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Best Reasoning LLMs 2026: AI Models for Logic & Multi-Step Tasks

Find top reasoning LLMs in 2026: logic, instruction-following, native thinking modes, and estimated API cost. Ideal for agents, analytics, and complex workflows across the US, Canada, and Australia.

Reasoning-focused LLM rankings with transparent API economics in 2026

Reasoning models power agents, planning, and long chains of tool use—but they often bill more output tokens when “thinking” is enabled. This view highlights logic-heavy capability while showing what that sophistication costs per month for your token mix, helping operators in the US, Canada, and Australia avoid surprise overages.

Est. monthlyROI scoreCodingReasoningSpeedMathContextOverallOpen-weight

Workload & pricing toggles

Workload presets

Same three scenarios as the main AI API calculator: moderate traffic, large RAG-style context, or per-request max tokens with a lower request count.

Include Vision / Image Processing

Off — no image fees in cost estimates for vision-capable models.

Turn On to include image fees.

OffOn

Use Cached Pricing

Enable to get 50% off input tokens where cached rates apply

OffOn

Deep Reasoning / Thinking Mode

Model hidden reasoning / extended thinking charged like output tokens when enabled.

OffOn

Batch Pricing

Enable for 50% off input & output where batch/async pricing applies

OffOn
≈ $100.00/mo
8K
1K—1.0M
≈ $100.00/mo
2K
100—500K
≈ $200.00 total
5K
10—100K

Cached / batch est. monthly values only change after the pipeline sets supports_caching or supports_batch in Supabase. The toggles here narrow the table to models whose catalog or provider typically supports those modes.

Magic quadrant (top 15)

X: est. monthly · Y: Reasoning · Dot: provider color · Hover for rank, model & details

Full leaderboard

Showing 48 of 327 models.

PickModelEst. monthlyROI scoreCodingReasoningSpeedMathContextOverall
OpenAI: gpt-oss-20b$2.6084
96
97
85
98
131K
97
Z.ai: GLM 5$44.8064
99
93
55
84
203K
92
Grok 3$270.0061
93
93
55
96
131K
94
Google: Gemma 4 31B$9.0072
97
92
70
97
262K
94
GLM 5 Turbo$88.0057
88
91
70
60
203K
83
Qwen: Qwen3.5-27B$23.4064
80
91
70
92
262K
88
Qwen: Qwen3.5-122B-A10B$31.2062
81
90
55
85
262K
87
Z.ai: GLM 5.1$77.0061
92
89
70
85
203K
89
Qwen: Qwen3.5 397B A17B$39.0062
85
89
60
92
262K
89
Upstage: Solar Pro 3$12.0066
85
89
65
80
128K
86
OpenAI: GPT-5.2$210.0057
85
89
65
78
400K
85
Xiaomi: MiMo-V2-Pro$70.0059
85
89
60
80
1.0M
86
Qwen: Qwen3 32B$5.6073
85
89
60
92
41K
89
Arcee AI: Trinity Large Thinking$17.3064
85
89
55
80
262K
86
Baidu: ERNIE 4.5 21B A3B$5.6072
85
89
60
87
120K
88
Meta: Llama 3.3 70B Instruct$7.2069
88
89
70
77
131K
86
Deep Cogito: Cogito v2.1 671B$62.5059
85
89
60
80
128K
86
Qwen: Qwen3.5-35B-A3B$19.5064
76
89
70
95
262K
87
Amazon: Nova Premier 1.0$225.0057
89
89
55
77
1.0M
86
Qwen: Qwen3 235B A22B Thinking 2507$20.9362
74
89
50
83
131K
84
Anthropic: Claude Opus Latest$450.0052
74
88
55
60
1.0M
78
OpenAI: GPT-5.1 Chat$150.0058
91
88
85
77
128K
86
OpenAI: GPT-5.5$500.0055
92
88
55
71
1.1M
85
Qwen: Qwen3 VL 32B Instruct$8.3269
88
88
65
87
131K
88
xAI: Grok 4.20$140.0057
88
87
55
76
2.0M
85
OpenAI: GPT-5.4 Image 2$470.0053
86
87
70
65
272K
81
Z.ai: GLM 4.7$32.6064
94
87
85
96
203K
91
Mistral: Mistral Medium 3$36.0063
92
87
70
91
131K
89
OpenAI: GPT-4.1$160.0055
86
87
55
65
1.0M
81
Qwen: Qwen3.5-9B$5.5068
65
87
70
83
262K
80
Qwen: Qwen3 Max$70.2061
93
87
70
89
262K
89
MoonshotAI: Kimi K2 0711$45.8060
90
87
60
80
131K
86
xAI: Grok 3 Mini Beta$17.0063
90
87
95
77
131K
85
xAI: Grok 3 Mini$17.0063
88
86
92
76
131K
84
Tencent: Hunyuan A13B Instruct$11.3064
64
86
55
94
131K
83
OpenAI: GPT-5.3 Chat$210.0053
85
86
70
61
128K
79
OpenAI: GPT-5.4 Pro$3,000.0049
67
85
55
72
1.1M
77
AionLabs: Aion-1.0-Mini$42.0061
85
85
95
88
131K
86
OpenAI: GPT-5.2 Pro$2,520.0052
85
85
55
73
400K
82
Mistral Large 2411$140.0056
87
85
70
72
131K
82
Mistral: Mistral Large 3 2512$35.0059
87
85
60
72
262K
82
AllenAI: Olmo 3 32B Think$11.0067
90
84
50
88
66K
87
DeepSeek: DeepSeek V3.2$13.8661
67
84
55
82
131K
79
Anthropic: Claude Opus 4.5$450.0057
85
84
55
95
200K
87
NVIDIA: Nemotron Nano 9B V2$3.2075
72
84
85
98
131K
85
Z.ai: GLM 5V Turbo$88.0058
88
83
70
80
203K
84
Qwen: Qwen3 235B A22B Instruct 2507$3.8470
73
83
55
78
262K
79
OpenAI: GPT-5.2 Chat$210.0054
85
83
90
73
128K
81

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How it works

Methodology: How we rank Reasoning LLMs

Transparent, benchmark-driven rankings—same craft as our single-model deep dives.

What counts as “reasoning” in our ranking signals

Our reasoning rankings prioritize models that excel at complex logic, multi-step instruction following, and zero-shot problem solving. We heavily weight benchmarks like GPQA (Graduate-Level Google-Proof Q&A) and MATH, while also factoring in native extended thinking capabilities (such as OpenAI's o1-style Chain of Thought or DeepSeek's R1 reasoning tokens). We normalize these scores so engineering teams can directly compare the cognitive capabilities of frontier models against cost-optimized alternatives.

Battle Arena

Compare up to four LLMs side by side

Tick up to four models in the leaderboard table, then open Battle Arena for API pricing, benchmarks, and workload math in one view—perfect when you are shortlisting vendors for a pilot in the US, Canada, or Australia.

Prefer a head start? Jump into high-intent comparisons people search for every day—same interactive calculator, zero signup.

Open Battle ArenaUp to 4 models · Live estimates
Signals & spend

Value analysis

Benchmarks vs. estimated API cost—read the story your CFO cares about.

When to pay for a dedicated reasoning SKU

If your workload is mostly short chat, a flagship reasoning model may be overspend. If you run multi-step analysis, compliance checks, or agentic flows, the reasoning column helps justify the premium. Teams in Canada and Australia frequently validate latency and residency after shortlisting here; US teams often pair a reasoning core with a low-latency edge model.

Production deployment

Complex Logic & Agentic Workflows

How teams in the US, Canada, and Australia deploy these models in production.

Legal analysis, medical triage, and o1-style Chain of Thought

Models with native extended thinking (like OpenAI's o-series or DeepSeek's R1-class) excel at zero-shot problem solving that previously required complex agent orchestration. Enterprises in the US and Australia deploy these high-reasoning models for automated contract analysis, regulatory compliance auditing, and multi-step data extraction where logical accuracy is strictly prioritized over latency.

Architecture

Managing Reasoning Token Economics

Strategies to reduce monthly API spend without sacrificing capability.

Controlling max_thinking_tokens and fallback strategies

Because reasoning models bill internal 'thinking' tokens at the output rate, costs can spiral unpredictably. Optimize by setting strict `max_completion_tokens` limits, using prompt engineering to constrain unnecessary verbosity, and building fallback logic that routes simpler sub-tasks to cheaper, non-reasoning models. Use our deep-thinking toggle to forecast these exact token economics.

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Support & clarity

Frequently Asked Questions

Focused on teams in the United States, Canada, and Australia.

For autonomous agents or complex data extraction, models with native 'thinking' capabilities (like OpenAI o1 or DeepSeek R1) typically perform best. However, they bill 'thinking' tokens as output tokens, which can get expensive. Teams in the US, Canada, and Australia often use this leaderboard to find the optimal balance—selecting a dedicated reasoning model for complex tasks and routing simpler queries to a cheaper model.