The Leaderboard

On the Artificial Analysis Intelligence Index — a composite of reasoning, coding, math and knowledge benchmarks — the top of the table looks like this:

61.4
Claude Opus 4.8 — #1 overall
60.2
GPT-5.5
57
Gemini 3.1 Pro
53
Grok 4.3

Note the spread: just over 8 points separate #1 from #4. For most real tasks, all four are extremely capable and the practical difference is small. (Claude Fable 5 would top this list outright — but it was pulled after a US export-control order on June 12, so it's not a model you can actually deploy right now.)

Best for Coding: Claude Opus 4.8

Opus 4.8 is the strongest generally available model for software engineering and long-running agentic coding tasks. It leads SWE-bench and holds up across multi-file refactors and debugging sessions where weaker models lose the thread.

ModelSWE-bench VerifiedSWE-bench Pro
Claude Opus 4.888.6%69.2%
GPT-5.558.6%
Gemini 3.1 Pro54.2%

If you write code with AI daily, this is the default. For the heaviest agentic work — large migrations, hours-long autonomous runs — Fable 5 was briefly ahead, but Opus 4.8 is the reliable, available choice.

Best for Reasoning: Gemini 3.1 Pro

When the task is hardest-mode reasoning — graduate-level science, novel logic puzzles, memorization-proof problems — Gemini 3.1 Pro leads the published benchmarks:

If your work lives in research, hard math, or analysis where being wrong is expensive, Gemini 3.1 Pro is worth keeping in the rotation specifically for the hard cases.

Best for Writing: GPT-5.5

The GPT line has owned creative writing since GPT-5.1, and GPT-5.5 continues it with a warm, natural tone that still reads least like a machine. It launched on April 23, 2026 with a reported 60% drop in hallucinations versus GPT-5.4 — a meaningful reliability gain on top of its prose strengths. It's free in ChatGPT, or $5 / $30 per million tokens via API.

Best for Price-Performance: Gemini 3.5 Flash

Not every task needs a frontier brain. Gemini 3.5 Flash lands at an Intelligence Index of 55 — within striking distance of the top — at a fraction of the cost, making it the best value for high-volume work: classification, summarization, extraction, routing, and the cheap legs of an agentic pipeline.

Pro move: Don't pick one model — route. Use a cheap, fast model for bulk steps and escalate to a frontier model only for the hard decisions. This is the core idea behind loop engineering, and it's how the best teams cut costs without losing quality.

The Quick-Pick Table

If your job is…UseWhy
Daily coding & agentsOpus 4.8Best available SWE-bench, reliable long runs
Hardest reasoningGemini 3.1 ProLeads GPQA & ARC-AGI-2
Writing & natural toneGPT-5.5Best prose, fewer hallucinations
High-volume / cheapGemini 3.5 FlashFrontier-ish at a fraction of the price
Real-time / X dataGrok 4.3Strong all-rounder, live data access

The Bottom Line

The frontier is a near-tie. Smartness is no longer the differentiator — fit is. Pick by task, not by leaderboard position, and keep at least two models wired up so you can switch when one gets better, cheaper, or (as June proved) suddenly unavailable.

Want to go deeper? Compare the same prompt across models with our multi-model workflow guide, explore live numbers in the AI Benchmarks tool, or let the AI Model Selector pick for your exact use case.

Use three models? Keep one prompt library.

If you're switching between Claude, GPT and Gemini, your prompts shouldn't live in three places. PromptChief keeps them in one library — versioned, searchable, and ready to paste into any model.

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