NULLSPACE — 267 JEE questions frontier models get wrong

Most STEM benchmarks are saturated. Frontier models score 96–98% on ordinary JEE questions, so an aggregate number tells you almost nothing about which model reasons better.

This set contains only the questions they fail. Every one was answered incorrectly by at least one frontier model, and every answer key was independently reproduced by a different model solving the question blind — so a failure here is a real failure, not a broken key.

267 questions public. 77 more are held out and unpublished, so the set can still measure a model that has seen this page.

⚠️ Read this before you score anything

This benchmark cannot fairly score the models that built it.

Every key here was confirmed by some model re-solving the question. A model that already solved a question cannot be tested on it. Every row therefore carries a do_not_score list, and honest evaluation means excluding those rows per model:

Model	Scorable rows
gemini-3.6-flash	183 of 267
gemini-3.5-flash	174 of 267
claude-opus-5	165 of 267
gemini-2.5-pro	161 of 267
gpt-5.1	5 of 267
gemini-3.1-pro-preview	3 of 267

This is not hypothetical. Scoring claude-opus-5 across rows it had touched gave 58.3%; on rows it had never seen it scored 43.0%. Ignore the field and you overstate by roughly 15 points.

gpt-5.1 and gemini-3.1-pro-preview produced nearly every failure this set is made of, so almost nothing is left to score them on. That is inherent to a failure-only benchmark — it exists to evaluate models that came after it.

do_not_score covers both roles: a model that confirmed a key has already solved the question, and a model that failed it is guaranteed wrong. Failures are gathered from every run rather than only the one that admitted the question — an earlier build missed 126 of them and advertised 56 rows as scorable for gpt-5.1 that it had already failed.

import json
rows = [json.loads(l) for l in open("benchmark.jsonl")]

MODEL = "claude-opus-5"
eligible = [r for r in rows if MODEL not in r["do_not_score"]]
print(f"{MODEL}: score on {len(eligible)} of {len(rows)} rows")

Results — three frontier flagships, same 267 questions

Every model below ran the identical set under identical conditions: same prompts, figures attached, high reasoning effort, 267 questions each.

Model	Org	Accuracy	Clean subset
claude-opus-5	Anthropic	62.2%	67.3% (n=165)
gpt-5.1	OpenAI	49.8%	n=5 — too few to report
gemini-3.1-pro-preview	Google	23.6%	n=3 — too few to report

43 of 267 questions — 16% — were failed by all three simultaneously. Only 23 were solved by all three. On ordinary questions from the same source, these models score 96–98%.

The ranking is not a capability ranking

Read the second column before the first. The ordering above tracks how much each model helped build the set, not how capable it is:

Model	Rows it failed or helped check
gemini-3.1-pro-preview	264 of 267
gpt-5.1	262 of 267
claude-opus-5	102 of 267

Gemini swept the entire source bank, so nearly every question here is one it already got wrong — its 23.6% is close to arithmetically forced, not a statement about the model. Claude leads largely because it had the least involvement.

The one uncontaminated figure in the table is Claude's 67.3% on the 165 rows it never touched — a vendor with no hand in selecting those questions still failing a third of them. That, and the 16% all three failed, are the numbers worth quoting. The full leaderboard is context.

Any model that did not help build this benchmark starts with all 267 rows eligible, and its accuracy is directly comparable to the first column.

How a question qualified

Both conditions were required:

1. a frontier model answered it wrong on a scored measurement — truncations and API errors are lost measurements, never failures; and
2. an independent model re-solved it without sight of the answer key or of the failed model's answer, with figures attached, and reached that same key.

Condition 2 is the whole point. A model disagreeing with a key means either the model was wrong or the key was wrong, and the two are indistinguishable from the outside.

Across 704 checked questions, 514 turned out to be wrong keys rather than model failures. Among questions where two vendors gave the same wrong answer, 81% were bad keys — measured on three independent pools. Every one of those was excluded. Without this step the set would be mostly broken questions rather than hard ones.

What's in it

Questions	267 public (77 held out)
Chemistry / Physics / Mathematics	122 / 82 / 63
JEE Advanced / JEE Main	208 / 59
With figures	101 (251 images included)
Failed by two vendors independently	130
Answer types	multiple-choice, single-choice, numerical, integer, match-the-following

Fields

Field	Meaning
item_id	stable public identifier, STEM-NNNN
exam, subject, answer_type	where it came from and what kind of answer is expected
question, options, column_a/column_b	the question content; HTML with MathML
answer, answer_key	the key, plain string and structured object
do_not_score	models that must be excluded when scoring this row
models_failed	which models got this question wrong
verdict	CONFIRMED_MODEL_FAILURE, DISPUTED_BUT_KEY_BACKED, DISPUTED
thumbnail	first figure image (typed Image), null for text-only questions

Grading

Answers are compared programmatically:

• Single choice / match-the-following — one letter, exact match
• Multi-select — exact set match; partial credit is not correct
• Numerical — tolerance max(0.011, 1% of |truth|), since keys are quoted to 2dp
• Integer — exact

Figure questions require the image. A model answering a diagram question without the diagram is answering a different question.

Confidence tiers

Rows are not equally strong.

Tier	Meaning
CONFIRMED_MODEL_FAILURE	solvers reached the key, no dissent
DISPUTED_BUT_KEY_BACKED	solvers split, but at least one reached the key
DISPUTED	solvers split between the key and the failed answer — weakest rows

For a high-confidence subset: both_vendors_failed == true and verdict != "DISPUTED".

Limitations

• Not usable for gpt-5.1 or gemini-3.1-pro-preview — see above.
• Wrong keys are the dominant failure mode of the source material and are not all gone. Excluded ones were caught by cross-vendor checking, but the base rate is high enough that residual bad keys should be assumed, not ruled out.
• The evidence is mostly model-on-model. Model agreement cannot rule out a shared blind spot; a measured case exists where three models from one vendor agreed and a fourth from another reached a different, correct answer.
• Single-solver rows are weaker than they look. Adding a second vendor to 65 verdicts settled by one vendor changed 20 of them — about one in three.
• Subject mix is uneven. Models fail Mathematics least, so it is the smallest slice.
• Solutions are not included. Answer keys are; worked explanations are not.

Held-out split

77 further questions exist and are deliberately unpublished. A benchmark meant to test future models loses its value once all of it is public — published questions can be trained on, and the first release becomes the last honest measurement. Contact the maintainers for held-out evaluation.

Citation

@misc{nullspace2026,
  title        = {NULLSPACE: A Failure-Only JEE Benchmark with Contamination Tracking},
  author       = {Nalandadata},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/Nalandadata/NULLSPACE}},
  note         = {267 public questions; 77 held out}
}