Best LLM in 2026: Which Model to Use Right Now

GPT-5.6 Sol is the best LLM if you only care about quality. Score 59 on the Artificial Analysis Intelligence Index. But it costs $35 per million tokens. Most people should use Grok 4.5 instead: score 54, costs $8/1M, handles 90% of the same tasks.

That's the short answer. Here's the full ranking and when each model actually makes sense.

Best LLM Rankings (July 2026)

Quality scores from the Artificial Analysis Intelligence Index v4.1 (independent benchmark testing reasoning, coding, and factual accuracy). Pricing from OpenRouter and official provider APIs, July 2026.

The Quick Answer: Which LLM Should You Use?

For most people: Grok 4.5 or GPT-5.6 Luna.

Grok 4.5 sits at the sweet spot. Score 54, costs $8/1M total, runs at 120 tokens per second. It uses 60% fewer tokens per task than Claude Fable 5, which means your actual cost per task is even lower than the per-token price suggests.

Luna is for when you're running high volume or the task is simple. Score 51, costs $7/1M, fastest generation at 150 tok/s. We use Luna for first drafts, simple code edits, and any task where "good enough" is genuinely good enough.

For hard problems: GPT-5.6 Sol.

Architecture decisions, debugging unfamiliar codebases, multi-step reasoning across large contexts. Sol's Coding Agent Index score of 80 is the highest of any model. When accuracy matters more than cost, Sol is the answer.

Skip Terra. It costs $17.50/1M but scores only 4 points higher than Luna ($7/1M). The gap doesn't justify the price for any workload we've tested.

Best LLM by Use Case

Best LLM for Coding

Winner: GPT-5.6 Sol (Coding Agent Index: 80)

Sol handles agentic coding tasks better than any model. Multi-file refactors, debugging complex issues, understanding large codebases. We've run it against Kimi K3 (78) and Claude Fable 5 (varies) on identical tasks. Sol produces cleaner, more targeted edits with fewer unnecessary changes.

Budget alternative: GPT-5.6 Luna (score 75, one-fifth the price). Handles 70-80% of coding tasks competently. Use Luna for boilerplate, simple bugs, and well-defined feature implementations. Switch to Sol when you're stuck.

For the full breakdown, see our best AI models for coding guide.

Best LLM for Writing

Winner: Claude Sonnet 5 (Quality: 53, $12/1M)

Anthropic's models produce more natural, varied prose than any competitor. The writing feels less templated. Sonnet 5 also excels at research synthesis: feeding it multiple documents and getting a coherent summary.

For marketing copy specifically: Grok 4.5 is surprisingly good. Punchy, direct, less prone to the "AI voice" problem. And cheaper.

Best LLM for Data Analysis

Winner: GPT-5.6 Sol for accuracy, Gemini 3.1 Pro for massive datasets.

Sol is best when mathematical precision matters (statistics, regression, multi-step calculations). Gemini 3.1 Pro has the only 2M token context window, which means you can feed it entire datasets that would exceed other models' limits.

Budget: Grok 4.5 handles most analytical work at a quarter of Sol's cost.

Full comparison: best AI for data analysis.

Best Free LLM

Winner: DeepSeek R1 (free via OpenRouter)

Has reasoning capabilities. Handles basic coding, analysis, and writing. The catch: 64K context window (small) and slower response times. Fine for personal projects, learning, and experimentation. Not production-grade for serious workloads.

The Cost-Performance Sweet Spot

Here's what most leaderboards don't show: the relationship between quality and price isn't linear. You pay exponentially more for each incremental point of quality at the top.

Going from Luna (51) to Grok 4.5 (54) costs an extra $1/1M for 3 more quality points. Going from Sol (59) to Fable 5 (60) costs an extra $25/1M for 1 quality point. The diminishing returns are steep.

Our recommendation: Start with Luna or Grok 4.5 as your default. Only escalate to Sol/Fable when the cheaper model fails the specific task. Most developers find that 80-90% of their work runs fine on mid-tier models.

What Changed in 2026

The LLM landscape shifts fast. Here's what's different from six months ago:

Kimi K3 entered the race. Moonshot's open-weight model (score 57) is the first open-source model to genuinely compete with GPT-5.6 Terra. It's 9x cheaper than Fable 5 with comparable reasoning. For teams that need self-hosted LLMs, K3 is now the answer.

GPT-5.6 split into three tiers. Instead of one model, OpenAI released Sol (frontier), Terra (mid), and Luna (budget). Luna at $1/$6 is the most important: it made GPT-class intelligence accessible at consumer-app pricing.

Grok 4.5 surprised everyone. xAI's model uses 60% fewer tokens per task, making its effective cost much lower than the per-token price suggests. In our testing, a coding agent task on Grok 4.5 costs roughly $0.15 vs $0.43 on Claude Fable 5.

Context windows normalized at 1M. Most models now support 1M+ tokens. The exception is Gemini 3.1 Pro at 2M. Context size stopped being a differentiator for all but the largest documents.

How We Test

We rank models using three data sources:

1. Artificial Analysis Intelligence Index (v4.1): Independent benchmark that tests reasoning, coding, and factual accuracy across standardized tasks. This is the "quality score" in our rankings.

2. Coding Agent Index (v1.1): Also from Artificial Analysis. Tests models specifically on agentic coding tasks: multi-file edits, debugging, and code generation.

3. Our own usage data: We run these models daily for content production, code generation, and data analysis across Dervity's tools. The recommendations reflect what we actually use, not just benchmark numbers.

Pricing is sourced from OpenRouter's aggregated pricing page and verified against official provider API documentation. Speed measurements come from Artificial Analysis performance benchmarks.

Compare Models Live

See the full rankings with interactive sorting and filtering in our LLM Leaderboard. Filter by task type, sort by any metric, find the best model for your specific workload.

For personalized recommendations based on your budget and use case, try the AI Model Selector.