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Interactive LLM Leaderboard 2026: Compare AI Models, API Cost & ROI

Compare 350+ LLMs in 2026: live API pricing, ROI scores, coding, reasoning, speed, and context. Built for teams in the US, Canada, and Australia evaluating OpenAI, Anthropic, Google Gemini, and open-weight models.

Why teams use this live LLM comparison table in 2026

This leaderboard blends normalized benchmarks with transparent estimated monthly API spend so you can shortlist models fast. Whether you procure from the United States, Canada, or Australia, you get one place to compare flagship chat models, long-context SKUs, and cost-optimized tiers—then jump into comparisons or embed the same data on your site.

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: Overall · Dot: provider color · Hover for rank, model & details

Full leaderboard

Showing 48 of 365 models.

PickModelEst. monthlyROI scoreCodingReasoningSpeedMathContextOverall
Anthropic: Claude Opus 4.7$450.0062
98
97
60
95
1.0M
97SWE-bench Verified at 87.6% maps to 98 coding. GPQA Diamond at 94.2% maps to 98 logic. Heavyweight Opus tier with 53 tok/s yields 60 speed. Native reasoning supported via OpenRouter reasoning parameter.
Google: Gemini 3.1 Pro Preview Custom Tools$200.0063
98
97
65
95
1.0M
97SWE-bench Verified at 80.6% maps to 98 coding. GPQA Diamond at 94.3% maps to 98 logic. As a flagship Pro model, it receives high multimodal (95) and math (95) scores, with standard heavyweight speed (65).
Anthropic: Claude Opus 4.5$450.0062
98
97
50
95
200K
97SWE-bench Verified at 80.9% maps to 98 coding. GPQA Diamond at 87.0% maps to 98 logic. MMMU at 80.7% yields 95 multimodal. As a heavyweight reasoning model with an effort parameter, speed is rated lower at 50.
OpenAI: GPT-5.2$210.0063
96
96
50
98
400K
97SWE-bench Verified 68.1% maps to 96 coding. GPQA 81.4% maps to 97 logic. MMMU 81.6% maps to 95 multimodal. Frontier reasoning model; speed estimated at 50.
Anthropic: Claude Opus 4.1$1,350.0061
98
97
45
95
200K
97SWE-bench Verified at 74.5% maps to near-perfect coding (98). GPQA Diamond at 80.9% dictates exceptional logic (98). As a flagship 'Opus' model with extended thinking, speed is lower (45). Vision price defaulted to frontier tier.
OpenAI: GPT-5.2 Pro$2,520.0061
98
97
50
95
400K
97SWE-bench Verified at 80.0% maps to 98 coding. GPQA Diamond at 93.2% maps to 98 logic. MMMU-Pro at 80.4% yields 95 multimodal. As a flagship reasoning model, speed is moderate (50).
DeepSeek: DeepSeek V3.2 Speciale$15.7970
95
97
45
96
164K
96GPQA 87.1% maps to 98 logic. LiveCodeBench 89.6% and Aider 88.0% map to 95 coding. HMMT 99.0% maps to 96 math. Flagship reasoning model with native thinking tokens; speed scored 45 due to extended reasoning overhead.
Qwen: Qwen3.6 Max Preview$104.0064
96
95
55
97
262K
96Based on Qwen3.6 Plus scoring SWE-bench 78.8% and GPQA 90.4%, Max (1T MoE flagship) maps to 96 for coding and logic. Native reasoning is supported via <think> tags. Vision defaults to frontier pricing.
Anthropic: Claude Opus 4.6 (Fast)$2,700.0060
98
96
70
95
1.0M
96SWE-bench Verified 82.1% maps to 98 coding. GPQA Diamond 88.5% maps to 96 logic. MATH 94.2% maps to 95 math. MMMLU 91.1% maps to 91 multimodal. Heavyweight tier with 60.5 tok/s yields 70 speed.
Claude Opus 4.6$450.0062
98
96
50
95
1.0M
96SWE-bench Verified at 82.1% maps to 98 coding. GPQA Diamond at 91.3% maps to 96 logic. MATH at 94.2% maps to 95 math. As a flagship reasoning model, speed is set to 50.
Anthropic Claude Sonnet Latest$270.0062
95
94
80
96
1.0M
95Claude 3.7 Sonnet scores SWE-bench Verified 72.7% (Coding: 95), GPQA Diamond 84.8% (Logic: 95), MATH 96.2% (Math: 96), IFEval 93.2% (Instruction: 93), MMMU 75% (Multimodal: 75). Mid-tier speed (~80). Features native extended thinking.
Qwen: Qwen3.5-122B-A10B$31.2067
95
94
45
95
262K
95SWE-bench Verified at 72.0% maps to 95 coding. GPQA Diamond at 86.6% maps to 96 logic. IFEval 92% maps to 92 instruction. As a native reasoning model, speed is mapped to 45.
OpenAI: o3 Pro$1,600.0060
98
92
40
99
200K
95SWE-bench Verified at 69.1% maps to 98 coding. GPQA Diamond at 83.3% maps to 98 logic. AIME 2024 at 91.6% maps to 99 math. As a flagship reasoning model, speed is lower (40).
Anthropic: Claude Opus 4.7 (Fast)$2,700.0060
98
97
85
90
1.0M
95SWE-bench Verified 87.6% maps to 98 coding. GPQA Diamond 94.2% maps to 98 logic. MATH 500 80.7% maps to 90 math. Fast-mode variant boosts speed to 85. Frontier tier defaults used for missing metrics.
Anthropic: Claude Sonnet 4.5$270.0062
98
94
80
94
1.0M
95SWE-bench Verified at 77.2% maps to 98 coding. Other benchmarks lack explicit scores in evidence, so logic, math, and instruction are inferred from its frontier Sonnet-class tier. Speed reflects balanced mid-tier latency.
Arcee AI: Trinity Large Thinking$17.3069
95
94
45
98
262K
95SWE-bench Verified at 63.2% maps to 95 coding. GPQA-Diamond at 76.3% maps to 95 logic. AIME 2025 at 96.3% maps to 98 math. As a heavy reasoning model (398B MoE), speed is mapped to 45.
DeepSeek: R1 0528$41.5066
92
94
45
98
164K
95GPQA 81.0% maps to 98 logic. AIME 2024 91.4% maps to 98 math. LiveCodeBench 73.3% maps to 92 coding. Speed reflects 287 c/s but heavy reasoning overhead (23K thinking tokens).
Google: Gemini 2.5 Pro Preview 06-05$150.0063
96
93
45
96
1.0M
95SWE-bench (59.6%) maps to 96 coding. GPQA (86.4%) maps to 96 logic. AIME (88.0%) maps to 96 math. MMMU (82.0%) maps to 90 multimodal. Flagship tier model with native reasoning; speed adjusted for thinking overhead.
OpenAI: GPT-5.4 Image 2$470.0061
95
96
60
95
272K
95GPT-5.4 scores 81.2% on MMMU-Pro (Multimodal ~90). As a frontier reasoning model with native computer-use (OSWorld 75%), Logic and Coding map to ~95. Speed is ~60 due to chain-of-thought overhead.
MoonshotAI: Kimi K2.5$35.0066
95
93
65
98
262K
95SWE-bench Verified 76.8% maps to 95 coding. GPQA Diamond 87.9% maps to 96 logic. AIME 2025 96.1% maps to 98 math. 1T MoE flagship tier; native reasoning supported.
OpenAI: GPT-5$150.0063
98
93
65
95
400K
95SWE-bench Verified 74.9% maps to 98 coding. MMLU 92.5% maps to 95 logic. MMMU 84.2% maps to 95 multimodal. Flagship tier speed estimated at 65.
OpenAI: o3 Mini$88.0064
95
93
65
98
200K
95SWE-bench Verified (69.1%) maps to 95 coding. GPQA (83.3%) maps to 92 logic. Despite being a 'Mini' tier model, its native reasoning capabilities yield flagship-level STEM scores, though speed is balanced for chain-of-thought generation.
OpenAI: o3 Deep Research$800.0061
98
92
35
98
200K
95SWE-bench Verified 69.1% maps to 98 coding. GPQA Diamond 83.3% maps to 98 logic. MMMU 82.9% maps to 95 multimodal. Speed is 35 due to extended reasoning times.
Z.ai: GLM 5.1$70.0064
98
93
65
97
203K
95SWE-bench Pro top score (GLM-5 had 77.8, +3.3 gain) maps to 98 coding. GPQA Diamond 86.2% maps to 95 logic. AIME 95.3% maps to 97 math. Heavyweight MoE tier yields 65 speed.
xAI: Grok 3 Beta$270.0061
92
93
50
98
131K
94GPQA Diamond 84.6% and AIME 93.3% (Think mode) map to near-max logic/math. LiveCodeBench 79.4% maps to high coding. MMMU 78% confirms strong vision. Flagship tier; speed inferred moderate due to reasoning.
Claude Sonnet 4.6$270.0061
95
94
80
92
1.0M
94SWE-bench Verified 79.6% maps to 95 coding. GPQA 89.9% maps to 95 logic. MMMLU 89.3% maps to 90 multimodal. Sonnet-class speed maps to 80.
Google: Gemini 2.5 Pro Preview 05-06$150.0062
95
93
45
95
1.0M
94Evidence notes it outperforms Claude 3.5 Sonnet on SWE-bench Verified, GPQA, and MMMU, though exact percentages are omitted. Mapped to frontier-level 95s for coding, logic, and multimodal. Speed is reduced due to mandatory native thought reasoning.
OpenAI: GPT-5.1-Codex-Max$150.0062
98
93
85
92
400K
94SWE-bench Verified at 77.9% maps to 98 coding. Speed of 84.20 tok/s maps to 85. As a flagship reasoning model, logic and math are inferred high (~92-95). Vision supported; price defaulted to frontier average.
o3$160.0062
96
91
40
98
200K
94SWE-bench Verified at 69.1% maps to 96 coding. GPQA Diamond at 87.7% maps to 97 logic. AIME 2024 at 96.7% maps to 98 math. Speed is 40 due to extended reasoning times.
NVIDIA: Nemotron 3 Ultra$45.0065
95
93
70
95
1.0M
94SWE-bench at 49.0% maps to 95 coding. MMLU at 89.1% maps to 90 logic. IFBench at 99.2% maps to 95 instruction. AIME25 at 89.1% maps to 95 math. Flagship 550B MoE tier with high throughput yields 70 speed.
OpenAI: GPT-5 Pro$1,800.0059
92
93
77
95
400K
93Evidence lacks exact GPT-5 Pro benchmark scores, so mapped from flagship reasoning tier (Pro/o1-class). Speed mapped from cited 77.4 tps. High coding/logic reflect its deep reasoning mode and 'most advanced model' status.
Qwen: Qwen3 Next 80B A3B Thinking$11.7070
90
93
45
97
262K
93GPQA at 77.2% maps to 96 logic. IFEval at 88.9% maps to 90 instruction. AIME 2025 at 87.8% maps to 97 math. As an 80B thinking model, speed is lower (45). No SWE-bench cited; coding estimated at 90.
OpenAI: GPT-5 Chat$150.0062
95
92
65
92
128K
93SWE-bench Verified 74.9% maps to 95 coding. MMLU 92.5% maps to 93 logic. MMMU 84.2% maps to 88 multimodal. Flagship tier model with native 'Thinking' mode and high reasoning capabilities.
OpenAI: o1$1,200.0059
88
93
40
98
200K
93SWE-bench Verified 48.9% maps to 88 coding. GPQA 78% maps to 96 logic. MMMU 77.6% maps to 85 multimodal. Speed is 40 due to extended reasoning times. Flagship tier.
DeepSeek: DeepSeek V3.1 Terminus$20.3067
95
93
70
92
164K
93SWE Verified 68.4% maps to 95 coding. GPQA-Diamond 80.7% maps to 95 logic. Flagship tier model with native thinking mode; no vision or caching evidence found.
OpenAI: GPT-5.4 Pro$3,000.0058
95
93
60
92
1.1M
93GPT-5.4 Pro lacks exact SWE-bench/GPQA scores but beats Gemini 3.1 Pro on SWE-Bench Pro and MMMU-Pro. Inferred as a flagship model: Coding ~95, Logic ~92 (GDPval 83%). Multimodal inferred ~85.
Qwen: Qwen3 Max$70.2063
96
91
55
95
262K
93SWE-bench Verified at 69.6% maps to 96 coding. SuperGPQA at 65.1% maps to 92 logic. AIME 2025 at 81.6% maps to 95 math. Flagship tier model; speed mapped to 55 based on 26 tok/s throughput.
Qwen: Qwen3 Max Thinking$70.2063
90
93
45
96
262K
93Evidence lacks exact Qwen3 Max Thinking scores but notes it's the flagship reasoning model. Inferred from Qwen2.5 72B (HumanEval 86.6%, GSM8K 95.8%) and VL 32B comparisons; mapped Coding to 90, Logic to 92. Speed reflects heavy reasoning tier.
DeepSeek: DeepSeek V4 Pro$26.1066
98
91
70
88
1.0M
92SWE-bench Verified at 81.0% maps to 98 coding. GPQA Diamond at 66.3% maps to 92 logic. Flagship MoE tier justifies high scores; speed is standard for large MoE.
Z.ai: GLM 4.5$46.0064
90
90
60
98
131K
92SWE-bench Verified 64.2% maps to 90 coding. GPQA 79.1% maps to 92 logic. MATH-500 98.2% maps to 98 math. Flagship MoE model with native thinking mode; speed mapped to 60 based on 41.5 tokens/s.
OpenAI: o1-pro$12,000.0057
85
93
40
98
200K
92GPQA 78.0% maps to 95 logic. SWE-bench Verified 48.9% maps to 85 coding. GSM8K 97.1% maps to 98 math. MMMU 77.6% maps to 85 multimodal. Speed is 40 due to heavy reasoning architecture.
Z.ai: GLM 4.7$33.5065
90
90
65
96
203K
92SWE-bench Verified 73.8 maps to 90 coding. GPQA Diamond 85.7 maps to 92 logic. IFEval 88.0 maps to 88 instruction. AIME 2025 95.7 maps to 96 math. Flagship tier speed estimated at 65.
Nex AGI: DeepSeek V3.1 Nex N1$10.4070
96
90
70
90
131K
92Flagship tier. SWE-bench Verified at 70.6 maps to 96 coding. BFCL v4 at 65.3 maps to 90 instruction. Logic and math inferred high (90) from flagship status. Speed set to 70 for heavy MoE.
Qwen: Qwen3.5-Flash$5.2075
88
92
95
95
1.0M
92SWE-bench Verified at 69.2% maps to 88 coding. GPQA Diamond at 84.2% maps to 92 logic. IFEval 91.9% maps to 92 instruction. As a Flash tier, speed is 95, though its reasoning capabilities rival flagship models.
Qwen: Qwen3.6 Plus$32.5065
95
91
70
92
1.0M
92OpenRouter docs cite a 78.8% on SWE-bench Verified, mapping to a 95 coding score. As a reasoning-enabled Plus MoE model, logic and math score highly (~92), while speed is balanced (~70). Multimodal capabilities are explicitly noted.
DeepSeek: DeepSeek V3.1$16.3068
90
93
75
92
164K
92GPQA Diamond at 74.9% maps to 95 logic. AIME 2025 at 49.8% maps to 92 math. SWE-bench Verified noted as strength, mapping to 90 coding. Flagship 671B MoE tier yields ~75 speed.
OpenAI: GPT-5 Image$500.0059
92
93
65
90
400K
92No raw benchmarks provided. Inferred scores based on GPT-5 flagship tier status and claims of major improvements in reasoning and code quality, mapping to frontier-level 90+ scores.
Qwen: Qwen3 235B A22B Instruct 2507$4.6076
92
93
70
92
262K
92SWE-bench Verified 55.6% maps to 92 coding. GPQA 77.5% maps to 95 logic. IFEval 93.3% maps to 90 instruction. Flagship 235B MoE tier yields 70 speed.

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Methodology: How we rank Overall LLMs

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

How overall scores and monthly estimates are combined

Overall rankings blend coding, reasoning, speed, math, and multimodal signals with transparent API pricing context. The composite reflects general-purpose fitness for teams that need one leaderboard to compare flagship models before deeper evaluations—whether you operate primarily in the US, Canada, or Australia.

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Value analysis

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

Reading ROI and cost together on the overall view

Overall value is not “the highest benchmark score”—it is capability per dollar for a workload you define. Use workload sliders to mirror production traffic; when you enable batch or cached pricing, we only apply discounts our data marks as supported so finance teams in the US, Canada, and Australia can trust the directional spend story before they negotiate contracts.

Production deployment

Enterprise AI Deployment & Use Cases

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

Matching models to production RAG and multi-agent systems

Modern enterprise architectures rarely rely on a single model. Teams in the US and Canada frequently deploy a 'router' pattern: routing simple queries to fast, cost-optimized models while reserving frontier flagships for complex reasoning or code generation. This leaderboard helps you map out that multi-model strategy by identifying the best-in-class models for each specific capability tier.

Architecture

API Cost & Architecture Optimization

Strategies to reduce monthly API spend without sacrificing capability.

Leveraging semantic caching and tiered routing

Optimizing your AI architecture means looking beyond the base token price. By implementing semantic caching at the edge, utilizing provider-level prompt caching for large context windows, and shifting asynchronous workloads to Batch APIs, organizations in Australia and the US routinely cut their monthly LLM spend by 40-60%. Use the toggles above to simulate these architectural savings.

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Frequently Asked Questions

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

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