or scientific problems, OpenAI has a clear lead that no competitor has closed yet.
Pattern 2: Claude dominates practical coding. Claude Opus 4.6 wins both HumanEval and SWE-bench Verified, the two benchmarks most directly relevant to software engineering. The SWE-bench result is especially telling because it measures the ability to understand real codebases, identify bugs, and write working fixes. This isn't toy code generation; it's actual engineering work.
The era of one model ruling every task is over. Smart teams are routing different workloads to different models based on capability and cost. Tools like Claude Code lean into this strength.
Pattern 3: General capability is converging. On MMLU, GSM8K, and Chatbot Arena, the gaps between top models keep shrinking. We're reaching a point where the "best" general-purpose LLM depends more on your specific use case than on any single leaderboard. (For a deeper look at why benchmarks alone can be misleading, see why AI benchmarks are broken.)
Benchmarks don't exist in a vacuum. Cost matters, especially at scale.
| Model | Context Window | Input (per MTok) | Output (per MTok) |
|---|---|---|---|
| Claude Opus 4.6 | 1M | $5.00 | $25.00 |
| Claude Sonnet 4.6 | 1M | $3.00 | $15.00 |
| Gemini 2.5 Pro | 1M | $1.25 | $10.00 |
| GPT-4o | 128K | $2.50 | $10.00 |
| Mistral Large 3 | 256K | $0.50 | $1.50 |
| Llama 4 Maverick | 1M | Open weights | Open weights |
GPT-4o is significantly cheaper per token than Claude Opus 4.6. But if Claude Opus solves your coding task in one shot where GPT-4o takes three attempts, the cheaper model ends up costing more. Effective cost per solved task is what actually matters, and raw token pricing can be misleading.
If Claude Opus solves your coding task in one shot where GPT-4o takes three attempts, the cheaper model ends up costing more.
Gemini 2.5 Pro, Claude Opus 4.6, and Llama 4 Maverick all offer 1-million-token context windows. For workloads that require processing entire codebases or very long documents, these three models lead the pack. And Llama 4 Maverick, with open weights and a 1-million-token context, offers the best economics if you're willing to self-host.
So the pricing question really becomes: what are you optimizing for? Lowest cost per token, lowest cost per completed task, or maximum capability regardless of price? Each answer points to a different model.
DeepSeek is punching above its weight. DeepSeek R1 and V3 show up consistently in the top five across multiple benchmarks. DeepSeek R1's 90.8% on MMLU puts it within striking distance of Claude Opus 4.6's 92.3%, which is remarkable for an open-weight model.
Grok 3 is a real contender. Appearing at 1268 Elo in Chatbot Arena puts xAI's model in serious company. A year ago, most people wouldn't have predicted Grok would be competitive with GPT-4o and Claude Opus.
ARC-AGI exposes a huge gap. The o3 model at 87.5% is in a class of its own on novel reasoning. Claude Opus 4.6's 53% and DeepSeek R1's 42% show just how far ahead o3 is on this frontier. Novel reasoning ability is clearly the next battleground, and most models still struggle badly with it.
The "best model" depends entirely on your task. Need math reasoning? Use o3. Need coding help? Claude Opus 4.6. Want the cheapest option that's still excellent? GPT-4o. Curious whether a budget GPU can compete? A $500 GPU beat Claude Sonnet at coding tasks. Processing massive documents? Gemini 2.5 Pro, Claude Opus 4.6, or Llama 4 Maverick (all 1M context). Want to run something locally? Llama 4 Maverick.
So who wins the 2026 LLM benchmark comparison? Nobody, and everybody. The era of one model ruling every task is over. Smart teams are routing different workloads to different models based on capability and cost. That's not a cop-out; it's the only honest conclusion the data supports.
Sources
For coding specifically, yes. Claude Opus 4.6 scores 93.7% on HumanEval vs GPT-4o's 90.2%, and leads SWE-bench Verified at 72% vs GPT-4.1's 54.6%. At $5 per million input tokens vs GPT-4o's $2.50, it's twice the price per token, but higher first-pass success rates often mean fewer retries and lower total cost per completed task. If coding is your primary use case, the premium pays for itself.
Llama 4 Maverick is open-weight and free to download, but running it requires significant hardware. With its large parameter count, you'll need multiple high-end GPUs for local inference. Cloud hosting through providers like Together AI or Fireworks AI is cheaper than proprietary APIs, but not free. For light usage, hosted APIs with free tiers may be more practical than self-hosting.
Benchmarks are useful directional signals but have well-known limitations. Models can be specifically tuned to perform well on popular benchmarks without matching that performance on novel tasks. SWE-bench Verified and LMSYS Chatbot Arena are considered the most reliable indicators of practical ability because they test real-world software engineering and direct user preferences. Always test models on your own use case before committing.
The o3 model uses an extended chain-of-thought reasoning approach where it generates and evaluates multiple solution paths before producing a final answer. This process burns significantly more tokens (and costs more per query) than standard inference, but it produces dramatically better results on problems requiring multi-step logical reasoning. The tradeoff is higher latency and cost per request compared to models like GPT-4o or Claude Opus 4.6.
SWE-bench Verified. Unlike HumanEval, which tests isolated function generation, SWE-bench requires models to understand full repositories, diagnose bugs from issue descriptions, and write working patches. It's the closest proxy to how developers actually use AI coding assistants. Claude Opus 4.6 leads at 72%, followed by o3 at 69.1%. If you only check one benchmark before choosing a coding model, make it this one.
