Models

AI model ratings

Compare model fit by overall score, coding, reasoning, vision, context, speed and price/power. Values are demo data for MVP evaluation.

Model filters

Sort and narrow the demo model board by task profile.

Demo data

GPT-5.5

OpenAI

89.9

closedPremium128K tokens context

Coding95

Reasoning96

Agent Power94

Cost Efficiency72

Strengths

Strong tool use
Reliable reasoning
Broad workflow coverage

Weaknesses

Premium pricing
Best results need structured context

Best use cases

Full-stack codingAgent workflowsComplex planning

Claude Opus / Sonnet

Anthropic

89.6

closedHigh200K tokens context

Coding93

Reasoning95

Agent Power91

Cost Efficiency76

Strengths

Long-context synthesis
Code review
Document reasoning

Weaknesses

Can be cautious
Cost grows on long runs

Best use cases

Large codebasesSpecsCareful refactors

DeepSeek

87.7

openLow128K tokens context

Coding90

Reasoning89

Agent Power82

Cost Efficiency93

Strengths

Price/performance
Code generation
Reasoning value

Weaknesses

Vision not a core strength
Needs guardrails in agents

Best use cases

Cheap API usageCoding draftsBatch reasoning

Gemini

Google

86.4

closedMedium1M tokens context

Coding86

Reasoning90

Agent Power84

Cost Efficiency80

Strengths

Multimodal work
Long context
Fast analysis

Weaknesses

Coding consistency varies
Agent behavior depends on host

Best use cases

Vision tasksResearchLarge document scans

Qwen

Alibaba

85.7

openLow128K tokens context

Coding88

Reasoning86

Agent Power80

Cost Efficiency89

Strengths

Strong open-weight ecosystem
Multilingual work
Good cost profile

Weaknesses

Quality depends on deployment
Tool use needs testing per host

Best use cases

Local/private AIMultilingual codingCost-sensitive apps

Kimi

Moonshot AI

84.5

closedMedium1M tokens context

Coding84

Reasoning88

Agent Power81

Cost Efficiency86

Strengths

Long-context reading
Research synthesis
Structured output

Weaknesses

Agent ecosystem is thinner
Vision support is not central here

Best use cases

ResearchLong documentsKnowledge extraction

Mistral

Mistral AI

83.4

openMedium128K tokens context

Coding82

Reasoning84

Agent Power78

Cost Efficiency88

Strengths

Fast responses
European deployment options
Clean API ergonomics

Weaknesses

Not always top for deep codebase edits
Needs benchmark validation

Best use cases

Business automationFast assistantsEU-sensitive workflows

Llama

Meta / local hosts

79.8

localLow32K tokens context

Coding79

Reasoning78

Agent Power72

Cost Efficiency95

Strengths

Local deployment
Data control
Low marginal cost

Weaknesses

Needs hosting expertise
Lower top-end reasoning

Best use cases

Private AIOffline prototypesInternal assistants

Filtered table

Rank	Model	Provider	Intelligence	Coding	Agent Power	Speed	Cost Efficiency	Context	Overall Score
#1	GPT-5.5	OpenAI	96	95	94	78	72	128K tokens	89.9
#2	Claude Opus / Sonnet	Anthropic	95	93	91	82	76	200K tokens	89.6
#3	DeepSeek	DeepSeek	88	90	82	84	93	128K tokens	87.7
#4	Gemini	Google	91	86	84	88	80	1M tokens	86.4
#5	Qwen	Alibaba	86	88	80	86	89	128K tokens	85.7
#6	Kimi	Moonshot AI	87	84	81	83	86	1M tokens	84.5
#7	Mistral	Mistral AI	84	82	78	89	88	128K tokens	83.4
#8	Llama	Meta / local hosts	80	79	72	74	95	32K tokens	79.8