A Deep Dive into Recent High Data

In Figure 1 we look at how the category distribution has changed over time. We see a substantial increase in hard prompts and coding. We also see a decrease in instruction following, math, and creative writing. The decrease in instruction following is explained by the fact that we implemented a more stringent classifier for labeling prompts as instruction following (details to follow).

In Figure 2 we provide a more fine-grained analysis: we plot the seven-day rolling average of the category distribution starting the beginning of 2024.

Why does instruction following drop so much? This category distribution shift is largely explained by a change in the classification model (the LLM used to assign categories to individual prompts) from llama-v3-70b-instruct to deepseek-v3-0324.

To select the classification model, we run label_bench – evaluating each candidate’s labeling accuracy, precision, and recall in a small dataset against gold labels obtained via four large LLMs: claude-3-7-sonnet, deepseek-r1, gemini-2.0-pro, and gpt-4.5. We define gold labels as True for a particular category if at least two models vote True. More details about the label_bench method can be found here.

Results for the candidate models are shown below.

As shown in Table 2, deepseek-v3-0324 exhibits significantly higher precision and lower recall in the instruction following (IF) category, strongly suggesting that it is more selective in its labeling, resulting in fewer prompts being classified as IF.

Recent data from the new UI comes from over 100 languages, with slightly more than half being English. We show the comparison in Figure 3.

In Figure 4, we show the seven-day rolling average of the language distribution starting the beginning of 2024.

Using our High Explorer pipeline, we build an interactive topic distribution graph below.

To take a deeper look into personal preference shifts, we explore model score changes over time.

Recall that High scores are invariant to shifts: the relative strengths of models are captured by the differences between their High scores, while the absolute value of the scores is determined by anchoring. Anchoring is a method that fixes a particular model to have a particular score; all other scores are derived relative to that model.

The graphs below anchor mixtral-8x7b-instruct-v0.1 at 1114.0: each time the leaderboard is calculated, mixtral's score always stays at 1114. This fixes the scale of the leaderboard across time periods, making the scores comparable.

In Figure 5, we show High score changes over the full distribution of our prompts.

For score shift graphs for other categories, please refer to the further analysis section.

In addition to shifts in user preference, several other factors also influence score changes.

In the next section, we dive into the main difference between the old and new UI from a data perspective–Evaluation Order, a new feature on the new UI, and a main contributor to the score changes.

For models that show a strong sensitivity to increased evaluation order, we conduct model-specific analysis here. We find that models such as gemini-2.5-flash and gemini-2.5-pro tend to perform better when the evaluation context is restricted to model outputs from the same family.

The new UI supports follow-up questions after voting. After a vote is cast and model identities are revealed, a new pair of models will be sampled. To ensure fairness, the winning response from the previous round is passed as context to both models in the next round. If the vote resulted in a tie or a both_bad outcome, then each newly-sampled model instead receives its respective side’s previous response as context.

We formally define the nth evaluation order as the nth vote-casting event within a single evaluation session.

Each evaluation order within a complete evaluation session has its own vote taken into account when leaderboard scores are calculated

The maximum evaluation order in our data is 118. Around 70% of votes have evaluation order equal to one. See Figure 9.

To visualize the effect of evaluation order, in Figure 10 we compare the scores from data where evaluation order is one only with the scores that use all evaluation orders.

We observe that including all evaluation orders decreases the scores of all Gemini models. Also experiencing a notable decrease are the o4-mini-2025-04-16 , gpt-4.1-2025-04-14 and qwen3-235b-a22b. Claude-3-7-sonnet-20250219 and llama-4-maverick-17b-128e-instruct exhibit a significant increase.

A natural question arising from Figure 10 is: why and how are some models' scores affected by evaluation order?

A Deeper Look into Gemini Models.

All Gemini models experience a notable drop in performance—less wins, more losses—as evaluation order increases beyond one.

For a deeper look into the Gemini models, we divide the data into three groups: evaluation order equal to one, two, and greater than two. We compare the performance of Gemini models under two context conditions: all-context (where the previous context can come from any model) and restricted-context.

In the restricted-context setting, we only include battles where the last context (i.e., the prior winning side) also came from a Gemini model. This ensures that for votes with an evaluation order of two, the context Gemini models received came exclusively from Gemini models. However, votes with evaluation order greater than two may include earlier context turns involving non-Gemini models.

Notably, when the context is restricted to Gemini-only, gemini-2.5-pro shows a much smaller decline going from evaluation order one to two compared to all context, and gemini-2.5-flash shows an even smaller decline. Overall, both models perform better at higher evaluation orders when the incoming context comes from other Gemini models. This suggests that Gemini models perform better when their context contains outputs primarily from other Gemini models.

A Deeper Look into GPT Models.

While o4-mini and o3 both experience notable declines when evaluation order increases to above one, chatgpt-4o-latest's performance barely changes. Following the same approach used in the Gemini analysis, we divide the data into three groups—evaluation order equal to one, two, and greater than two—and compare the performance shifts for these models under both all-context (where prior context can come from any model) and restricted-context (where the previous winning model is limited to the same model family). We observe mixed results even with restricted context: the win rates sometimes increase and sometimes decrease as evaluation order increases.

A Deeper Look into Claude-3-7-Sonnet.

Claude-3-7-sonnet experiences a notable increase in performance as evaluation order increases to above one, unlike most other models. We perform the same analysis here as for the previous families of models.

We observe that the win rate of Claude-3-7-sonnet is higher for higher evaluation orders than for evaluation order equal to one, suggesting strong performance in longer conversations.

We take a look at coefficients quantifying the importance of style on recent data. We observe a fairly stable distribution over time; see Figure 18.

We also look at a categorical breakdown of style coefficients in Figure 19.

Categorial Score Shifts

This dataset contains user votes collected in the text-only category. Each row represents a single vote judging two models (model_a and model_b) on a user conversation, along with the full conversation history and metadata. Key fields include:

• id: Unique feedback ID of each vote/row.
• evaluation_session_id: Unique ID of each evaluation session, which can contain multiple separate votes/evaluations.
• evaluation_order: Evaluation order of the current vote.
• winner: Battle result containing either model_a, model_b, tie, or both_bad.
• conversation_a/conversation_b: Full conversation of the current evaluation order.
• full_conversation: The entire conversation, including context prompts and answers from all previous evaluation orders. Note that after each vote new models are sampled, thus the responding models vary across the full context.
• conv_metadata: Aggregated markdown and token counts for style control.
• category_tag: Annotation tags including the categories math, creative writing, hard prompts, and instruction following.
• is_code: Whether the conversation involves code.

To download the dataset, run

We’re excited to share this dataset and hope it serves as a valuable resource for advancing research into real-world AI behavior and human preference.