This is not a leaderboard; it is a map for choosing. It gathers the strongest and most reasonably priced models for writing, research, coding, and everyday tasks. Models and prices keep moving; what you see here is the most recently checked snapshot.
Use this to narrow your options, not to replace testing on your own tasks.
Each model's accuracy on DeepSWE (long-horizon agentic coding) against its average cost per task. Note that the X axis runs opposite to convention: cheaper to the right, more accurate toward the top, so the top-right corner means accurate and cheap. Full chart notes are below the figure.
How to read this chart: six main models (sol, terra, luna, fable-5, opus 4.8, sonnet 5) each get an "effort curve" connecting the cost and score of their five settings from low to max; the model name sits at its highest-scoring point. The flatter the curve, the less accuracy a model loses when you dial effort down to save money. The other seven models are plotted only at their best point, as background reference. The gold line is the efficiency frontier: the settings for which no point on the chart is both more accurate and cheaper. Hover over any point for its score, ±95% confidence interval, and cost per task.
Source · , ; figures come straight from the leaderboard's public data file, and cost is the per-task average in USD. This is one benchmark's view (long-horizon agentic coding): rankings for pure coding or everyday chat differ. For other ways to draw this: the official interactive DeepSWE board (every setting of every model plotted, with cost, output tokens, or agent steps on the X axis), or Artificial Analysis (a different scatter: overall intelligence versus price).
| Model | Vendor | Effort | DeepSWE score | Cost per task | Frontier |
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ARC-AGI-2 tests a batch of abstract visual reasoning puzzles no model has seen, to measure "generalization ability": whether a model can reason on the fly about rules it has never encountered, rather than pattern-matching to similar problems from its training data. A higher score means it handles genuinely novel problems better; this is a different capability from the coding benchmarks above.
Up front: has no official ARC-AGI-2 submission. Submissions are voluntary and vendor-initiated, not something we curate; the 11 models in the table below are every officially published result we could find.
Source · ARC Prize (arcprize.org/leaderboard), verified ; cost is the per-task figure vendors reported at submission time, on a different basis from the DeepSWE chart above, comparable only within this chart, not additive across charts.
This leaderboard's model roster isn't identical to the one above: ARC-AGI-2 submissions are voluntary and vendor-initiated, not something we selected. As of verification, have no official results, so they don't appear in this table; that doesn't mean weaker generalization, just that no comparable data exists yet on this leaderboard. Model names follow ARC Prize's official submission naming, which may refer to a different generation than the coding-benchmark names above.
| Model | Vendor | Effort | ARC-AGI-1 | ARC-AGI-2 | Cost Per Task |
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This is the order used to rank this page, not a hard rule. For your own situation, use the prompts at the bottom and let an AI re-compare against your task.
"Cost" here means API usage cost, not a chat subscription fee. DeepSWE figures are cost per task; SWE-bench figures are price per million tokens. The two baselines differ and should only be compared within their own kind.
Coding boards come in two kinds: those run with each vendor's own harness (such as Artificial Analysis's Coding Agent Index, where each lab uses its own Claude Code, Codex, or Grok Build) tend to be optimistic and hard to compare across vendors; those run with a standardized agent (such as DeepSWE, one yardstick, contamination-resistant) are fairer but cover fewer models. Check which kind a score belongs to before reading it.
These are the picks most worth considering at each budget, what analysts call the value frontier.
The same model can look very different on another board or another task. These are gaps worth knowing beyond the scores.
When a top pick changes, the date and reason are recorded here so you can trace how the judgment evolved. Newest first.
This page is a general comparison. Your actual choice also depends on your task, budget, usage volume, and error tolerance. Copy a question below into the AI you already use and let it compare for your situation.