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 5.1リサーチ抜粋、コード、ログ、フローチャートを Claude Fable 5.1 に提供し、証拠を整理して考えられる原因を分析し、複雑な課題を実行可能な計画に分解します。
Claude Haiku 5.5Claude Haiku 5.5 に文章、一連の短い入力、または特定の質問を与えてください。素材を簡潔な要約、有用なカテゴリーセット、またはソースと照合可能な詳細リストに変換します。
Claude Opus 4.6Claude Opus 4.6 で長い資料を読み解き、複雑なコードの変更を検討し、草稿を丁寧に推敲できます。背景情報と目指す結果を伝え、対話を通じて内容を磨いていきましょう。
Claude Opus 5.5アイデアやソース資料を実用的な成果物に変換します。記事の開発、意思決定の推論、Web ページの構築、またはコードでのモーション探索を行い、対話を通じて結果を洗練させます。
Claude Sonnet 5.5明確な目標と根拠となる資料から始めましょう。Claude Sonnet 5.5 を使用して構造化されたドキュメントを作成したり、視覚的な参考資料について議論したり、保持すべき事実やトーンを保ちながらドラフトを修正したりできます。
DeepSeek-V4.1-Flash粗いブリーフィング、コードの問題点、メモの集まりを、焦点を絞った下書きに変換しましょう。タスクを定義し、コンテキストを提供して、次のステップとして役立つアクションを依頼してください。
Gemini 3.8 Flash参考資料から、次に取る行動を明確に。Gemini 3.8 Flash で文書の内容を読み解き、画像の細部を比較し、大まかな依頼を構成の整った下書きにまとめて、対話しながら改善できます。
Gemini 4 ArgonGemini 4 Argon を活用して、数学的な問題を分解し、コード設計を検討したり、長い技術文書を読み解いたりしましょう。グラフやスクリーンショットを組み込むことで、問題の詳細をより明確に伝えることができます。
GPT-6.1 Sol機能要件、コードの変更点、UI スクリーンショットを GPT-6.1 Sol に渡して、次のステップを明確にしましょう。複雑なロジックを要件に基づいて説明し、実装計画、テストケース、または技術文書を作成します。
GPT-6 Astra難しい質問、その背景にある資料、重要な制約条件を持ち込みます。GPT-6 Astra は証拠を結びつけ、代替案を検討し、結果を継続して改善できる明確なブリーフに変換します。
Grok 4.7Grok 4.7 を使ってコードレビュー、スクリーンショットの説明、または難しい技術的トレードオフの整理を行いましょう。要件と関連資料を一箇所にまとめ、議論をテストチェックリスト、移行計画、簡潔な意思決定提案へと変換します。

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 Sense とコスト

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