Best AI Models in 2025: Complete API Model Rankings

The AI model landscape has fundamentally changed. We're no longer in the "OpenAI versus everyone else" era. Frontier models from OpenAI, Anthropic, Google, and xAI now achieve 70-80%+ on SWE-bench Verified (solving real GitHub issues) and 90%+ on MMLU (general intelligence).

More significantly, open-source alternatives like DeepSeek-V3 now match GPT-4o performance at $0.27 per million input tokens versus $2.50—a 10x cost reduction with no capability compromise.

This creates both opportunity and complexity. The best model for your use case isn't necessarily the most expensive or the newest. It's the one that balances capability, cost, latency, and reliability for your specific tasks.

# COMPLETE AI MODELS COMPARISON

guest@theairankings:~$cat ai_models.txt --all

RANK	MODEL	PROVIDER	LICENSE	SCORE	SWE	MMLU	CTX	IN $/M	OUT $/M
[01]	GPT-5.2	OpenAI	Prop.	85.8	80%	91.5%	400K	$1.75	$14
[02]	Claude Opus 4.5	Anthropic	Prop.	84.2	80.9%	87.4%	200K	$5	$25
[03]	Gemini 3 Pro Preview	Google	Prop.	84	76.2%	91.8%	1M	$2	$12
[04]	GPT-5.1	OpenAI	Prop.	83.9	76.3%	91.5%	400K	$1.25	$10
[05]	Claude Sonnet 4.5	Anthropic	Prop.	83.2	77.2%	89.1%	200K	$3	$15
[06]	GPT-5	OpenAI	Prop.	83.2	74.9%	91.4%	400K	$1.25	$10
[07]	o3	OpenAI	Prop.	81.2	69.1%	93.3%	200K	$2	$8
[08]	Kimi K2 Thinking	Moonshot AI	Open	80.4	71.3%	89.5%	256K	—	—
[09]	Grok 4	xAI	Prop.	80.05	73.5%	86.6%	256K	$3	$15
[10]	Claude Opus 4	Anthropic	Prop.	80	72.5%	87.4%	200K	$15	$75
[11]	o4-mini	OpenAI	Prop.	79.2	68.1%	90.3%	200K	$1.1	$4.4
[12]	Claude Sonnet 4	Anthropic	Prop.	79.1	72.7%	85.4%	200K	$3	$15
[13]	Kimi K2	Moonshot AI	Open	77.7	65.8%	89.5%	128K	—	—
[14]	Gemini 2.5 Pro	Google	Prop.	75.6	61.7%	89.5%	1M	$1.25	$10
[15]	GPT-4.1	OpenAI	Prop.	72.4	54.6%	90.2%	1M	$2	$8
[16]	o1	OpenAI	Prop.	70.4	48.9%	91.8%	200K	$15	$60
[17]	Claude 3.5 Sonnet	Anthropic	Prop.	68.9	49%	88.7%	200K	$3	$15
[18]	DeepSeek-R1	DeepSeek	Open	67	49%	85%	128K	$0.55	$2.19
[19]	Grok 3	xAI	Prop.	64.9	N/A	92.7%	1M	$3	$15
[20]	DeepSeek-V3	DeepSeek	Open	64.5	42%	87%	128K	$0.27	$1.1
[21]	Claude 3.5 Haiku	Anthropic	Prop.	62.8	40.6%	85%	200K	$0.8	$4
[22]	Claude Haiku 4.5	Anthropic	Prop.	62.3	73.3%	N/A	200K	$1	$5
[23]	Claude 3 Opus	Anthropic	Prop.	62.4	38%	86.8%	200K	$15	$75
[24]	Llama 3.1 405B	Meta	Open	61.4	N/A	87.7%	128K	—	—
[25]	GPT-4.1 Mini	OpenAI	Prop.	61.3	N/A	87.5%	1M	$0.4	$1.6
[26]	Gemini 2.5 Flash	Google	Prop.	61.3	37.5%	85%	1M	$0.3	$2.5
[27]	Qwen 2.5 72B	Alibaba	Open	60.3	N/A	86.1%	128K	—	—
[28]	Llama 3.3 70B	Meta	Open	60.2	N/A	86%	128K	—	—
[29]	Gemini 2.0 Flash	Google	Prop.	60.2	N/A	86%	1M	$0.1	$0.4
[30]	Llama 3.2 90B Vision	Meta	Open	60.2	N/A	86%	128K	—	—
[31]	GLM-4.6	Zhipu AI	Prop.	60.2	N/A	86%	200K	—	—
[32]	o1-mini	OpenAI	Prop.	59.6	N/A	85.2%	128K	$3	$12
[33]	GPT-4o	OpenAI	Prop.	59.5	33.2%	85.7%	128K	$2.5	$10
[34]	Qwen 2.5 14B	Alibaba	Open	59.5	N/A	85%	128K	—	—
[35]	Phi-4	Microsoft	Open	59.4	N/A	84.8%	16K	—	—
[36]	Llama 3.1 70B	Meta	Open	58.8	N/A	84%	128K	—	—
[37]	Yi-Large	01.AI	Prop.	58.1	N/A	83%	32.8K	$2.7	$2.7
[38]	Mistral Large 3	Mistral AI	Open	57.8	N/A	82.5%	128K	$2	$6
[39]	GPT-4o Mini	OpenAI	Prop.	57.4	N/A	82%	128K	$0.15	$0.6
[40]	Mistral Small 3.2	Mistral AI	Open	56.7	N/A	81%	128K	$0.1	$0.3
[41]	GPT-4.1 Nano	OpenAI	Prop.	56.1	N/A	80.1%	1M	$0.1	$0.4
[42]	Qwen 2.5 32B	Alibaba	Open	55.8	N/A	79.7%	128K	—	—
[43]	Phi-3.5 MoE	Microsoft	Open	55.2	N/A	78.9%	128K	—	—
[44]	Yi-1.5 34B	01.AI	Open	53.9	N/A	77%	32K	—	—
[45]	Claude 3 Haiku	Anthropic	Prop.	52.6	N/A	75.2%	200K	$0.25	$1.25
[46]	Qwen 2.5 7B	Alibaba	Open	51.9	N/A	74.2%	128K	—	—
[47]	Llama 3.2 11B Vision	Meta	Open	51.1	N/A	73%	128K	—	—
[48]	GLM-4 9B Chat	Zhipu AI	Open	50.4	N/A	72%	128K	—	—
[49]	Llama 3.1 8B	Meta	Open	48.6	N/A	69.4%	128K	—	—
[50]	Grok Code Fast 1	xAI	Prop.	25.1	29.5%	N/A	N/A	$0.2	$1.5

Prices per million tokens. "—" = open-source models available via inference providers (GROQ, Together AI, Fireworks) at varying rates. License: Prop. = Proprietary, Open = Open-source.

# BEST MODELS BY CATEGORY

Best for SWE-bench Verified (Real-World Coding)

SWE-bench tests ability to solve actual GitHub issues from popular repositories. This is the hardest benchmark and the best predictor of production coding capability.

RANK	MODEL	SCORE	PROVIDER	PRICE (IN/OUT)
[01]	Claude Opus 4.5	80.9%	Anthropic	$5.00 / $25.00
[02]	GPT-5.2	80%	OpenAI	$1.75 / $14.00
[03]	Claude Sonnet 4.5	77.2%	Anthropic	$3.00 / $15.00
[04]	GPT-5.1	76.3%	OpenAI	$1.25 / $10.00
[05]	Gemini 3 Pro Preview	76.2%	Google	$2.00 / $12.00

Why this matters: If you're building coding assistants, CI/CD automation, or anything involving code generation, these are your options. GPT-5.2 now matches Claude Opus 4.5 at the top, with significantly lower pricing.

Best for MMLU (General Intelligence)

MMLU tests knowledge across 57 subjects from elementary to professional level. Top models are now saturated at 91%+ (human expert baseline is 89.8%).

RANK	MODEL	SCORE	PROVIDER
[01]	Grok 4	93.5%	xAI
[02]	o3 (high)	93.3%	OpenAI
[03]	Grok 3	92.7%	xAI
[04]	Gemini 3 Pro Preview	91.8%	Google
[05]	GPT-5.2	91.5%	OpenAI

Why this matters less than you think: MMLU has a 6.5% error rate in questions. Performance above 90% is near the ceiling. The benchmark no longer differentiates top models—focus on SWE-bench for real capability differences.

# BEST VALUE MODELS

Budget Tier ($0.10-$0.50 per MTok input)

These models deliver strong performance at a fraction of premium pricing.

MODEL	SCORE	PRICE (IN/OUT)	BEST FOR
DeepSeek-V3	64.5	$0.27 / $1.10	GPT-4o-class at 10x lower cost, MIT license
Gemini 2.5 Flash	61.3	$0.30 / $2.50	1M context, hybrid reasoning
Gemini 2.0 Flash	60.2	$0.10 / $0.40	Maximum efficiency, 1M context
Mistral Small 3.2	56.7	$0.10 / $0.30	92.9% HumanEval coding, Apache 2.0
GPT-4.1-nano	56.1	$0.10 / $0.40	1M context, OpenAI ecosystem

The standout: DeepSeek-V3 matches GPT-4o on many tasks at 1/10th the cost. Open-source (MIT license) means you can self-host if volume justifies it.

Mid-Tier ($1.00-$3.00 per MTok input)

Where most production applications should live—the sweet spot of capability and cost.

MODEL	SCORE	PRICE (IN/OUT)	BEST FOR
GPT-5.2	85.8	$1.75 / $14.00	#1 overall, 80% SWE-bench, 400K context
Gemini 3 Pro	84	$2.00 / $12.00	#1 on LMArena, 1M+ context
GPT-5.1	83.9	$1.25 / $10.00	Best value frontier, 76.3% SWE-bench
Claude Sonnet 4.5	83.2	$3.00 / $15.00	Best coding value, 77.2% SWE-bench
o4-mini	79.2	$1.10 / $4.40	Best reasoning value

The recommendation: For most applications, Claude Sonnet 4.5 hits the sweet spot. Best coding performance in this tier, 200K context (1M in beta), extended thinking mode, computer use capabilities. At $3/$15, it's 67% cheaper than Opus 4.5 while delivering 95% of the capability.

# SPECIAL CAPABILITIES

Longest context windows

Llama 4 Scout: 10 million tokens (multimodal, open-weight)
Gemini 3 Pro: 1-2M tokens (production-ready)
GPT-4.1: 1M tokens (all variants)
Grok 4.1 Fast: 2M tokens ($0.20/$0.50)

Best computer use (autonomous control)

Claude Sonnet 4.5: 61.4% OSWorld (SOTA)
Gemini 2.5 Computer Use Preview
Claude Opus 4.5: Extended sessions (30+ hours)

Best reasoning models

o3 / o3-pro (OpenAI)
DeepSeek-R1 (open-source, MIT)
Gemini Deep Think
Kimi K2 Thinking (256K context, open-weight)

Best for self-hosting

DeepSeek-V3 / R1 (MIT license, competitive performance)
Llama 4 family (Llama License, 700M MAU limit)
Qwen 2.5 family (Apache 2.0, strong multilingual)
Mistral family (Apache 2.0 / Research License)

Best multimodal (vision + text)

Gemini 3 Pro (native multimodal, 1hr+ video understanding)
GPT-5 (vision, audio I/O, DALL-E integration)
Claude Opus 4.5 (vision, extended thinking with images)
Qwen2.5-VL-72B (open-weight, GPT-4o competitive)

Models with real-time data

Grok 4 (X/Twitter integration, up-to-the-minute posts)
Gemini (Google Search grounding, cited sources)

# UNDERSTANDING THE BENCHMARKS

SWE-bench Verified: The Gold Standard for Coding

What it tests: 500 real-world GitHub issues from popular Python repositories (Django, scikit-learn, matplotlib). The model must understand the issue, locate relevant code, implement a fix, and ensure all tests pass.

Why it's hard: This isn't "write a function that sorts an array." It's "here's a bug report from an actual user—fix the production codebase without breaking anything else."

Current leaders: Claude Opus 4.5 (80.9%), GPT-5.2 (80.0%), Claude Sonnet 4.5 (77.2%)

MMLU: The General Intelligence Baseline

What it tests: 14,042 multiple-choice questions across 57 subjects—elementary mathematics to professional medicine.

Why it matters less now: Top models are saturated at 91-93%, near the human expert baseline of 89.8%. The benchmark has a 6.5% error rate. Beyond 90%, you're measuring benchmark quirks more than model capability.

The insight: MMLU is now a threshold test, not a differentiator. If a model scores <85%, it's not frontier-class. If it scores >90%, look at other benchmarks.

# OUR RANKING METHODOLOGY

The Composite Score Formula

Both benchmarks available: (SWE-bench + MMLU) / 2

Only MMLU available: MMLU × 0.7 (30% penalty for missing real-world coding)

Only SWE-bench available: SWE-bench × 0.85 (15% penalty for missing general intelligence)

Why this weighting: SWE-bench is the hardest, most production-relevant benchmark. Missing it is a bigger red flag than missing MMLU. Most open-source models only report MMLU scores because SWE-bench testing is expensive—this methodology honestly reflects that gap.

# HOW TO CHOOSE THE RIGHT MODEL

Start with Task Complexity

Simple Tasks (80% of applications)

Summarization, classification, basic Q&A, simple code generation

Use: DeepSeek-V3, Gemini Flash, GPT-4.1-nano

These handle routine tasks at 1/10th the cost of premium options.

Medium Complexity (15% of tasks)

Multi-step reasoning, complex code generation, research synthesis

Use: Claude Sonnet 4.5, Gemini 3 Pro, o4-mini

Sweet spot of capability and cost for complex work.

Maximum Complexity (5% of tasks)

Novel research, large-scale refactoring, multi-day autonomous agents

Use: Claude Opus 4.5, o3-pro, Gemini Deep Think

When correctness matters more than cost.

The routing strategy: Most production applications should use multiple models—route simple tasks to cheap models, complex tasks to expensive ones. RouteLLM achieves 85% cost reduction while maintaining 95% of GPT-4 quality.

# FREQUENTLY ASKED QUESTIONS

What is the best AI model overall?

GPT-5.2 leads our composite ranking with 85.8, combining 80.0% SWE-bench with 91.5% MMLU and a 400K context window. For most users, GPT-5.1 offers excellent value at $1.25/$10.00 per million tokens.

What is the best AI model for coding?

Claude Opus 4.5 (80.9% SWE-bench) narrowly leads GPT-5.2 (80.0%) on SWE-bench Verified, but GPT-5.2 achieves state-of-the-art on SWE-bench Pro (55.6%). For budget-conscious developers, Claude Haiku 4.5 achieves 73.3% SWE-bench at just $1/$5 per million tokens.

What is the best free AI model?

DeepSeek-V3 offers GPT-4o-class performance at $0.27/$1.10 per million tokens with an MIT license for self-hosting. Gemini 2.0 Flash provides excellent value at $0.10/$0.40 with 1M context.

What is the difference between AI apps and AI models?

AI apps (ChatGPT, Claude.ai) are consumer interfaces with monthly subscriptions. AI models are the underlying engines accessed via API, priced per token. Developers use models directly; consumers use apps.

Should I use open-source or proprietary models?

For most applications, proprietary APIs (Claude, GPT, Gemini) offer better reliability and support. Open-source (DeepSeek, Llama, Qwen) makes sense for high-volume applications (50M+ tokens/day) or strict data privacy requirements.

What do SWE-bench and MMLU scores mean?

SWE-bench Verified tests real-world coding ability (solving actual GitHub issues). MMLU tests general knowledge across 57 subjects. We weight both equally, with penalties for missing benchmarks.

How often do you update these rankings?

Monthly. The AI model landscape changes rapidly—new releases, price changes, and benchmark updates happen constantly. Check back regularly for the latest data.

guest@theairankings:~$_