A model for every idea

Explore AI models for research, writing, image editing and video generation. Compare measured performance and task costs to find a model for your work.

17 models

Research and writing

Claude Fable 5.1연구 발췌문, 코드, 로그 또는 흐름도를 Claude Fable 5.1에 전달하여 증거를 정리하고 가능한 원인을 분석하며 복잡한 문제를 실행 가능한 계획으로 분해하세요.
Claude Haiku 5.5Claude Haiku 5.5에 지문, 짧은 입력 세트 또는 특정 질문을 제공하세요. 자료를 간결한 요약, 유용한 카테고리 세트 또는 원본과 대조할 수 있는 세부 정보 목록으로 변환합니다.
Claude Opus 4.6Claude Opus 4.6을 활용하여 방대한 원본 자료를 검토하고, 복잡한 변경 사항을 추론하며, 세심하게 초안을 작성하세요. 필요한 맥락과 원하는 결과를 제시한 후, 대화를 통해 작업을 다듬어 나가세요.
Claude Opus 5.5아이디어와 원본 자료를 유용한 결과물로 변환하세요. 기사를 작성하고, 의사결정을 추론하며, 웹사이트를 구축하거나 코드로 모션을 탐색한 후, 대화 속에서 결과를 다듬을 수 있습니다.
Claude Sonnet 5.5명확한 목표와 그 근거가 되는 자료를 바탕으로 시작하세요. Claude Sonnet 5.5를 활용해 구조화된 문서를 작성하고, 시각적 레퍼런스를 논의하거나 초고를 수정하되, 유지하고자 하는 사실과 어조를 그대로 보존할 수 있습니다.
DeepSeek-V4.1-Flash대략적인 브리핑, 코드 문제 또는 메모 세트를 집중된 초안으로 변환하세요. 작업을 정의하고, 맥락을 제공하며, 다음 단계에서 유용한 조치를 요청하세요.
Gemini 3.8 Flash참고 자료를 바탕으로 다음에 할 일을 구체화하세요. Gemini 3.8 Flash로 문서 내용을 이해하고 이미지의 세부 사항을 비교하거나, 폭넓은 요청을 체계적인 초안으로 정리한 뒤 대화를 이어가며 다듬을 수 있습니다.
Gemini 4 ArgonGemini 4 Argon으로 수학 문제를 분해하고, 코드 솔루션을 설계하거나, 긴 기술 문서를 읽어보세요. 차트와 스크린샷을 함께 활용하면 문제의 세부 사항을 더 명확하게 전달할 수 있습니다.
GPT-6.1 Sol기능 요구사항, 코드 변경 사항 또는 UI 스크린샷을 GPT-6.1 Sol에게 전달하여 다음 단계로 진행할 계획을 세우세요. 복잡한 로직을 요구사항과 함께 설명하고, 이를 바탕으로 구현 계획, 테스트 케이스 또는 기술 문서를 작성합니다.
GPT-6 Astra어려운 질문과 그 배경 자료, 그리고 중요한 제약 조건을 제시하세요. GPT-6 Astra는 증거를 연결하고 대안을 검토하여 지속적으로 다듬을 수 있는 명확한 브리프로 결과를 정리해 드립니다.
Grok 4.7Grok 4.7을 사용하여 코드를 리뷰하고, 스크린샷을 해석하거나, 어려운 기술적 트레이드오프를 정리하세요. 요구 사항과 관련 자료를 함께 입력하여 테스트 체크리스트, 마이그레이션 계획 또는 명확한 의사결정 제안으로 논의를 구조화할 수 있습니다.

Create and edit images

Bring ideas into motion

Compare AI models

AI model research performance

Compare the factual accuracy of AI models when they research information and answer questions. Scores are out of 100; a higher score means more accurate answers on this set of tasks.

Factuality / 100
Model	Low	Medium	High
GPT-6.1 Sol	97.50	97.73	No complete result
GPT-6 Astra	95.76	98.59	99.55
GPT-6 Luna	97.90	100.00	99.32
Claude Opus 5.5	83.23	95.17	97.17
Claude Sonnet 5.5	91.46	96.18	97.40
Gemini 4 Argon	80.93	87.10	80.58
DeepSeek V4.1 Flash	89.92	No complete result	95.95
Gemini 3.8 Flash	0.00	43.07	65.36

GPT-6 Astra scored 99.55 for factual accuracy in the completed high-effort research evaluations shown here.

AI model writing performance

Compare how well AI writing models complete content creation tasks and deliver the requested results. Scores are out of 100 and measure task completion rather than article quality.

Original scoring criteria

Model	Task fulfillment / 100
GPT-6.1 Sol	100.00
GPT-6 Astra	100.00
GPT-6 Luna	99.00
Claude Opus 5.5	99.00
Claude Sonnet 5.5	99.00
Gemini 4 Argon	99.00
Claude Haiku 5.5	96.00
DeepSeek V4.1 Flash	95.00
Gemini 3.8 Flash	94.00
Claude Opus 4.6	93.00

Updated scoring criteria

Updated scoring criteria
Model	Low	Medium	High
GPT-6.1 Terra	99.72	100.00	100.00

AI model feature-use performance

Use tasks such as researching information, writing documents and creating slides to check whether AI models choose the right features and follow instructions. Scores measure feature use, not the quality of the finished articles, images or slides.

Writing
Research
Images
Slides
Webpages

Model	Feature-use score / 100Effort
Gemini 4 Argon	Low 92.00 Medium 92.00 High 92.00
GPT-6.1 Terra	Low 84.67 Medium 82.00 High 82.00
Claude Haiku 5.5	Medium 92.00
GPT-6.1 Sol	Low 92.00 Medium 87.00 High 87.00
GPT-6 Luna	High 100.00
Claude Sonnet 5.5	Medium 77.71
DeepSeek V4.1 Flash	High 98.57
Gemini 3.8 Flash	Medium 92.00

Results with different scoring rules are shown in separate groups.

How to use

Choose your model
Choose an AI model for research, article writing or summarizing content. Switch between GPT, Claude, Gemini and other models in YouMind to explore different approaches.
Choose a model
Selected: GPT-6.1 Sol
Bring your context
Add PDFs, documents, images or notes, then describe your task. Ask AI to use your reference material to summarize key points, create an article outline or draft content.
Review and refine
Rewrite AI-generated text, refine the wording or select an image area for a local edit. Keep the parts you like and use specific instructions to refine the article or image.

Our Writing Sense analysis

Read the full evaluation

Compare AI writing results and task costs

Compare Writing Sense scores with average agent task costs from the published report.

EffortHighMediumLowAll

Writing Sense와 비용

In the published report's high-effort configurations, Claude Opus 5.5 scored 90.28 for Writing Sense at an average cost of $0.6035 per task. Gemini 3.8 Flash scored 78.85 at $0.1393 per task.

Higher effort did not always produce a higher score on these tasks. GPT-6.1 Sol scored 80.26 for Writing Sense at medium effort and 77.20 at high effort.

View data table

Model	Effort	Writing Sense score / 100	Understanding the author's intent / 100	Following the author's intent in writing / 100	USD / task
Claude Opus 5.5	High	90.28	100.00	87.04	$0.6035
GPT-6 Astra	High	86.80	100.00	82.40	$1.7338
GPT-6.1 Sol	High	77.20	92.59	72.07	$0.4075
Gemini 4 Argon	High	81.72	100.00	75.63	$0.1955
Claude Sonnet 5.5	High	64.13	66.67	63.28	$0.2787
Claude Opus 4.6	High	63.11	77.78	58.22	$0.4546
Gemini 3.8 Flash	High	78.85	77.78	79.21	$0.1393
DeepSeek V4.1 Flash	High	59.96	62.96	58.96	$0.0407
GPT-6 Luna	High	48.06	22.22	56.68	$0.0646
Claude Haiku 5.5	High	39.31	33.33	41.31	$0.0227
GPT-6.1 Terra	High	73.15	77.78	71.61	No comparable cost

Which AI models understand your source material?

Compare error rates and the choices behind them.

Over-cuttingPolished but wrongOther

Model	Reading error rate	Among wrong answers
	0.0%	No errors in this sample
	4.9%	Over-cutting: 25.0%Polished but wrong: 75.0%Other: 0.0%
	6.2%	Over-cutting: 80.0%Polished but wrong: 20.0%Other: 0.0%
	8.6%	Over-cutting: 14.3%Polished but wrong: 71.4%Other: 14.3%
	22.2%	Over-cutting: 50.0%Polished but wrong: 0.0%Other: 50.0%
	23.5%	Over-cutting: 47.4%Polished but wrong: 5.3%Other: 47.4%
	38.3%	Over-cutting: 67.7%Polished but wrong: 16.1%Other: 12.9%
	45.7%	Over-cutting: 62.2%Polished but wrong: 13.5%Other: 24.3%
	71.6%	Over-cutting: 19.0%Polished but wrong: 62.1%Other: 19.0%
	75.3%	Over-cutting: 26.2%Polished but wrong: 39.3%Other: 32.8%

View data table

Model	Error rate	Over-cutting	Polished but wrong	Other
Gemini 4 Argon	0.0%	Not applicable	Not applicable	Not applicable
GPT-6 Astra	4.9%	25.0%	75.0%	0.0%
Claude Opus 5.5	6.2%	80.0%	20.0%	0.0%
GPT-6.1 Sol	8.6%	14.3%	71.4%	14.3%
Claude Opus 4.6	22.2%	50.0%	0.0%	50.0%
Gemini 3.8 Flash	23.5%	47.4%	5.3%	47.4%
Claude Sonnet 5.5	38.3%	67.7%	16.1%	12.9%
DeepSeek V4.1 Flash	45.7%	62.2%	13.5%	24.3%
GPT-6 Luna	71.6%	19.0%	62.1%	19.0%
Claude Haiku 5.5	75.3%	26.2%	39.3%	32.8%

How much do AI models write, and do they use the right material?

See how much each model writes and how much unwanted material it uses.

Model	Output lengthRelative to the author’s original 1×2×	Distractor useDistracting / required material 0×1×
	1.36×	0.34×
	1.73×	0.41×
	1.50×	0.49×
	1.73×	0.58×
	1.66×	0.59×
	1.68×	0.62×
	1.74×	0.66×
	1.31×	0.82×
	1.82×	0.86×
	1.97×	0.99×

Can AI models write what you mean after understanding it?

Does reading well mean writing well? Compare model rankings in both tasks.

Model	Reading	Writing
	Rank#1100.0%	Rank#373.2%
	Rank#295.1%	Rank#275.5%
	Rank#393.8%	Rank#184.9%
	Rank#491.4%	Rank#572.0%
	Rank#577.8%	Rank#757.5%
	Rank#676.5%	Rank#472.7%
	Rank#761.7%	Rank#664.1%
	Rank#854.3%	Rank#855.8%
	Rank#928.4%	Rank#954.4%
	Rank#1024.7%	Rank#1048.6%

When correct reading still leads to a wrong turn

Among the articles a model read correctly every time, how many still drifted from the author’s intent when rewritten?

44.4%

46.3%

54.0%

59.3%

65.1%

66.7%

77.8%

85.2%

View data table

Model	Reading	Writing	Breaks an author choice
Claude Opus 4.6	77.8%	57.5%	66.7%
Claude Haiku 5.5	24.7%	48.6%	No qualifying data
Claude Opus 5.5	93.8%	84.9%	44.4%
Claude Sonnet 5.5	61.7%	64.1%	85.2%
DeepSeek V4.1 Flash	54.3%	55.8%	77.8%
Gemini 3.8 Flash	76.5%	72.7%	46.3%
Gemini 4 Argon	100.0%	73.2%	59.3%
GPT-6.1 Sol	91.4%	72.0%	65.1%
GPT-6 Astra	95.1%	75.5%	54.0%
GPT-6 Luna	28.4%	54.4%	No qualifying data