Best LLMs for Research & Knowledge Work
Research and knowledge work stress a model differently than code or chat: the failure mode is a confident wrong answer, and the benchmarks that predict it are knowledge-heavy ones. This page ranks models on MMLU-Pro, GPQA, and Humanity’s Last Exam, the suites that measure factual breadth and graduate-level reasoning rather than conversational fluency.
The quality index is the same per-task projection the Inferbase router consults for open-ended question answering in production. Models without published results on these suites are omitted rather than estimated.
The top pick
The strongest measured fit for this task right now, taken from the live ranking below.
- HLE
- 61.4%
- Context
- 1M
The ranking
Ordered from live catalog data; the note beneath the table states exactly how. Missing values are shown as missing, never estimated.
| # | Model | Quality index | MMLU-Pro | GPQA | HLE | Context |
|---|---|---|---|---|---|---|
| 1 | anthropic | 100 | No data | No data | 61.4% | 1M |
| 2 | google | 99 | No data | No data | 57.1% | 1M |
| 3 | anthropic | 98.5 | No data | 93.7% | 59.1% | 1M |
| 4 | openai | 98.5 | No data | 96.1% | 54.7% | 1.1M |
| 5 | anthropic | 98 | No data | No data | 55.0% | 1M |
| 6 | openai | 97.3 | No data | 94.1% | 49.5% | 1.1M |
| 7 | google | 96.8 | No data | 95.3% | 47.8% | 1M |
| 8 | openai | 96.6 | No data | No data | 52.9% | 1.1M |
| 9 | anthropic | 96.5 | No data | 93.2% | 54.9% | 1M |
| 10 | google | 96 | No data | 94.1% | 47.0% | 1M |
This list is ordered by the same per-task quality projection the Inferbase router uses to route production traffic: benchmark results from independent suites are normalized and combined per task. The quality index is a 0-100 percentile among the 197 models with measured evidence for this task. Models without published benchmark results here are not ranked, and not guessed at.
marks models served through the Inferbase API. Missing values are shown as No data rather than estimated.
Also worth evaluating
The next ranks after the top ten, for teams that want a wider shortlist.
Or stop choosing manually
The honest answer changes as benchmarks and prices move. Routing makes the choice per request instead of once.
Send model="auto" to one OpenAI-compatible endpoint and each request is served by the best fit from the same quality and price data behind this ranking, with the decision disclosed per request.
Every routed request records the task it was classified as, the models that qualified, the one that won and why, so a pick made here can be checked against what the router chose in production. A model pinned by name is served as named, with no routing decision.
from openai import OpenAI
client = OpenAI(
base_url="https://api.inferbase.ai/v1",
api_key="YOUR_INFERBASE_KEY",
)
# Let routing pick the best model per request,
# or pin any model id from GET /models.
response = client.chat.completions.create(
model="auto",
messages=[{"role": "user", "content": "..."}],
)Frequently asked questions
How this ranking works and how to act on it.
MMLU-Pro measures broad multi-domain knowledge under harder distractors than the original MMLU. GPQA is graduate-level science question answering designed to resist lookup. HLE (Humanity’s Last Exam) is a frontier-difficulty exam across disciplines. Together they are a reasonable proxy for research-grade factual reliability.
Each suite’s scores are normalized within that suite’s population, then combined into one per-task score, the projection the Inferbase router uses for knowledge-heavy traffic. The index shown is a 0-100 percentile among models with measured evidence.
Yes. Higher benchmark performance correlates with fewer factual errors, but no model at any rank is a citation-grade source. For research workflows, retrieval grounding and source verification remain necessary regardless of which model this page ranks first.
Not necessarily. Summarizing documents you supply is an easier task than answering open questions from parametric knowledge, and a cheaper model frequently suffices for it. Routing per request, rather than standardizing on one model, captures exactly that difference.
Benchmark and pricing data refresh on the catalog sync cycle and the page regenerates at least hourly. New models appear as soon as a covered suite publishes results for them.
The top of this table on the knowledge suites, with two practical additions: a context window large enough for the papers you paste, and retrieval in front of the model so that claims come from sources you supply. The ranking measures reasoning and factual breadth; it does not measure whether a model cites correctly, so verify citations against the source.
For graduate-level questions, yes: models that think before answering score highest on GPQA and Humanity’s Last Exam, and the table shows that. The cost is latency and tokens, since the thinking is billed, so a routine lookup does not need one. The catalog marks which models support extended thinking, and routing can be set to prefer quality for the requests that warrant it.
Three habits. Give the model the source material rather than asking it to recall, since the failure mode on these suites is a confident wrong answer from memory. Ask for the passage a claim rests on and check it. And match the model to the difficulty of the question: the scores here separate models on exactly the graduate-level questions where weaker models guess.
Related rankings
The same evidence-first method, applied to other tasks.
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