# How to find a model benchmark-first or task-first
**URL:**
**Author:**  [@mattnorris](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fmattnorris)%5C
**Post date:** [February 12, 2024, 7:18pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhow-to-find-a-model-benchmark-first-or-task-first%2F72882%2F1 "2024-02-12T19:18:08Z")
Hello đź‘‹,
I explored the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) and see that it is organized model-first. If you have a model in mind, you can show certain benchmarks and their scores to evaluate that model.
Is this same information organized benchmark-first or task-first elsewhere?
For example, I need a model that I provides a natural chat and can also calculate numbers accurately. I don’t have a particular model in mind. I know the task I need accomplished, but I don’t know if there is a model that does it well.
The [leaderboard’s raw data](https://huggingface.co/datasets/open-llm-leaderboard/results/tree/main) could be reformatted and combined with other data to accomplish this, but I thought there may be another Space or tool that does this already.
Thank you!
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**Author:**  [@abishekvr](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fabishekvr)%5C
**Post date:** [March 31, 2026, 10:50pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhow-to-find-a-model-benchmark-first-or-task-first%2F72882%2F2 "2026-03-31T22:50:34Z")
I was using inferencebench to check the best bets against models, not sure how much that could help you.
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**Author:**  [@John6666](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FJohn6666)%5C
**Post date:** [April 1, 2026, 2:23am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhow-to-find-a-model-benchmark-first-or-task-first%2F72882%2F3 "2026-04-01T02:23:38Z")
I can’t seem to find an all-in-one solution that can do that right now…
* * *
Yes, partly.
There is still no single public site that perfectly answers a plain-English request like **“I want natural chat and accurate calculation”** and then returns a definitive ranked list. But there **are** now several places that are much closer to **benchmark-first** or **task-first** than the Open LLM Leaderboard, and the best current method is to combine them. ([Hugging Face](https://huggingface.co/docs/hub/en/leaderboard-data-guide "Accessing Benchmark Leaderboard Data · Hugging Face"))
## The direct answer
The closest things to what you want are:
- **Hugging Face OpenEvals Official Benchmarks Leaderboard 2026**. It is a unified leaderboard across official Hugging Face benchmarks, and its description says you can filter by selected tasks, model name, and size. That is much closer to benchmark-first browsing than the older Open LLM Leaderboard. ([Hugging Face](https://huggingface.co/spaces/OpenEvals/every-leaderboards "Official Benchmarks Leaderboard 2026"))
- **Hugging Face Benchmark Finder**. OpenEvals lists it as a Space to “view and inspect all the tasks in Lighteval,” which is explicitly task-oriented. ([Hugging Face](https://huggingface.co/OpenEvals/spaces "OpenEvals (OpenEvals)"))
- **Hugging Face’s benchmark leaderboard APIs and aggregated dataset**. The docs now expose an official dataset-centric API, plus a pre-aggregated `OpenEvals/leaderboard-data` Parquet file for cross-benchmark analysis. That is the strongest answer if you want to build your own task-first view. ([Hugging Face](https://huggingface.co/docs/hub/en/leaderboard-data-guide "Accessing Benchmark Leaderboard Data · Hugging Face"))
- **Arena.ai categories**. This is not benchmark-first in the academic sense, but it is very useful for task-first shortlisting because it breaks public preference data into categories like **math** , **instruction following** , **multi-turn** , and **creative writing**. ([arena.ai](https://arena.ai/blog/arena-category/ "Chatbot Arena Categories"))
- **Artificial Analysis**. It is good for model comparison after you know the relevant task axes, because it exposes separate evaluation pages such as **MATH-500** and **IFBench** alongside price, speed, and context comparisons. ([Artificial Analysis](https://artificialanalysis.ai/evaluations/math-500 "MATH-500 Benchmark Leaderboard | Artificial Analysis"))
- **LiveBench**. It is useful as a category-first sanity check because it scores models across **math, reasoning, data analysis, language, coding, and instruction following** , and it is designed to reduce contamination and rely on objective grading. ([ukgovernmentbeis.github.io](https://ukgovernmentbeis.github.io/inspect_evals/evals/knowledge/livebench/ "LiveBench: A Challenging, Contamination-Free LLM Benchmark"))
- **OpenCompass and HELM**. These are more evaluator-oriented than shopper-oriented, but they are strong if you want benchmark browsing and scenario-based comparison rather than one flat rank. OpenCompass says CompassHub is a benchmark browser, and HELM is built around many scenarios and metrics instead of a single score. ([GitHub](https://github.com/open-compass/opencompass "GitHub - open-compass/opencompass: OpenCompass is an LLM evaluation platform, supporting a wide range of models (Llama3, Mistral, InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets. · GitHub"))
## The important background
The Open LLM Leaderboard is mostly **model-first** : start with a model, then inspect benchmark results. Your question is the reverse: **start with the task, then ask which models are strong on the relevant benchmarks**.
That reverse workflow is now supported better than before. Hugging Face’s own docs explicitly describe a **dataset-centric** leaderboard API, where you query a benchmark and get ranked models, and they also provide a pre-aggregated multi-benchmark dataset for cross-benchmark views. In other words, the platform now officially supports the exact kind of rearrangement you are asking for. ([Hugging Face](https://huggingface.co/docs/hub/en/leaderboard-data-guide "Accessing Benchmark Leaderboard Data · Hugging Face"))
But the broader problem is still only partially solved, because real user needs are not single benchmarks. “Natural chat and accurate calculation” is already a mixed task. It combines at least three separate abilities:
- conversational quality,
- numerical reliability,
- instruction discipline. ([arena.ai](https://arena.ai/blog/arena-category/ "Chatbot Arena Categories"))
## The clean way to think about your example
Your example is not one task. It is a bundle.
### 1. Natural chat
This usually maps to:
- **overall human preference**
- **multi-turn conversation**
- **creative writing / prose quality**
- **instruction following** if you care about tone, brevity, or structure
Arena’s category system is unusually useful here because it explicitly separates these. Arena also states that a high overall rank does **not** mean a model will excel uniformly across every use case. ([arena.ai](https://arena.ai/blog/arena-category/ "Chatbot Arena Categories"))
### 2. Accurate calculation
This splits further:
- **formal math reasoning** , where MATH-500 is useful
- **instruction-following under constraints** , where IFBench or IFEval matter
- **real-world numeracy** , which standard math leaderboards do not fully capture
This last part matters a lot. NumericBench was proposed specifically because many existing benchmarks focus on language ability or structured math problems while missing basic real-world numerical tasks like arithmetic, contextual retrieval of numbers, comparison, and numeric summaries. ([Artificial Analysis](https://artificialanalysis.ai/evaluations/math-500 "MATH-500 Benchmark Leaderboard | Artificial Analysis"))
There is a second caution too: GSM-Symbolic reports that model performance can drop when only the numerical values are changed, even when the problem structure is otherwise the same. So “high math benchmark score” does **not** automatically mean “robust numeric behavior in normal use.” ([arXiv](https://arxiv.org/html/2410.05229v1 "GSM-Symbolic: Understanding the Limitations of ..."))
## So where is the information organized benchmark-first or task-first?
### Best benchmark-first options
**1. Hugging Face official benchmark APIs and `OpenEvals/leaderboard-data`**
This is the most literal benchmark-first answer. You can:
- discover official benchmark datasets,
- call a benchmark’s leaderboard endpoint,
- or load one aggregated Parquet file with cross-benchmark scores. ([Hugging Face](https://huggingface.co/docs/hub/en/leaderboard-data-guide "Accessing Benchmark Leaderboard Data · Hugging Face"))
**2. OpenEvals Official Benchmarks Leaderboard 2026**
This is the best current Hugging Face UI for browsing across official benchmarks rather than drilling into one model page. ([Hugging Face](https://huggingface.co/spaces/OpenEvals/every-leaderboards "Official Benchmarks Leaderboard 2026"))
**3. OpenCompass CompassHub**
This is closer to a benchmark browser for researchers and advanced users. Its own repo says CompassHub is designed to simplify exploring and using a large benchmark collection. ([GitHub](https://github.com/open-compass/opencompass "GitHub - open-compass/opencompass: OpenCompass is an LLM evaluation platform, supporting a wide range of models (Llama3, Mistral, InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets. · GitHub"))
**4. HELM**
HELM is less about “pick a winner quickly” and more about “see many scenarios and metrics at once.” It is valuable because it rejects the idea that one score is enough. ([arXiv](https://arxiv.org/abs/2211.09110 "Holistic Evaluation of Language Models"))
### Best task-first approximations
**1. Arena.ai categories**
This is the closest public tool to “I know the task shape, not the model name.” You can inspect categories like math, multi-turn, creative writing, and instruction following separately. ([arena.ai](https://arena.ai/blog/arena-category/ "Chatbot Arena Categories"))
**2. Artificial Analysis**
This is good after you define your task axes. It lets you compare models by benchmark family and by operational trade-offs like price, speed, and context. ([Artificial Analysis](https://artificialanalysis.ai/evaluations/math-500 "MATH-500 Benchmark Leaderboard | Artificial Analysis"))
**3. LiveBench**
This works well as a category-first objective cross-check because it already separates the capability areas instead of forcing you into one overall number. ([ukgovernmentbeis.github.io](https://ukgovernmentbeis.github.io/inspect_evals/evals/knowledge/livebench/ "LiveBench: A Challenging, Contamination-Free LLM Benchmark"))
## What I would do for your exact use case
I would not ask “which model is best overall?” I would ask:
> Which models are strong on conversational preference, instruction following, and numeracy at the same time?
That leads to a much better workflow.
### Step 1. Translate your need into benchmark axes
For **natural chat + accurate numbers** , I would map it like this:
- **Natural chat**
- **Control**
- **Numbers**
- **Long sessions**
This “task profile first” approach is the right one. ([arena.ai](https://arena.ai/blog/arena-category/ "Chatbot Arena Categories"))
### Step 2. Generate a shortlist
Use **Arena.ai** or **OpenEvals** first, not the Open LLM Leaderboard alone.
- Use Arena if “naturalness” matters most.
- Use OpenEvals if you want benchmark-first filtering.
- Use Artificial Analysis if you also care early about price, speed, and context. ([arena.ai](https://arena.ai/blog/arena-category/ "Chatbot Arena Categories"))
### Step 3. Cross-check category weaknesses
Then inspect the shortlisted models on:
- MATH-500
- IFBench
- LiveBench Math / Instruction Following
- long-context benchmarks if relevant. ([Artificial Analysis](https://artificialanalysis.ai/evaluations/math-500 "MATH-500 Benchmark Leaderboard | Artificial Analysis"))
### Step 4. Run a tiny private test pack
This is the step public leaderboards do not replace.
Use maybe 12 to 20 prompts:
- 4 chat-only prompts
- 4 calculation-only prompts
- 4 mixed prompts where the model must stay natural **and** compute correctly
That last group matters most. Many models are separately good at chat and separately decent at math, but weaker when both are required at once.
## A practical answer to your “inferencebench” idea
That idea makes sense, but **later** in the process.
Inference benchmarking tools are useful for **latency, throughput, hardware fit, and serving efficiency**. They are not the right first filter for “natural chat + accurate numbers.” For example, LLM-Inference-Bench describes itself as a hardware inference benchmarking suite across platforms and inference frameworks. That is valuable after you already know which models are quality candidates. ([GitHub](https://github.com/argonne-lcf/LLM-Inference-Bench "argonne-lcf/LLM-Inference-Bench"))
So the order should be:
1. **quality shortlist by task**
2. **benchmark cross-check**
3. **your own prompt pack**
4. **inference/performance benchmark for deployment**
Not the reverse. ([GitHub](https://github.com/argonne-lcf/LLM-Inference-Bench "argonne-lcf/LLM-Inference-Bench"))
## What I would bookmark first
If you want the smallest useful set:
- **OpenEvals Official Benchmarks Leaderboard 2026** for cross-benchmark browsing. ([Hugging Face](https://huggingface.co/spaces/OpenEvals/every-leaderboards "Official Benchmarks Leaderboard 2026"))
- **OpenEvals Benchmark Finder** for browsing tasks. ([Hugging Face](https://huggingface.co/OpenEvals/spaces "OpenEvals (OpenEvals)"))
- **Hugging Face leaderboard data guide** for building your own benchmark-first view. ([Hugging Face](https://huggingface.co/docs/hub/en/leaderboard-data-guide "Accessing Benchmark Leaderboard Data · Hugging Face"))
- **Arena.ai categories** for task-shaped shortlisting. ([arena.ai](https://arena.ai/blog/arena-category/ "Chatbot Arena Categories"))
- **Artificial Analysis** for benchmark pages plus price/speed/context comparison. ([Artificial Analysis](https://artificialanalysis.ai/models "Comparison of AI Models across Intelligence, Performance ..."))
- **LiveBench** for a contamination-aware objective check. ([ukgovernmentbeis.github.io](https://ukgovernmentbeis.github.io/inspect_evals/evals/knowledge/livebench/ "LiveBench: A Challenging, Contamination-Free LLM Benchmark"))
## The bottom line
So the answer is:
- **Yes** , the ecosystem now has better **benchmark-first** and **task-filtered** options than the Open LLM Leaderboard alone. ([Hugging Face](https://huggingface.co/docs/hub/en/leaderboard-data-guide "Accessing Benchmark Leaderboard Data · Hugging Face"))
- **No** , there is still no single perfect public tool that takes a natural-language need and gives a fully reliable final answer. ([arena.ai](https://arena.ai/blog/arena-category/ "Chatbot Arena Categories"))
- For your example, the best approach is to combine:
The main conceptual shift is this:
> Do not choose **a model** , then inspect its scores.
> Choose **the capability profile** , then find models that survive it.
That is the task-first version of model selection.