ARC-AGI-2 leaderboard: Which models reason best?
ARC-AGI-2 (opens in a new tab) (Abstract Reasoning Corpus for Artificial General Intelligence) is built to test fluid intelligence: the knack for solving a fresh problem without leaning on memorised patterns or training data. MMLU checks what a model knows. ARC-AGI-2 checks whether it can actually work something out. The results separate the models that think from the ones that recall.
What ARC-AGI-2 measures
ARC-AGI-2 throws visual and logical puzzles at a model that call for:
- Spotting patterns in unfamiliar domains
- Inferring abstract rules
- Reasoning by analogy across different representations
- Composing simple rules into a solution for a harder problem
The tasks are deliberately built to defeat memorisation. A model can't coast by matching something similar from its training set. It has to reason from the ground up.
The ARC-AGI-2 leaderboard (June 2026)
A note before the numbers, because it matters: only one figure in this table is a real, measured ARC-AGI-2 score. That is Gemini 3.1 Pro at 77.1%, confirmed across public leaderboards (llm-stats (opens in a new tab)). Every other ARC-AGI-2 percentage below is an author estimate, derived from how MMLU and ARC-AGI-2 scores have tended to track each other. They are not benchmark results, and some of them are wide of the mark when checked against live data (more on that below). Treat the asterisked rows as a rough ordering, not a scoreboard.
| Rank | Model | ARC-AGI-2 | MMLU | Context | Price (In/Out) |
|---|---|---|---|---|---|
| 1 | Gemini 3.1 Pro | 77.1% | 88.1% | 1M | $3.50 / $10.50 |
| 2 | Claude Fable 5 | ~75%* | 92.1% | 1M | $10.00 / $50.00 |
| 3 | Claude Opus 4.8 | ~72%* | 89.8% | 1M | $5.00 / $25.00 |
| 4 | GPT-5.5 Pro | ~71%* | 89.7% | 400K | $8.00 / $40.00 |
| 5 | Claude Opus 4.7 | ~70%* | 89.2% | 1M | $5.00 / $25.00 |
| 6 | GPT-5.5 | ~69%* | 88.4% | 400K | $5.00 / $30.00 |
| 7 | Claude Sonnet 4.6 | ~68%* | 87.6% | 1M | $3.00 / $15.00 |
| 8 | Grok 4 | ~67%* | 87.2% | 256K | $5.00 / $25.00 |
| 9 | Gemini 3.5 Flash | ~66%* | 86.8% | 1M | $0.35 / $0.70 |
| 10 | MiniMax M3 | ~65%* | 86.4% | 1M | $0.30 / $1.20 |
| 11 | Kimi K2.7-Code | ~64%* | 85.7% | 256K | $0.50 / $2.00 |
| 12 | DeepSeek V3.5 | ~63%* | 85.8% | 1M | $0.15 / $0.60 |
| 13 | GLM-5.2 | ~63%* | 85.2% | 256K | $0.80 / $2.40 |
| 14 | Mistral Large 2 | ~62%* | 85.1% | 256K | $2.00 / $6.00 |
| 15 | Llama 4 | ~62%* | 84.8% | 256K | Free |
| 16 | Qwen 3 | ~61%* | 84.6% | 128K | $0.40 / $1.20 |
| 17 | GPT-5.5 Instant | ~58%* | 84.2% | 128K | $0.50 / $1.50 |
*Estimated from the correlation between MMLU and ARC-AGI-2 performance. Only Gemini 3.1 Pro's 77.1% is a confirmed benchmark score.
A few honest caveats on this table. Several models in it (the various Opus 4.7/4.8 and GPT-5.5 variants, MiniMax M3, Kimi K2.7, GLM-5.2 and others) carry MMLU and pricing figures we have not individually checked, and some of those models may be unreleased. Claude Fable 5 is real (opens in a new tab), Anthropic shipped it on 9 June 2026 at $10 in / $50 out per million tokens, the listed numbers there are right. The Gemini 3.1 Pro pricing in the table ($3.50 / $10.50) does not match what is reported publicly: OpenRouter (opens in a new tab) lists roughly $2.00 input / $12.00 output per million tokens under 200K, rising above that for longer context. The ~1M context window is about right.
Where the estimates fall down
This is the part to be straight about. The single confirmed score, Gemini 3.1 Pro at 77.1%, is genuinely strong. But the article's original framing put Gemini 3.1 Pro at the top of the pile as the June 2026 reasoning leader, and live leaderboards don't back that up. As of June 2026, BenchLM.ai (opens in a new tab) and llm-stats (opens in a new tab) show GPT-5.5 leading ARC-AGI-2 at around 85%, with a GPT-5.4 Pro also reportedly ahead of Gemini. On that reading Gemini 3.1 Pro sits second or third, not first.
The estimated rows have the same problem in miniature. The GPT-5.5 estimate of ~69% lands well below its reported ~85%. The Grok 4 estimate of ~67% is the starkest miss: live data points to something closer to 15.9% on llm-stats, with a separate "Grok 4.20" entry at 53.3% on BenchLM. Neither is anywhere near 67%. So when you read down the table, read the asterisks as a reminder that MMLU-to-ARC-AGI-2 extrapolation can be badly wrong for individual models.
Why reasoning matters
A high ARC-AGI-2 score tends to travel with:
- Stronger results on genuinely new problems that aren't in the training data
- More dependable multi-step deduction
- Less sensitivity to how a prompt is worded
- Better transfer into unfamiliar domains
When the work in front of you is genuinely novel, research, open-ended problem-solving, strategic analysis, ARC-AGI-2 is a better guide to a model's usefulness than MMLU. MMLU rewards recall; this rewards working it out.
Verdict
Here is the measured version, without the hype. Gemini 3.1 Pro's confirmed 77.1% on ARC-AGI-2 is a serious reasoning result and a fair reason to shortlist it for reasoning-heavy work. It is not, on the public 2026 leaderboards, the outright leader, GPT-5.5 (around 85%) sits ahead of it, so calling any one model the "champion with no equal" overstates the case.
For knowledge work, coding or general Q&A, other models may give you better value per dollar. For tasks that hinge on genuine abstract reasoning, puzzles, novel research, creative synthesis, Gemini 3.1 Pro is a strong pick, just check the current ARC Prize leaderboard (opens in a new tab) before you commit, because the top of this list moves.
Strong on reasoning (confirmed): Gemini 3.1 Pro at 77.1%, though GPT-5.5 reportedly leads the live ARC-AGI-2 board.
ARC-AGI-2 leaderboard: answer-first summary
ARC-AGI-2 leaderboard matters because it can change how Founders and operators plan, build, or govern an tool evaluation workflow. ARC-AGI-2 tests fluid intelligence and abstract reasoning.
The direct answer is this: do not treat the topic as a standalone trend. Treat it as a decision about inputs, outputs, review ownership, data exposure, and whether the workflow produces a result that is faster, safer, or more useful than the current process.
ARC-AGI-2 leaderboard: implementation checklist
- Define the user, job to be done, and success metric for the tool evaluation workflow.
- Collect real examples, policies, source files, customer questions, or search queries before writing prompts or choosing tools.
- Separate low-risk drafts from decisions that need approval, privacy checks, or senior review.
- Document what the AI is allowed to access, what it must not access, and who signs off before production use.
- Review time to value, adoption rate, cost per workflow, quality review score after a small pilot rather than judging the idea from a demo.
This keeps the work practical. It also gives search engines and AI answer engines a clean factual structure: what the topic is, who it helps, what to do next, and which risks matter before implementation.
Decision criteria for ARC-AGI-2 leaderboard
| Decision area | What to check | Production signal |
|---|---|---|
| Intent | Does ARC-AGI-2 leaderboard solve a real workflow problem? | The use case has a named owner and measurable outcome. |
| Data | Can the required data be used safely? | Sensitive data is classified and access is controlled. |
| Quality | Can a reviewer judge the output consistently? | Examples, rubrics, or acceptance criteria exist. |
| Scale | Can the workflow be repeated without hero effort? | The process is documented and can be handed to another team member. |
Practical example for ARC-AGI-2 leaderboard
A small business could use this article to choose one practical test. For example, a manager might take one customer-facing process, one internal document workflow, or one recurring content task and redesign only that step with AI support. The goal is not to automate the whole business at once; it is to learn where Model Review creates reliable leverage.
The useful deliverable is a short operating note: the trigger, the source material, the prompt or tool, the review checklist, the escalation rule, and the metric. That note becomes the handover asset for staff training, SEO/GEO content, service delivery, or future agent work.
Risks and controls for ARC-AGI-2 leaderboard
The common failure pattern is moving too quickly from a promising idea into an unmanaged workflow. For ARC-AGI-2 leaderboard, the risk is not only bad output. It can also be unclear data permission, staff confusion, duplicate content, unreviewed customer advice, or a tool that quietly changes cost or capability.
- Control tool sprawl with a named owner, a review step, and written acceptance criteria.
- Control unclear pricing with a named owner, a review step, and written acceptance criteria.
- Control vendor lock-in with a named owner, a review step, and written acceptance criteria.
- Control unreviewed data sharing with a named owner, a review step, and written acceptance criteria.
Measurement plan for ARC-AGI-2 leaderboard
A useful AI or SEO initiative should leave evidence. Track time to value, adoption rate, cost per workflow, quality review score and compare the pilot against the current process. If the measure does not improve, keep the learning but avoid scaling the workflow.
For GEO readiness, the page should also answer the core question directly, define the entities involved, include implementation steps, explain tradeoffs, and link readers to the next relevant AI Kick Start service, guide, tool, or article.
Definitions and entities for ARC-AGI-2 leaderboard
For search, GEO, and staff handover, define the core entities in plain language. In this article the important entities are the workflow owner, the AI tool or model, the source material, the review process, the risk boundary, and the measurable business outcome. Clear definitions make the page easier for people to scan and easier for AI answer engines to quote accurately.
- Workflow owner: the person accountable for deciding whether ARC-AGI-2 leaderboard belongs in the business process.
- Source material: the documents, examples, policies, URLs, prompts, videos, or customer questions that ground the output.
- Review boundary: the point where a human checks accuracy, privacy, brand voice, or customer impact before the result is used.
- Success metric: the measure that proves whether the tool evaluation workflow is worth repeating.
ARC-AGI-2 leaderboard versus doing nothing
Doing nothing is also a decision. The cost may be slow manual work, weaker search visibility, inconsistent advice, duplicated effort, or staff using unmanaged AI tools without a shared process. The practical question is whether a controlled pilot can reduce that cost without creating a larger governance problem.
| Option | When it makes sense | What to watch |
|---|---|---|
| Do nothing | The workflow is rare, low value, or already reliable. | Competitors may improve speed, content depth, or service consistency first. |
| Run a small pilot | The task repeats often and has clear review criteria. | Keep scope tight and measure the result against the current process. |
| Build a production workflow | The pilot is repeatable and risk controls are documented. | Assign ownership, monitoring, training, and a rollback path. |
AI Kick Start handover package for ARC-AGI-2 leaderboard
A production handover should be concrete enough that another person can run it. For ARC-AGI-2 leaderboard, that means a short brief, a workflow map, approved prompts or tool settings, source material, a review checklist, internal links to supporting resources, and a simple measurement sheet. This is the difference between reading about AI and turning it into operational capability.
That packaging also strengthens E-E-A-T. It shows experience through implementation notes, expertise through decision criteria, authoritativeness through source-aware structure, and trust through risks, controls, and review steps. The article becomes useful even if the reader never buys a tool because it helps them make a better operational decision.
