ModelRefs / Best AI Models for Reasoning
Best AI Models for Reasoning
Top models for graduate-level reasoning, math, and analyst-grade decisioning.
Overview
This page answers: Which model is best at hard reasoning?
Candidates are ranked against the reasoning use case using current ModelRefs evidence. The ordering below was computed when this page was built and is recomputed on every deploy; with JavaScript enabled it is re-ranked against the live catalogue on load.
How this ranking is produced
Recommendation engine phase-2.0.0, 16 benchmarks evaluated, aggregate freshness fresh. Computed from the canonical ModelRefs registry when this page was built on 2026-11-06, and recomputed on every deploy.
Each candidate below is a provisional fit signal under the stated constraints, not a guarantee, a certification, or a final ranking. Fit scores compare models against this use case's capability weights using current ModelRefs evidence; they are not accuracy rates, benchmark results, or production-readiness claims.
What we measure for Complex Reasoning
- Reasoning
- 100%
Weights derived from the ModelRefs capability ontology. Scores sourced from primary benchmark leaderboards where available; expansion entries carry lower confidence (0.65).
Ranked candidates
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#1 GPT-5 — OpenAI
Fit score 93 out of 100.
- Strong hallucination resistance (99/100).
- Strong tool use (97/100).
- Strong reasoning (93/100).
Capability evidence
- Reasoning — evidence confidence 40% — Top-tier reasoning (93/100). via aime-2025, gpqa
Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 100% · Benchmarks: 100% · Stability: 88%
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#2 Llama 3.1 405B — Meta
Fit score 89 out of 100.
- Strong reasoning (89/100).
- Strong structured output (89/100).
- Strong instruction following (87/100).
Capability evidence
- Reasoning — evidence confidence 40% — Top-tier reasoning (89/100). via gsm8k, bbh
Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 100% · Benchmarks: 100% · Stability: 63%
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#3 GPT-5 Mini — OpenAI
Fit score 88 out of 100.
- Strong hallucination resistance (99/100).
- Strong long context (89/100).
- Strong rag suitability (89/100).
Capability evidence
- Reasoning — evidence confidence 40% — Top-tier reasoning (88/100). via aime-2025, gpqa
Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 100% · Benchmarks: 100% · Stability: 88%
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#4 o3 — OpenAI
Fit score 86 out of 100.
- Strong reasoning (86/100).
- Caution: Weak cost efficiency (13/100).
- Caution: Limited benchmark coverage (1 scores).
Capability evidence
- Reasoning — evidence confidence 40% — Top-tier reasoning (86/100). via gpqa
Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 100% · Benchmarks: 100% · Stability: 13%
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#5 Gemini 2.5 Flash — Google
Fit score 86 out of 100.
- Strong cost efficiency (95/100).
- Strong reasoning (86/100).
- Caution: Limited benchmark coverage (1 scores).
Capability evidence
- Reasoning — evidence confidence 40% — Top-tier reasoning (86/100). via gpqa
Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 100% · Benchmarks: 100% · Stability: 13%
-
#6 Mistral Large 2 — Mistral AI
Fit score 86 out of 100.
- Strong reasoning (86/100).
- Strong instruction following (86/100).
- Caution: Limited benchmark coverage (1 scores).
Capability evidence
- Reasoning — evidence confidence 40% — Top-tier reasoning (86/100).
Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 0% · Benchmarks: 0% · Stability: 13%
-
#7 Mixtral 8x7B — Mistral AI
Fit score 83 out of 100.
- Strong cost efficiency (91/100).
- Strong reasoning (83/100).
- Strong instruction following (83/100).
- Caution: Limited benchmark coverage (1 scores).
Capability evidence
- Reasoning — evidence confidence 40% — Top-tier reasoning (83/100).
Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 0% · Benchmarks: 0% · Stability: 13%
-
#8 o4 Mini — OpenAI
Fit score 79 out of 100.
- Strong reasoning (79/100).
- Caution: Limited benchmark coverage (1 scores).
Capability evidence
- Reasoning — evidence confidence 40% — Top-tier reasoning (79/100). via gpqa
Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 100% · Benchmarks: 100% · Stability: 13%
-
#9 Claude Opus 4 — Anthropic
Fit score 79 out of 100.
- Strong reasoning (79/100).
Capability evidence
- Reasoning — evidence confidence 40% — Top-tier reasoning (79/100). via gpqa, aime-2025
Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 100% · Benchmarks: 100% · Stability: 88%
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#10 Gemini 1.5 Pro — Google
Fit score 78 out of 100.
- Strong long context (94/100).
- Strong reasoning (78/100).
- Strong instruction following (78/100).
- Caution: Limited benchmark coverage (2 scores).
Capability evidence
- Reasoning — evidence confidence 40% — Top-tier reasoning (78/100). via mmlu
Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 100% · Benchmarks: 100% · Stability: 25%
Knowledge Graph signal
Independent cross-validation from the ModelRefs semantic graph. Models below were identified via graph traversal of benchmark to capability to use-case edges — a separate signal from the recommendation engine above.
Capability fit describes how strongly a model's measured capabilities match this use case. It is not an accuracy rate or production-readiness guarantee. Evidence confidence describes how complete and well-supported the evidence behind that fit is; missing or stack-level requirements lower confidence.
- #1 GPT-5 — evidence confidence 53%
- #2 Llama 3.1 405B — evidence confidence 80%
- #3 GPT-5 Mini — evidence confidence 53%
- #4 o3 — evidence confidence 27%
- #5 Gemini 2.5 Flash — evidence confidence 27%
- #6 Mistral Large 2 — evidence confidence 27%
Limits of this ranking
Coverage is uneven. A model ranks only where ModelRefs holds benchmark-eligible evidence for the capabilities this use case requires, so a strong model with thin evidence can rank low or be absent entirely. Confidence, evidence, and benchmark-coverage figures beside each candidate say how well supported its position is — read them before acting on the order.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Best AI Models for Reasoning.