YouMind
Accedi

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.

18 models

Research and writing

12

Create and edit images

4

Bring ideas into motion

2

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
ModelLowMediumHigh
GPT-6.1 Sol
97.5097.73No complete result
GPT-6 Astra
95.7698.5999.55
GPT-6 Luna
97.90100.0099.32
Claude Opus 5.5
83.2395.1797.17
Claude Sonnet 5.5
91.4696.1897.40
Gemini 4 Argon
80.9387.1080.58
DeepSeek V4.1 Flash
89.92No complete result95.95
Gemini 3.8 Flash
0.0043.0765.36

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

How to use

  1. 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

  2. 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.

  3. 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.

Effort
Writing Sense vs. costo

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
ModelEffortWriting Sense score / 100Understanding the author's intent / 100Following the author's intent in writing / 100USD / task
Claude Opus 5.5High90.28100.0087.04$0.6035
GPT-6 AstraHigh86.80100.0082.40$1.7338
GPT-6.1 SolHigh77.2092.5972.07$0.4075
Gemini 4 ArgonHigh81.72100.0075.63$0.1955
Claude Sonnet 5.5High64.1366.6763.28$0.2787
Claude Opus 4.6High63.1177.7858.22$0.4546
Gemini 3.8 FlashHigh78.8577.7879.21$0.1393
DeepSeek V4.1 FlashHigh59.9662.9658.96$0.0407
GPT-6 LunaHigh48.0622.2256.68$0.0646
Claude Haiku 5.5High39.3133.3341.31$0.0227
GPT-6.1 TerraHigh73.1577.7871.61No comparable cost

Which AI models understand your source material?

Compare error rates and the choices behind them.

Over-cuttingPolished but wrongOther
ModelReading error rateAmong 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
ModelError rateOver-cuttingPolished but wrongOther
Gemini 4 Argon0.0%Not applicableNot applicableNot applicable
GPT-6 Astra4.9%25.0%75.0%0.0%
Claude Opus 5.56.2%80.0%20.0%0.0%
GPT-6.1 Sol8.6%14.3%71.4%14.3%
Claude Opus 4.622.2%50.0%0.0%50.0%
Gemini 3.8 Flash23.5%47.4%5.3%47.4%
Claude Sonnet 5.538.3%67.7%16.1%12.9%
DeepSeek V4.1 Flash45.7%62.2%13.5%24.3%
GPT-6 Luna71.6%19.0%62.1%19.0%
Claude Haiku 5.575.3%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.

ModelOutput 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.

ModelReadingWriting
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
ModelReadingWritingBreaks an author choice
Claude Opus 4.677.8%57.5%66.7%
Claude Haiku 5.524.7%48.6%No qualifying data
Claude Opus 5.593.8%84.9%44.4%
Claude Sonnet 5.561.7%64.1%85.2%
DeepSeek V4.1 Flash54.3%55.8%77.8%
Gemini 3.8 Flash76.5%72.7%46.3%
Gemini 4 Argon100.0%73.2%59.3%
GPT-6.1 Sol91.4%72.0%65.1%
GPT-6 Astra95.1%75.5%54.0%
GPT-6 Luna28.4%54.4%No qualifying data

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