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text-to-3d-with-llm-code

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Sync Text-to-3D with LLM code

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README.md ADDED
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+ ---
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: data/train-*
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+ license: 'cc-by-4.0'
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+ task_categories:
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+ - text-to-video
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+ language:
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+ - en
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+ size_categories:
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+ - 10K<n<100K
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+ pretty_name: 'Text-to-3D with LLM code'
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+ tags:
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+ - 'rapidata'
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+ - 'benchmark'
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+ - 'llm-evaluation'
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+ - 'text-to-3d'
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+ - 'with'
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+ - 'llm'
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+ - 'code'
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+ dataset_info:
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+ features:
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+ - name: prompt
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+ dtype: string
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+ - name: video1
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+ dtype: string
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+ - name: video2
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+ dtype: string
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+ - name: model1
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+ dtype: string
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+ - name: model2
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+ dtype: string
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+ - name: weighted_results_video1_creativity
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+ dtype: float32
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+ - name: weighted_results_video2_creativity
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+ dtype: float32
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+ - name: detailed_results_creativity
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+ dtype: string
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+ - name: weighted_results_video1_coherence
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+ dtype: float32
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+ - name: weighted_results_video2_coherence
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+ dtype: float32
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+ - name: detailed_results_coherence
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+ dtype: string
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+ - name: weighted_results_video1_alignment
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+ dtype: float32
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+ - name: weighted_results_video2_alignment
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+ dtype: float32
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+ - name: detailed_results_alignment
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+ dtype: string
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+ - name: weighted_results_video1_preference
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+ dtype: float32
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+ - name: weighted_results_video2_preference
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+ dtype: float32
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+ - name: detailed_results_preference
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+ dtype: string
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+ ---
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+
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+ # Rapidata Text-to-3D with LLM code Benchmark
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+
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+ Built by [Rapidata](https://www.rapidata.ai), collected with the [Rapidata Python SDK](https://docs.rapidata.ai).
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+
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+ To evaluate your own models and create a leaderboard, check out our [MRI](https://docs.rapidata.ai/mri/).
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+
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+ ## Live Leaderboard
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+
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+ Explore the full interactive leaderboard — filter by model, question and prompt, and inspect individual head-to-head matchups on Rapidata.
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+
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+ **[Open the live leaderboard](https://app.rapidata.ai/mri/benchmarks/bmk_1YVpXgN69Mv8I9)**
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+
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+ ## At a glance
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+
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+ | | |
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+ | --- | --- |
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+ | Head-to-head comparisons (rows) | **<!-- auto:rows -->44,107<!-- /auto:rows -->** |
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+ | Human votes | **<!-- auto:votes -->693,224<!-- /auto:votes -->** |
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+ | Models compared | **<!-- auto:models -->14<!-- /auto:models -->** |
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+ | Prompts | **<!-- auto:prompts -->500<!-- /auto:prompts -->** |
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+
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+ ## The leaderboards
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+
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+ | Leaderboard | Question shown to annotators | Prompt shown? |
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+ | --- | --- | --- |
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+ | **Creativity** | Which object is more creative and interesting? | No |
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+ | **Coherence** | Which 3D model has more errors, like floating, misplaced or broken parts? | No |
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+ | **Alignment** | Which 3D model matches the description better? | Yes |
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+ | **Preference** | Which 3D model looks better? | No |
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+
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+ ## Dataset structure
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+
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+ One row per head-to-head comparison of two samples generated from the same prompt.
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+
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+ | Column | Type | Meaning |
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+ | --- | --- | --- |
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+ | `prompt` | string | The prompt the two compared samples were generated from. |
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+ | `video1` | string | Compared video 1: public URL of the video. |
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+ | `video2` | string | Compared video 2: public URL of the video. |
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+ | `model1` | string | Name of the model that produced `video1`. |
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+ | `model2` | string | Name of the model that produced `video2`. |
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+ | `weighted_results_video1_creativity` | float32 | Sum of the userScore weights of the votes for `video1` on the **Creativity** leaderboard. |
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+ | `weighted_results_video2_creativity` | float32 | Sum of the userScore weights of the votes for `video2` on the **Creativity** leaderboard. |
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+ | `detailed_results_creativity` | string | Every vote on the **Creativity** leaderboard as a JSON array; `votedFor` is `A` for side 1 and `B` for side 2. |
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+ | `weighted_results_video1_coherence` | float32 | Sum of the userScore weights of the votes for `video1` on the **Coherence** leaderboard. |
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+ | `weighted_results_video2_coherence` | float32 | Sum of the userScore weights of the votes for `video2` on the **Coherence** leaderboard. |
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+ | `detailed_results_coherence` | string | Every vote on the **Coherence** leaderboard as a JSON array; `votedFor` is `A` for side 1 and `B` for side 2. |
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+ | `weighted_results_video1_alignment` | float32 | Sum of the userScore weights of the votes for `video1` on the **Alignment** leaderboard. |
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+ | `weighted_results_video2_alignment` | float32 | Sum of the userScore weights of the votes for `video2` on the **Alignment** leaderboard. |
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+ | `detailed_results_alignment` | string | Every vote on the **Alignment** leaderboard as a JSON array; `votedFor` is `A` for side 1 and `B` for side 2. |
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+ | `weighted_results_video1_preference` | float32 | Sum of the userScore weights of the votes for `video1` on the **Preference** leaderboard. |
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+ | `weighted_results_video2_preference` | float32 | Sum of the userScore weights of the votes for `video2` on the **Preference** leaderboard. |
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+ | `detailed_results_preference` | string | Every vote on the **Preference** leaderboard as a JSON array; `votedFor` is `A` for side 1 and `B` for side 2. |
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+
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+ The `weighted_results_*` pair is a userScore-weighted vote sum per side, not a probability: the two values of a row do not sum to 1. Divide by their sum for a normalised win share.
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+
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+ ### Overall ranking
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+
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+ | # | Model | ELO |
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+ |---|-------|-----|
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+ | 1 | gemini-4-argon | 1581.55 |
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+ | 2 | gpt-6-astra | 1578.51 |
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+ | 3 | gpt-6.1-sol | 1460.51 |
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+ | 4 | claude-opus-5.5 | 1316.63 |
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+ | 5 | muse-wonder | 1311.88 |
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+ | 6 | gemini-3.8-flash | 1124.86 |
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+ | 7 | claude-fable-5.1 | 1098.67 |
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+ | 8 | grok-4.7 | 991.13 |
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+ | 9 | kimi-k3 | 742.366 |
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+ | 10 | qwen3.8-max-prime | 701.778 |
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+ | 11 | glm-5.3-prime | 670.075 |
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+ | 12 | glm-5.3-flash | 559.467 |
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+ | 13 | mistral-large-4 | 492.705 |
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+ | 14 | deepseek-v4-pro | 369.868 |
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+
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+ ### Creativity ranking
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+
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+ | # | Model | ELO |
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+ |---|-------|-----|
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+ | 1 | gemini-4-argon | 1794.94 |
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+ | 2 | gpt-6-astra | 1423.91 |
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+ | 3 | gemini-3.8-flash | 1322.04 |
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+ | 4 | gpt-6.1-sol | 1299.32 |
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+ | 5 | muse-wonder | 1274.79 |
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+ | 6 | claude-opus-5.5 | 1163.15 |
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+ | 7 | grok-4.7 | 1052.69 |
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+ | 8 | claude-fable-5.1 | 982.764 |
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+ | 9 | glm-5.3-prime | 766.745 |
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+ | 10 | kimi-k3 | 760.325 |
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+ | 11 | qwen3.8-max-prime | 717.383 |
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+ | 12 | glm-5.3-flash | 637.826 |
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+ | 13 | mistral-large-4 | 462.044 |
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+ | 14 | deepseek-v4-pro | 342.073 |
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+
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+ ### Coherence ranking
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+
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+ | # | Model | ELO |
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+ |---|-------|-----|
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+ | 1 | gpt-6-astra | 1619.89 |
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+ | 2 | gpt-6.1-sol | 1499.65 |
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+ | 3 | claude-opus-5.5 | 1388.76 |
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+ | 4 | muse-wonder | 1290.42 |
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+ | 5 | gemini-4-argon | 1271.85 |
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+ | 6 | claude-fable-5.1 | 1164.81 |
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+ | 7 | grok-4.7 | 967.768 |
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+ | 8 | gemini-3.8-flash | 912.763 |
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+ | 9 | kimi-k3 | 770.67 |
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+ | 10 | qwen3.8-max-prime | 748.593 |
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+ | 11 | glm-5.3-prime | 643.177 |
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+ | 12 | mistral-large-4 | 627.273 |
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+ | 13 | glm-5.3-flash | 568.274 |
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+ | 14 | deepseek-v4-pro | 526.101 |
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+
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+ ### Alignment ranking
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+
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+ | # | Model | ELO |
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+ |---|-------|-----|
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+ | 1 | gpt-6-astra | 1601.59 |
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+ | 2 | gemini-4-argon | 1576.04 |
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+ | 3 | gpt-6.1-sol | 1500.92 |
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+ | 4 | claude-opus-5.5 | 1357.94 |
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+ | 5 | muse-wonder | 1331.09 |
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+ | 6 | claude-fable-5.1 | 1146.26 |
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+ | 7 | gemini-3.8-flash | 1052.44 |
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+ | 8 | grok-4.7 | 976.087 |
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+ | 9 | kimi-k3 | 766.661 |
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+ | 10 | qwen3.8-max-prime | 704.244 |
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+ | 11 | glm-5.3-prime | 661.558 |
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+ | 12 | glm-5.3-flash | 492.166 |
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+ | 13 | mistral-large-4 | 478.849 |
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+ | 14 | deepseek-v4-pro | 354.162 |
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+
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+ ### Preference ranking
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+
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+ | # | Model | ELO |
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+ |---|-------|-----|
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+ | 1 | gemini-4-argon | 1794.09 |
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+ | 2 | gpt-6-astra | 1690.55 |
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+ | 3 | gpt-6.1-sol | 1552.64 |
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+ | 4 | muse-wonder | 1439.38 |
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+ | 5 | claude-opus-5.5 | 1357.33 |
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+ | 6 | gemini-3.8-flash | 1186.73 |
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+ | 7 | claude-fable-5.1 | 1084.72 |
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+ | 8 | grok-4.7 | 993.241 |
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+ | 9 | kimi-k3 | 666.817 |
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+ | 10 | qwen3.8-max-prime | 629.546 |
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+ | 11 | glm-5.3-prime | 582.313 |
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+ | 12 | glm-5.3-flash | 447.393 |
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+ | 13 | mistral-large-4 | 377.978 |
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+ | 14 | deepseek-v4-pro | 197.273 |
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+
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+ ## About Rapidata
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+
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+ Rapidata's technology makes collecting human feedback at scale faster and more accessible than ever before. Visit [rapidata.ai](https://www.rapidata.ai) to learn more.
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