Qwen3.5 9B — Benchmarks

Benchmark scores for Qwen3.5 9B aggregated from public leaderboards (8 boards), with how it ranks among open models and how it compares with its nearest neighbours. See hardware requirements for what you need to run it.

llmrun Composite Score →

99.4

90% range 96.0–102.9 · from 8 boards

CodingNot enough data yet
AgentsNot enough data yet
MathNot enough data yet
Science104.590% range 96–113
Reasoning97.090% range 89–106

Qwen3.5 9B reasoning benchmarks

Multi-step logic and problem-solving — can the model think through a hard problem rather than recall an answer.

GPQA Diamond
GPQA Diamond is a set of extremely hard, graduate-level science questions (physics, chemistry, biology) written by domain experts and filtered so that skilled non-experts with web access still fail. It measures genuine reasoning rather than memorization.
79.0%
79.0%
median 55.7%open leader 93.2%
Overall best: Gemini 4 Argon (high) 96.4%
73rd percentile · #28 of 100 open · #135 of 309 all
#28 of 100
#135 of 309
Independent
Epoch AI · Feb 2026
CritPt
CritPt (Critical Points) tests graduate-level physics problem-solving on research-style questions spanning mechanics, electromagnetism, quantum mechanics, and more. It was created by physicists to probe whether a model can reason through multi-step physics rather than pattern-match textbook answers.
0.6%
0.6%
median 0.9%open leader 26.6%
Overall best: Gemini 4 Carbon (high) 36.0%
47th percentile · #46 of 84 open · #181 of 244 all
#46 of 84
#181 of 244
Lab-reported
Epoch AI · Feb 2026
DTBench
DTBench is a set of handcrafted, expert-validated multiple-choice questions on decision theory — Newcomb-like problems and related puzzles — built by the Conceptual Reasoning Index project with Anthropic. It tests whether a model can follow an abstract argument rather than recall a fact.
71.2%
71.2%
median 68.7%open leader 93.9%
Overall best: Claude Opus 5.5 Max 98.9%
54th percentile · #33 of 70 open · #158 of 235 all
#33 of 70
#158 of 235
Lab-reported
Epoch AI · Feb 2026
LMCA
LMCA (Language Model Conceptual Argumentation) scores models on expert-rated arguments across philosophy, decision theory, and AI risk — domains where there is no empirical feedback loop to check an answer against. It is part of the Conceptual Reasoning Index, built with Anthropic by researchers Emery Cooper and Caspar Oesterheld.
24.5%
24.5%
median 25.5%open leader 55.5%
Overall best: Claude Sonnet 5.5 Max 68.3%
47th percentile · #30 of 56 open · #153 of 197 all
#30 of 56
#153 of 197
Lab-reported
Epoch AI · Feb 2026

Qwen3.5 9B coding benchmarks

Writing, editing, and fixing real code — measured by whether the resulting program actually runs and passes tests.

Terminal-Bench
Terminal-Bench measures whether a model can complete real, end-to-end tasks in a command-line environment — running commands, editing files, and chaining steps — making it an agentic test of practical software skill.
9.2%
9.2%
median 31.7%open leader 52.4%
Overall best: GPT 5.5 (unspecified) 84.7%
7th percentile · #15 of 16 open · #56 of 57 all
#15 of 16
#56 of 57
Lab-reported
Epoch AI · Feb 2026
SciCode
SciCode asks a model to write real scientific-computing code — implementing multi-step numerical methods drawn from physics, chemistry, biology, and math research — and checks the output against reference solutions. It was built by a team of scientists and ML researchers to test coding ability on genuine research problems, not typical software tasks.
29.5%
29.5%
median 40.1%open leader 60.9%
Overall best: Claude Opus 5.5 Max 66.9%
18th percentile · #59 of 72 open · #200 of 217 all
#59 of 72
#200 of 217
Lab-reported
Epoch AI · Feb 2026

Qwen3.5 9B math benchmarks

Symbolic and quantitative problem-solving, from competition math to multi-step calculation.

AIME 2024/2025
AIME (American Invitational Mathematics Examination) is a prestigious high-school competition of hard, integer-answer problems. It's a widely-cited yardstick for multi-step mathematical reasoning, here on the 2024–2025 papers.
61.7%
61.7%
median 54.4%open leader 99.4%
Overall best: Claude Fable 5 (high) 100.0%
56th percentile · #39 of 88 open · #173 of 292 all
#39 of 88
#173 of 292
Independent
Epoch AI · Feb 2026

Qwen3.5 9B knowledge benchmarks

Breadth and depth of factual knowledge across many subjects, paired with the reasoning to apply it.

MMLU-Pro
MMLU-Pro is a harder, cleaned-up successor to MMLU with ten answer choices and more reasoning-heavy questions across 14 subjects, measuring broad knowledge and reasoning together.
82.5%
82.5%
median 52.0%open leader 88.0%
Overall best: Gemini-3.1-Pro 91.2%
89th percentile · #17 of 146 open · #53 of 257 all
#17 of 146
#53 of 257
Independent
TIGER-Lab

Score vs VRAM in the 24 GB tier

Qwen3.5 9B
Other models
Best Score for its VRAM
x: VRAM (GB) · y: llmrun Score

#10 of 36 that fit 24 GB

Each dot is a model at the best quant that fits a 24 GB GPU. The line steps up to the best Score available at each amount of VRAM; a dot below it is beaten by a model that needs no more memory.

Full 24 GB ranking →

Qwen3.5 9B vs its neighbours

Same boards for the nearest open models per llmrun Score and the best model that fits 24 GB. The highest score in each row is marked.

Board	Qwen3.5 9Bthis model	Qwen3.8 27Bbest in classQ4_K_M · 18 GB	Qwen3.5 4Bnearest by ScoreQ4_K_M · 3.4 GB	Qwen3 14Bnearest by ScoreQ4_K_M · 12 GB	GPT OSS 20Bnearest by ScoreQ4_K_M · 13 GB
llmrun Score	99.4	122.0	98.5	98.0	96.4
GPQA Diamond	79.0%	—	—	63.8%	60.8%
CritPt	0.6%	5.4%	0.0%	0.0%	1.4%
DTBench	71.2%	88.0%	—	64.0%	68.0%
LMCA	24.5%	41.4%	—	18.2%	14.5%
Terminal-Bench	9.2%	—	—	—	3.4%
SciCode	29.5%	46.6%	—	31.6%	38.9%
AIME 2024/2025	61.7%	—	55.8%	66.4%	65.3%
MMLU-Pro	82.5%	—	79.1%	—	73.6%
Compare head to head		Compare	Compare	Compare	Compare

Scores aggregated from public benchmark sources (Epoch AI: 7 boards, TIGER-Lab: 1 board) — each linked from the benchmark pages. llmrun does not run these benchmarks. Percentiles and ranks are among open models in the llmrun catalogue; the median and leader marks refer to the same field.