LLM Comparison Matrix

How to Choose the Right LLM for Your Use Case

Choosing an AI model in 2026 means balancing four dimensions: intelligence (benchmark scores), cost (per-token pricing), capacity (context window), and constraints (licensing and data policies). No single model wins across all four. GPT-5.6 Sol leads in math benchmarks but costs $4 per million input tokens. DeepSeek R1 matches its math performance at $0.55 per million tokens but has a smaller context window and no multimodal support.

For high-volume production workloads where cost matters, open-source models like Llama 4 Maverick and DeepSeek V4 Pro offer strong performance at a fraction of proprietary pricing. For applications requiring the longest context, Llama 4 Scout's 10 million token window is unmatched. For coding tasks, Claude and GPT models consistently score highest on HumanEval benchmarks.

Understanding LLM Benchmark Scores

This comparison includes four widely used benchmarks. MMLU tests knowledge across 57 academic subjects from elementary mathematics to professional law. GPQA Diamond uses graduate-level questions written by domain experts, specifically designed to be difficult even for experts outside the specific field. HumanEval measures code generation by asking models to write Python functions that pass unit tests. MATH uses competition-level problems from AMC, AIME, and Olympiad contests.

Benchmark scores should be interpreted with caution. Different evaluation setups (few-shot vs. zero-shot, different prompt templates) can shift scores by several percentage points. Some providers self-report scores under favorable conditions. Where possible, this table uses third-party evaluation data. The absence of a score means reliable data was not available, not that the model performs poorly.

LLM Pricing: What You Actually Pay

API pricing is quoted per million tokens, but your actual cost depends on how efficiently each model tokenizes your text. The same prompt produces different token counts across models because each uses a different tokenizer. A model that quotes a lower per-token price might actually cost more if its tokenizer splits your text into more tokens. Use our AI Token Calculator to compare token counts for your specific text across all major model families.

Many providers offer discounts for cached inputs (typically 90% off) and batch processing (typically 50% off). Open-source models can be self-hosted for the cost of GPU compute, which can be significantly cheaper at scale but requires infrastructure expertise. The prices in this table reflect standard API rates from each provider or the lowest available hosted option for open-source models.

LLM Comparison FAQ