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- Claude Fable 5.1Bring research excerpts, code, logs or diagrams to Claude Fable 5.1. Connect the evidence, investigate possible causes and turn a complex question into a workable plan.
- Claude Haiku 5.5Give Claude Haiku 5.5 a passage, a set of short inputs or a focused question. Turn the material into a concise summary, a useful set of categories or a list of details you can check against the source.
- Claude Opus 4.6Use Claude Opus 4.6 to examine substantial source material, reason through a difficult change and shape a draft with care. Bring the context and the result you want, then refine the work in conversation.
- Claude Opus 5.5Turn ideas and source material into work you can use. Develop an article, reason through a decision, build a webpage or explore motion in code, then refine the result in conversation.
- Claude Sonnet 5.5Start with a clear goal and the material behind it. Use Claude Sonnet 5.5 to develop a structured document, discuss a visual reference or revise a draft while keeping the facts and voice you want to preserve.
- DeepSeek-V4.1-FlashTurn a rough brief, a code question or a set of notes into a focused draft. Define the task, provide the context and ask for the next useful step.
- Gemini 3.8 FlashConnect source material with a clear next step. Use Gemini 3.8 Flash to explain a document, compare details in a visual reference or turn a broad brief into a structured draft you can keep refining.
- Gemini 4 ArgonWork through a math problem, code design or technical document with Gemini 4 Argon. Add a chart or screenshot to connect visual details with your question.
- GPT-6.1 SolTurn a feature brief, code change or interface screenshot into a concrete next step with GPT-6.1 Sol. Connect the requirements, explain the tricky parts and draft implementation plans, tests or technical documents.
- GPT-6.1 TerraBring an image, a set of notes or an unfinished draft. Use GPT-6.1 Terra to explain the details that matter, organize your material and shape it into clear writing for the reader you have in mind.
- GPT-6 AstraBring a difficult question, the material behind it and the constraints that matter. GPT-6 Astra helps connect the evidence, weigh alternatives and turn the result into a clear brief you can keep refining.
- Grok 4.7Review code, explain a screenshot or work through a technical decision with Grok 4.7. Bring the relevant requirements and source material together, then turn the discussion into tests, a migration plan or a concise recommendation.
Create and edit images
4- GPT Image 2Create product images, posters, portraits and illustrations from a written brief or a reference image. Set the subject, style and composition, then refine the details that matter.
- GPT Image 2.5 SunburstChange the setting without losing the subject. GPT Image 2.5 Sunburst helps create and refine images when the details you want to preserve are as important as the changes you want to make.
- GPT Image 2.6 FlareTurn a post idea or reference image into an image, scalable SVG graphic, or animated GIF with GPT Image 2.6 Flare. Explore layouts and illustration styles, then describe what to change in the next version.
- GPT Image 2.6 SunburstCreate detailed images, scalable SVG artwork, and animated GIFs with GPT Image 2.6 Sunburst. Describe your subject, setting, light, and composition, or add reference images to develop campaign and editorial visuals.
Bring ideas into motion
2- Seedance 2.0Turn a written scene or a reference image into a moving sequence. Direct the subject, camera and atmosphere to explore character moments, illustrated worlds and visual stories.
- Seedance 2.5Create video scenes from a written brief or a reference image. Shape the action, camera movement and visual style, then refine each clip for the story you want to tell.
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.
| Model | Low | Medium | High |
|---|---|---|---|
| 97.50 | 97.73 | No complete result | |
| 95.76 | 98.59 | 99.55 | |
GPT-6 Luna | 97.90 | 100.00 | 99.32 |
| 83.23 | 95.17 | 97.17 | |
| 91.46 | 96.18 | 97.40 | |
| 80.93 | 87.10 | 80.58 | |
| 89.92 | No complete result | 95.95 | |
| 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 |
|---|---|
100.00 | |
100.00 | |
GPT-6 Luna | 99.00 |
99.00 | |
99.00 | |
99.00 | |
96.00 | |
95.00 | |
94.00 | |
93.00 |
Updated scoring criteria
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 |
|---|---|
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| |
| |
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GPT-6 Luna |
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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.
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
Compare AI writing results and task costs
Compare Writing Sense scores with average agent task costs from the published report.
Writing Sense vs. cost
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?
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?
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 |
Make the vase blue.