Model benchmark
DeepSeek V3.2 Exp:
AI recommendation benchmark.
How strongly does DeepSeek V3.2 Exp recommend a business when answering buyer questions—and how much does naming the business change its answer?
A sample still growing.
Recommendation scores will appear once enough distinct businesses have eligible analyses. Missing benchmarks are not zero scores.
Explore the provider
Deepseek models, side by side.
Compare recommendation behavior and brand recognition. Open a model for its findings and sample details.
| Model | Organic / 100 | Mentioned / 100 | Gap | Unprompted inclusion | Recognized |
|---|---|---|---|---|---|
deepseek/deepseek-v4.1-flash Insufficient data |
— | — | — | — | |
deepseek/deepseek-v4.1-flash:batch Insufficient data |
— | — | — | — | |
deepseek/deepseek-v4-flash-vision-exp Insufficient data |
— | — | — | — | |
deepseek/deepseek-v4-pro-0813 Insufficient data |
— | — | — | — | |
deepseek/deepseek-v4-flash-0731 Insufficient data |
— | — | — | — | |
deepseek/deepseek-v4-pro Insufficient data |
— | — | — | — | |
deepseek/deepseek-v4-flash Insufficient data |
— | — | — | — | |
deepseek/deepseek-v3.2 Insufficient data |
— | — | — | — | |
deepseek/deepseek-v3.2-exp Insufficient data |
— | — | — | — | |
deepseek/deepseek-v3.1-terminus Insufficient data |
— | — | — | — | |
deepseek/deepseek-chat-v3.1 Insufficient data |
— | — | — | — | |
deepseek/deepseek-r1-0528 Insufficient data |
— | — | — | — | |
deepseek/deepseek-chat-v3-0324 Insufficient data |
— | — | — | — | |
deepseek/deepseek-r1-distill-qwen-32b Insufficient data |
— | — | — | — | |
deepseek/deepseek-r1-distill-llama-70b Insufficient data |
— | — | — | — | |
deepseek/deepseek-r1 Insufficient data |
— | — | — | — | |
deepseek/deepseek-chat Insufficient data |
— | — | — | — | |
| No models match this search. | |||||
Compare with context. Samples differ between models. Low gap does not mean high visibility: read both scores together. Recognition is measured separately through Domain Clarity.
About this model
DeepSeek V3.2 Exp in AEOchamp.
DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...
Provider
Deepseek
Context window
163,840 tokens
Model status
Active
Analysis type
Supports structured analysis
Model details come from AEOchamp’s model catalog. These API benchmark results do not represent every consumer chat product or browsing configuration.
How to read the data
A benchmark with its workings shown.
The numbers describe AEOchamp’s tests. They are not search rankings or a universal model quality score.
What do Organic and Mentioned mean?
Organic asks a buyer question without supplying the target domain and checks whether the business appears in the returned recommendation list. Mentioned asks the same question with the target domain supplied. Both use the model’s existing knowledge, without browsing.
How is the recommendation gap calculated?
For each completed question pair, subtract the Organic score from the Mentioned score. Average the pairs within each domain, then average across distinct domains. A +39 gap means 39 score points, not a 39% improvement.
What happens if a business is absent?
A completed Organic result contributes 0 if the target business is absent from the returned list. Unfinished or failed pairs are excluded from both averages. Absence from this list does not establish that the model has never heard of the business.
Can I compare different models fairly?
These tables show each model’s available sample, which may include different businesses and questions. They are descriptive comparisons, not a controlled leaderboard. A fair head-to-head test needs the same domains, questions and test settings.
Which questions and businesses are included?
The benchmark uses completed baseline question pairs and counts each distinct domain once per model. Custom questions and internal/demo projects are excluded. Local fake analyses and results without completed training run records are excluded. When multiple projects track the same domain, only its most recently updated eligible project is included for that model.
How much data is enough to publish?
Scores are published only when enough distinct businesses have eligible analyses. Only aggregate scores are public; sample counts, customer domains, buyer questions and raw answers are not shown.
Latest eligible results are used without a time cutoff. Aggregates refresh every 15 minutes. Recognition samples are separate from User Intent samples. Inclusion is averaged within each domain before averaging across domains. Rounded averages may not subtract to the displayed paired gap.
From the average to your business
The benchmark shows the pattern.
AEOchamp shows where you stand.
Find out whether AI models recognize your business, how they describe it, and whether they recommend it for the questions your buyers ask.
- Check your category, audience and jobs
- Compare Organic and Mentioned scores
- Find positioning gaps worth investigating
Domain Clarity + User Intent, in one place.