Model comparison

GPT-5.5 vs Qwen2.5-Coder-32B

GPT-5.5 is the stronger model overall, scoring 63.4 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 15× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.

Last verified October 10, 2026. 13 shared benchmarks.

GPT-5.5 OpenAI

63.4

Rank #9 Confirmed

Qwen2.5-Coder-32B

Alibaba (Qwen)

33.4

Rank #245 Confirmed

Summary

They share 13 benchmarks with published results for both. GPT-5.5 scores higher in 8 categories and Qwen2.5-Coder-32B in 0 categories; 8 gaps are clear of the uncertainty.
The widest gap is in reasoning, where GPT-5.5 leads 72.8 to 21.2.
Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $5 / $30 for GPT-5.5.
GPT-5.5 accepts more context: 1.05M tokens versus 33K.
Qwen2.5-Coder-32B has downloadable open weights; the other is API-only.

Side by side

GPT-5.5 and Qwen2.5-Coder-32B specifications
	GPT-5.5	Qwen2.5-Coder-32B
Provider	OpenAI	Alibaba (Qwen)
Noometry Index	63.4	33.4
Released	2026-04-23	2024-09-18
Weights	Proprietary	Open
Context window	1.05M	33K
Max output	128K	29K
Input $ / M tokens	$5	$0.66
Output $ / M tokens	$30	$1
Results tracked	71	31

Sponsored placements are available on pages like this one. Advertise on Noometry

Category by category

Coding GPT-5.5 leads

GPT-5.5: 58.2 (#17), Qwen2.5-Coder-32B: 22.6 (#333)

Coding benchmarks
Benchmark	GPT-5.5	Qwen2.5-Coder-32B
LMArena Coding	1494	1276
SWE-bench Verified	80.6%	—
DeepSWE	67%	—
FrontierCode	43%	—
SWE-bench Verified (bash only)	—	9%
Aider Polyglot	—	16.4%
LMArena WebDev	1513	—
SciCode	56.1%	—
GSO	40.2%	—
WeirdML	84.9%	—
BigCodeBench Instruct	—	49%
LiveBench Coding	—	56.9%
MirrorCode	10%	—
BigCodeBench Complete	—	58%
ALE-Bench	1,943	—
HumanEval+	—	87.2%
MBPP+	—	77%

Agentic & Tool Use Not comparable

GPT-5.5: 50.7 (#6), Qwen2.5-Coder-32B: —

Agentic & Tool Use benchmarks
Benchmark	GPT-5.5	Qwen2.5-Coder-32B
Terminal-Bench	84.7%	—
APEX-Agents	55.1%	—
OSWorld 2.0	13%	—
Remote Labor Index	6.3%	—
τ²-bench Banking	44.6%	—
DeepResearch Bench	54%	—
PostTrainBench	27.2%	—
ExploitBench	47.4%	—
GBAEval	53.2%	—
GDP.pdf	26%	—
LMArena Search	1242	—
Vending-Bench 2	7,524	—

Reasoning GPT-5.5 leads

GPT-5.5: 72.8 (#11), Qwen2.5-Coder-32B: 21.2 (#225)

Reasoning benchmarks
Benchmark	GPT-5.5	Qwen2.5-Coder-32B
LMArena Hard Prompts	1489	1251
Epoch Capabilities Index	159.1	119.49
ARC-AGI-2	85%	—
SimpleBench	69%	—
Kagi LLM Benchmark	88.8%	—
NYT Connections (extended)	96.2%	—
ARC-AGI-1	95%	—
CritPt	27.1%	—
Chess Puzzles	54%	—
EBR-Bench	34.3%	—
LiveBench Reasoning	—	42.1%
Mystery Game Puzzles	56%	—
DTBench	96%	—
LiveBench Data Analysis	—	49.9%
LMCA	54.3%	—
Surface Evolver Bench	88.1%	—
Bench to the Future 3	0.14	—
ForecastBench	60.6	—
HellaSwag	—	83%
LiveBench	—	46.2%
WinoGrande	—	80.8%

Math GPT-5.5 leads

GPT-5.5: 81.7 (#11), Qwen2.5-Coder-32B: 33.3 (#204)

Math benchmarks
Benchmark	GPT-5.5	Qwen2.5-Coder-32B
LMArena Math	1486	1251
FrontierMath (Tiers 1-3)	85.3%	—
FrontierMath Tier 4	72.5%	—
MathArena Final-Answer Competitions	94.3%	—
OTIS Mock AIME 2024-2025	100%	—
ProofBench	50%	—
LiveBench Math	—	46.6%
FrontierMath (Feb 2025 set)	51.7%	—
FrontierMath Erdős	0%	—
FrontierMath Tier 4 (v1)	35.4%	—
GSM8K	—	93%

Knowledge GPT-5.5 leads

GPT-5.5: 64.4 (#17), Qwen2.5-Coder-32B: 33.4 (#203)

Knowledge benchmarks
Benchmark	GPT-5.5	Qwen2.5-Coder-32B
LMArena Expert	1508	1221
GPQA Diamond	94%	—
SimpleQA Verified	63%	—
Vectara Hallucination Rate	9.3%	—
ARC (AI2) Challenge	—	70.5%
MMLU	—	79.1%

Multimodal Not comparable

GPT-5.5: 46.9 (#12), Qwen2.5-Coder-32B: —

Multimodal benchmarks
Benchmark	GPT-5.5	Qwen2.5-Coder-32B
LMArena Vision	1297	—
Blueprint-Bench 2	36.2%	—
Furniture Assembly	44.2%	—
LMArena Document	1486	—

Multilingual GPT-5.5 leads

GPT-5.5: 56.4 (#20), Qwen2.5-Coder-32B: 37.8 (#235)

Multilingual benchmarks
Benchmark	GPT-5.5	Qwen2.5-Coder-32B
LMArena Non-English	1467	1205
LMArena Chinese	1533	1222
LMArena Russian	1473	1228
LMArena French	1486	—
LMArena German	1480	—
LMArena Japanese	1498	—
LMArena Korean	1460	—
LMArena Spanish	1468	—

Instruction Following GPT-5.5 leads

GPT-5.5: 77.5 (#18), Qwen2.5-Coder-32B: 61.4 (#245)

Instruction Following benchmarks
Benchmark	GPT-5.5	Qwen2.5-Coder-32B
LMArena Instruction Following	1479	1223
LiveBench Instruction Following	—	58.7%

Long Context GPT-5.5 leads

GPT-5.5: 48.3 (#12), Qwen2.5-Coder-32B: 38.0 (#208)

Long Context benchmarks
Benchmark	GPT-5.5	Qwen2.5-Coder-32B
LMArena Longer Query	1484	1251
CL-bench Life	22.2%	—

Writing & Preference GPT-5.5 leads

GPT-5.5: 72.7 (#13), Qwen2.5-Coder-32B: 41.6 (#240)

Writing & Preference benchmarks
Benchmark	GPT-5.5	Qwen2.5-Coder-32B
LMArena Text	1472	1230
LMArena Creative Writing	1455	1174
LMArena Multi-Turn	1476	1222
EQ-Bench Creative Writing	1844	—
EQ-Bench 4	1315	—
LiveBench Language	—	23.3%

Frequently asked questions

Is GPT-5.5 better than Qwen2.5-Coder-32B?

Which is cheaper, GPT-5.5 or Qwen2.5-Coder-32B?

Qwen2.5-Coder-32B is cheaper. It lists at $0.66 per million input tokens and $1 per million output tokens; GPT-5.5 lists at $5 and $30.

Is GPT-5.5 or Qwen2.5-Coder-32B better for coding?

GPT-5.5 scores higher on coding benchmarks: 58.2 versus 22.6 in the Noometry coding category.

Which has the bigger context window?

GPT-5.5 does, with 1.05M tokens against 33K.

How many benchmarks do GPT-5.5 and Qwen2.5-Coder-32B share?

13 benchmarks have published results for both models. GPT-5.5 has 71 scored results on Noometry and Qwen2.5-Coder-32B has 31.