COMPARE AI MODELS

Compare every frontier AI model side-by-side (2026)

GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, DeepSeek V4, Grok 4.3, Qwen3.6-Max, Mistral Large 3 — feature, price, and use-case comparison across every major lab. Or run them all in parallel through an LLM council.

Run all of them free Live benchmarks

There is no single 'best AI model' — there's the best model for your job. Claude Opus 4.7 wins on nuance and long-form reasoning. GPT-5.5 wins on tool-calling and code. Gemini 3.1 Pro wins on 1M+ context and multimodal. DeepSeek V4 wins on cost-per-token. Grok 4.3 wins on real-time / X-corpus questions. Qwen3.6-Max wins on multilingual. Mistral Large 3 wins on data sovereignty and EU compliance. Most teams settle on 2-3 of these and switch by task — or they run all of them through an LLM council and let the moderator synthesize. This page summarizes the cross-lab landscape so you can decide either way.

Quick verdict by use case

If you want... Pick Why
Best writing nuanceClaude Opus 4.7Strongest at tone, voice, and editorial taste.
Best general codeGPT-5.5 + Claude Sonnet 4.6GPT for tool calling and agentic loops; Sonnet for code review.
Massive context (1M+)Gemini 3 ProLargest context window in production; strong multimodal.
Lowest cost per tokenDeepSeek V4 Pro~95% cheaper than GPT-5.5, surprisingly capable.
Real-time / current eventsGrok 4.3Native X/web/news search integration.
MultilingualQwen3.6-MaxStrongest Chinese + multilingual frontier model.
EU data residencyMistral Large 3French lab; EU-resident endpoints; strong on European languages.
Maximum verificationAll of them, in council modeCross-lab consensus catches single-model hallucinations.

Detailed comparisons

Frequently asked questions

Which AI model is best in 2026?

It depends on the job. Claude Opus 4.7 wins on writing nuance and long-form reasoning. GPT-5.5 wins on tool-calling. Gemini 3 Pro wins on 1M+ context and multimodal. DeepSeek V4 wins on cost-per-token. Grok 4.3 wins on real-time questions. There is no universal winner — that's exactly why Council AI runs them in parallel.

Are these benchmarks objective?

We surface third-party benchmark scores (Intelligence Index, GPQA, MATH, LiveCodeBench) on the /benchmarks page. The verdicts on this page also incorporate hands-on use across the team. We don't sell any one provider's API directly, so we have no incentive to push you toward a specific lab.

Can I test multiple models against the same prompt?

Yes — that's the core Council AI workflow. Type a prompt, select up to 10 models, watch them respond in parallel, and let the moderator synthesize. Free includes 3 models per chat; Pro and Ultra go up to 10.