Best AI Models in 2026 — Overall Rankings
The best AI model right now is a tie. Claude Opus 5.5 (Supported) and GPT-6.1 Sol (Supported) share the top of the BenchLM overall ranking at 86.6 and 84.1 as of October 17, 2026, and the two 90% intervals overlap. GPT-6 Astra ranks third at 83.9.
Citable stat224 AI models are ranked as of October 17, 2026; the top two, at 86.6/100 and 84.1/100, are tied within their 90% intervals.
What the ranking says today
I would start a broad evaluation with Claude Opus 5.5, GPT-6.1 Sol, and GPT-6 Astra. They are the first three rows under the same public scoring contract; the order is a shortlist, not a purchase order.
Operator review by Glevd
Current ranking. 224 AI models are ranked as of October 17, 2026; Claude Opus 5.5 and GPT-6.1 Sol, at 86.6/100 and 84.1/100, are tied within their 90% intervals.
Honest limit. BenchAlign v5.8 projects a common score from uneven public evidence. An “estimated” badge and a wide interval mean the row is useful for discovery but weak evidence for a close call. Replay the finalists on your own tasks, latency budget, and safety constraints. Use the model selection guide to plan that test, then compare the top two rows.
Full Rankings (224 ranked, 1 tracked)
74.3
BenchAlign v5.8
Conditional range 59.92–88.62
69.4
BenchAlign v5.8
Conditional range 59.71–79.16
69
BenchAlign v5.8
Conditional range 54.61–83.31
68.6
BenchAlign v5.8
Conditional range 54.25–82.95
68
BenchAlign v5.8
Conditional range 53.68–82.38
66.7
BenchAlign v5.8
Conditional range 57.02–76.46
65.6
BenchAlign v5.8
90% interval 60.59–70.63
64.7
BenchAlign v5.8
Conditional range 54.98–74.43
64.5
BenchAlign v5.8
Conditional range 54.79–74.23
63.8
BenchAlign v5.8
Conditional range 49.43–78.13
63.2
BenchAlign v5.8
Conditional range 48.84–77.54
62.8
BenchAlign v5.8
Conditional range 48.45–77.15
62.3
BenchAlign v5.8
Conditional range 47.98–76.68
58.4
BenchAlign v5.8
Conditional range 48.68–68.13
56.1
BenchAlign v5.8
Conditional range 41.74–70.43
55.9
BenchAlign v5.8
90% interval 43.60–68.19
55.3
BenchAlign v5.8
Conditional range 40.99–69.69
55.3
BenchAlign v5.8
Conditional range 45.58–65.03
55.1
BenchAlign v5.8
Conditional range 45.36–64.80
54.2
BenchAlign v5.8
Conditional range 39.85–68.55
54.2
BenchAlign v5.8
Conditional range 39.85–68.55
54.2
BenchAlign v5.8
Conditional range 39.85–68.55
54.2
BenchAlign v5.8
Conditional range 39.85–68.55
54.2
BenchAlign v5.8
Conditional range 39.85–68.55
54.1
BenchAlign v5.8
Conditional range 39.75–68.45
54.1
BenchAlign v5.8
90% interval 41.41–66.68
53.9
BenchAlign v5.8
Conditional range 39.52–68.22
53.5
BenchAlign v5.8
90% interval 45.05–61.94
53.2
BenchAlign v5.8
Conditional range 38.82–67.52
52.6
BenchAlign v5.8
90% interval 42.27–62.94
52.2
BenchAlign v5.8
Conditional range 37.86–66.56
50.4
BenchAlign v5.8
Conditional range 36.03–64.72
49.6
BenchAlign v5.8
90% interval 36.97–62.28
49.5
BenchAlign v5.8
90% interval 42.30–56.74
49.4
BenchAlign v5.8
Conditional range 35.04–63.74
48.9
BenchAlign v5.8
Conditional range 34.50–63.19
48.6
BenchAlign v5.8
Conditional range 34.24–62.94
48.5
BenchAlign v5.8
Conditional range 34.12–62.81
48.4
BenchAlign v5.8
Conditional range 38.68–58.13
48
BenchAlign v5.8
Conditional range 38.24–57.68
47
BenchAlign v5.8
Conditional range 37.24–56.69
46.9
BenchAlign v5.8
Conditional range 32.58–61.28
46.7
BenchAlign v5.8
Conditional range 32.37–61.07
46.6
BenchAlign v5.8
Conditional range 32.24–60.93
46
BenchAlign v5.8
Conditional range 36.26–55.71
45.8
BenchAlign v5.8
Conditional range 36.11–55.55
45.8
BenchAlign v5.8
90% interval 34.82–56.79
45.7
BenchAlign v5.8
90% interval 30.94–60.36
45.2
BenchAlign v5.8
Conditional range 30.86–59.56
45.1
BenchAlign v5.8
Conditional range 30.76–59.46
43.7
BenchAlign v5.8
Conditional range 29.30–58.00
43.5
BenchAlign v5.8
Conditional range 29.16–57.86
43.5
BenchAlign v5.8
Conditional range 33.76–53.20
42.8
BenchAlign v5.8
Conditional range 33.10–52.54
42.1
BenchAlign v5.8
Conditional range 32.39–51.84
41.7
BenchAlign v5.8
Conditional range 32.02–51.46
41.2
BenchAlign v5.8
Conditional range 26.84–55.54
41.2
BenchAlign v5.8
Conditional range 31.44–50.88
41
BenchAlign v5.8
Conditional range 31.28–50.72
40.8
BenchAlign v5.8
90% interval 24.25–57.31
40.1
BenchAlign v5.8
Conditional range 30.33–49.77
39.9
BenchAlign v5.8
Conditional range 30.16–49.61
39.4
BenchAlign v5.8
Conditional range 29.65–49.09
39.2
BenchAlign v5.8
Conditional range 29.46–48.90
39.1
BenchAlign v5.8
Conditional range 29.41–48.86
38.1
BenchAlign v5.8
Conditional range 28.36–47.80
37.9
BenchAlign v5.8
Conditional range 23.55–52.25
37.5
BenchAlign v5.8
Conditional range 23.15–51.85
36.5
BenchAlign v5.8
90% interval 20.08–52.91
36.4
BenchAlign v5.8
Conditional range 22.00–50.70
36.1
BenchAlign v5.8
Conditional range 21.79–50.49
34.7
BenchAlign v5.8
Conditional range 24.98–44.43
34.5
BenchAlign v5.8
Conditional range 24.78–44.23
34.1
BenchAlign v5.8
Conditional range 24.37–43.81
34
BenchAlign v5.8
Conditional range 19.63–48.33
33.8
BenchAlign v5.8
Conditional range 19.42–48.12
33.6
BenchAlign v5.8
Conditional range 19.27–47.97
33.5
BenchAlign v5.8
Conditional range 23.80–43.24
33.5
BenchAlign v5.8
Conditional range 23.76–43.20
33.5
BenchAlign v5.8
Conditional range 23.76–43.20
33.1
BenchAlign v5.8
Conditional range 23.36–42.81
32.4
BenchAlign v5.8
Conditional range 18.08–46.78
32.2
BenchAlign v5.8
Conditional range 17.87–46.57
31.6
BenchAlign v5.8
Conditional range 17.29–45.99
31.3
BenchAlign v5.8
Conditional range 16.98–45.67
31.2
BenchAlign v5.8
Conditional range 16.85–45.55
30.8
BenchAlign v5.8
Conditional range 16.46–45.16
30.1
BenchAlign v5.8
Conditional range 15.77–44.47
30
BenchAlign v5.8
Conditional range 15.63–44.33
29.9
BenchAlign v5.8
Conditional range 15.59–44.29
29.3
BenchAlign v5.8
Conditional range 14.92–43.62
29
BenchAlign v5.8
Conditional range 14.68–43.37
28.8
BenchAlign v5.8
Conditional range 14.46–43.16
28.8
BenchAlign v5.8
Conditional range 14.45–43.15
28.7
BenchAlign v5.8
Conditional range 19.02–38.46
28.5
BenchAlign v5.8
90% interval 13.24–43.74
28.5
BenchAlign v5.8
Conditional range 14.10–42.80
28.5
BenchAlign v5.8
Conditional range 14.10–42.80
28.4
BenchAlign v5.8
Conditional range 14.06–42.76
28.4
BenchAlign v5.8
Conditional range 14.04–42.74
28.4
BenchAlign v5.8
Conditional range 18.63–38.07
28.2
BenchAlign v5.8
Conditional range 18.48–37.93
28.1
BenchAlign v5.8
Conditional range 13.73–42.43
27.8
BenchAlign v5.8
Conditional range 18.05–37.49
27.7
BenchAlign v5.8
Conditional range 18.00–37.45
27.6
BenchAlign v5.8
Conditional range 17.86–37.31
27.4
BenchAlign v5.8
Conditional range 13.00–41.70
26.6
BenchAlign v5.8
Conditional range 16.89–36.33
25.5
BenchAlign v5.8
Conditional range 11.15–39.85
24.9
BenchAlign v5.8
Conditional range 15.22–34.66
24.5
BenchAlign v5.8
Conditional range 10.18–38.88
20.7
BenchAlign v5.8
Conditional range 10.94–30.39
20
BenchAlign v5.8
Conditional range 10.32–29.77
19.4
BenchAlign v5.8
Conditional range 9.71–29.16
19.2
BenchAlign v5.8
Conditional range 9.46–28.91
18.9
BenchAlign v5.8
Conditional range 9.16–28.60
18.2
BenchAlign v5.8
Conditional range 8.44–27.89
14.2
BenchAlign v5.8
90% interval 5.82–22.56
9.9
BenchAlign v5.8
Conditional range 0.21–19.66
7.2
BenchAlign v5.8
Conditional range 0.00–16.93
Public preview through Mistral Studio; Mistral says the weights are due by the end of October 2026.
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tracked
Current position
224 AI models are ranked as of October 17, 2026; Claude Opus 5.5 and GPT-6.1 Sol, at 86.6/100 and 84.1/100, are tied within their 90% intervals.
GPT-6.1 Sol ranks #2 at 84.1 with Supported evidence.
GPT-6 Astra ranks #3 at 83.9 with Supported evidence.
Key Takeaways
224 AI models are ranked as of October 17, 2026; Claude Opus 5.5 and GPT-6.1 Sol, at 86.6/100 and 84.1/100, are tied within their 90% intervals.
The best open-weight model is MiMo-V2.6-Pro at position #12.
224 models are ranked here; 1 tracked model is listed without a position.
Score in Context
What these scores mean
The overall score is a weighted average across 8 benchmark categories. Agentic (22%), coding (20%), and reasoning (17%) carry the most weight. A 5-point gap in overall score is meaningful — it reflects consistent performance differences across multiple domains.
Known limitations
The overall score compresses 8 categories into one number. Two models with the same overall score can have very different strengths — one might lead coding while the other leads reasoning. Always check category scores for your specific use case.
About this ranking
Ranking data as of October 17, 2026
BenchLM.ai now distinguishes provisional overall ranking from verified overall ranking. The provisional score is a normalized weighted average across 8 benchmark categories: agentic (22%), coding (20%), reasoning (17%), knowledge (12%), multimodal & grounded (12%), multilingual (7%), instruction following (5%), and math (5%), using non-generated benchmark coverage plus bounded external consensus calibration. The verified leaderboard is stricter and only counts sourced benchmark rows. Each score includes a confidence indicator (1-4 dots) based on how much sourced coverage supports it. Display-only benchmarks — including MMLU, OpenBookQA, HumanEval, FLTEval, BBH, LisanBench, and older AIME/HMMT variants — remain visible for context but do not affect ranking.
This is the public BenchAlign v5.8 overall lane. Scores combine the approved agentic and coding projections; evidence labels distinguish supported rows from estimates.
224 AI models are ranked as of October 17, 2026; Claude Opus 5.5 and GPT-6.1 Sol, at 86.6/100 and 84.1/100, are tied within their 90% intervals. All rows use the same BenchAlign v5.8 projection contract. Evidence badges matter as much as small point gaps because the public source coverage is uneven.
Open-weight and proprietary models share this lane, but deployment terms, latency, and operating cost are not part of the BenchAlign score. Use the ranking to choose a test set, then compare the finalists under the constraints that will exist in production.
This ranking uses the public BenchAlign v5.8 overall contract. For detailed model profiles, click any model name above. To compare two specific models head-to-head, use the "vs #" links.
Questions
What is the best AI model right now?
No single model, as of October 17, 2026. Claude Opus 5.5 scores 86.6 and GPT-6.1 Sol 84.1 on the overall ranking, but their 90% intervals overlap, so the data does not separate them. GPT-6 Astra is third at 83.9.
What is the smartest AI model?
It depends on the yardstick. On the broadest one, the overall score, Claude Opus 5.5 and GPT-6.1 Sol are statistically tied at 86.6 and 84.1 as of October 17, 2026. For a narrower meaning of smart, the reasoning, knowledge, and math rankings each measure one kind of work and can put a different model first.
What is the best LLM overall?
As of October 17, 2026, Claude Opus 5.5 and GPT-6.1 Sol share first place on the overall score at 86.6 and 84.1. Both positions are Supported, with 90% intervals of 81.74–91.38 and 80.10–88.04. Supported means we have enough independent evidence to stand behind the row.
How are these AI models ranked?
Each model’s overall score is a normalized weighted average across 8 benchmark categories, using non-generated benchmark coverage plus bounded external consensus calibration. A stricter verified leaderboard counts only sourced benchmark rows. Models without enough non-generated coverage are tracked but unranked, so a high position here reflects real, sourced evidence rather than a single headline benchmark.
Is the best AI model worth paying for?
Usually only for the slice of work where quality failures are expensive. The top-ranked models carry premium per-token prices, while models a few points lower often cost 2–5x less. Check the price-vs-performance view and the per-provider API pricing hubs to find the cheapest model that clears your quality bar, then route only high-stakes tasks to the leader.
Explore More
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
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