Model comparison

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

GPT-5 is the stronger model overall, scoring 50.9 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 4.6× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.

Last verified October 11, 2026. 15 shared benchmarks.

GPT-5 OpenAI

50.9

Rank #45 Confirmed

Qwen2.5-Coder-32B

Alibaba (Qwen)

33.4

Rank #245 Confirmed

Summary

They share 15 benchmarks with published results for both. GPT-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 long context, where GPT-5 leads 69.5 to 38.0.
The biggest single-benchmark swing is Aider Polyglot: 88% for GPT-5 and 16.4% for Qwen2.5-Coder-32B.
Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $1.25 / $10 for GPT-5.
GPT-5 accepts more context: 400K tokens versus 33K.
Qwen2.5-Coder-32B has downloadable open weights; the other is API-only.

Side by side

GPT-5 and Qwen2.5-Coder-32B specifications
	GPT-5	Qwen2.5-Coder-32B
Provider	OpenAI	Alibaba (Qwen)
Noometry Index	50.9	33.4
Released	2025-08-07	2024-09-18
Weights	Proprietary	Open
Context window	400K	33K
Max output	128K	29K
Input $ / M tokens	$1.25	$0.66
Output $ / M tokens	$10	$1
Results tracked	69	31

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Category by category

Coding GPT-5 leads

GPT-5: 50.3 (#47), Qwen2.5-Coder-32B: 22.6 (#333)

Coding benchmarks
Benchmark	GPT-5	Qwen2.5-Coder-32B
SWE-bench Verified (bash only)	65%	9%
Aider Polyglot	88%	16.4%
LMArena Coding	1436	1276
SWE-bench Verified	73.6%	—
LMArena WebDev	1418	—
SciCode	42.9%	—
GSO	6.9%	—
WeirdML	60.7%	—
BigCodeBench Instruct	—	49%
LiveBench Coding	—	56.9%
BigCodeBench Complete	—	58%
ALE-Bench	1,162	—
AlgoTune	1.67	—
HumanEval+	—	87.2%
MBPP+	—	77%

Agentic & Tool Use Not comparable

GPT-5: 33.1 (#56), Qwen2.5-Coder-32B: —

Agentic & Tool Use benchmarks
Benchmark	GPT-5	Qwen2.5-Coder-32B
Terminal-Bench	49.6%	—
GDPval	34.8%	—
Remote Labor Index	1.7%	—
DeepResearch Bench	49.6%	—
BALROG	32.8%	—
LMArena Search	1133	—
METR Time Horizons	69.6%	—

Reasoning GPT-5 leads

GPT-5: 38.3 (#64), Qwen2.5-Coder-32B: 21.2 (#225)

Reasoning benchmarks
Benchmark	GPT-5	Qwen2.5-Coder-32B
LMArena Hard Prompts	1416	1251
Epoch Capabilities Index	150	119.49
ARC-AGI-2	9.9%	—
SimpleBench	56.7%	—
Kagi LLM Benchmark	72.7%	—
ARC-AGI-1	65.7%	—
CritPt	12.6%	—
Chess Puzzles	37%	—
EnigmaEval	10.5%	—
EBR-Bench	12.7%	—
LiveBench Reasoning	—	42.1%
Mystery Game Puzzles	23%	—
DTBench	90.7%	—
LiveBench Data Analysis	—	49.9%
LMCA	40%	—
ForecastBench	61.4	—
HellaSwag	—	83%
LiveBench	—	46.2%
WinoGrande	—	80.8%

Math GPT-5 leads

GPT-5: 55.0 (#44), Qwen2.5-Coder-32B: 33.3 (#204)

Math benchmarks
Benchmark	GPT-5	Qwen2.5-Coder-32B
LMArena Math	1407	1251
FrontierMath (Tiers 1-3)	55.4%	—
FrontierMath Tier 4	22%	—
OTIS Mock AIME 2024-2025	91.4%	—
ProofBench	18%	—
Omni-MATH	64.7%	—
LiveBench Math	—	46.6%
MATH Level 5	98.1%	—
FrontierMath (Feb 2025 set)	32.4%	—
FrontierMath Tier 4 (v1)	12.5%	—
GSM8K	—	93%

Knowledge GPT-5 leads

GPT-5: 56.6 (#43), Qwen2.5-Coder-32B: 33.4 (#203)

Knowledge benchmarks
Benchmark	GPT-5	Qwen2.5-Coder-32B
LMArena Expert	1419	1221
GPQA Diamond	86.2%	—
Humanity's Last Exam	25.3%	—
SimpleQA Verified	50.1%	—
MMLU-Pro	86.3%	—
Confabulations	10.3%	—
Vectara Hallucination Rate	14.7%	—
GPQA (HELM)	79.2%	—
ARC (AI2) Challenge	—	70.5%
MMLU	—	79.1%

Multimodal Not comparable

GPT-5: 46.8 (#13), Qwen2.5-Coder-32B: —

Multimodal benchmarks
Benchmark	GPT-5	Qwen2.5-Coder-32B
LMArena Vision	1232	—
GeoBench	81%	—
VPCT	66%	—

Multilingual GPT-5 leads

GPT-5: 51.4 (#110), Qwen2.5-Coder-32B: 37.8 (#235)

Multilingual benchmarks
Benchmark	GPT-5	Qwen2.5-Coder-32B
LMArena Non-English	1397	1205
LMArena Chinese	1422	1222
LMArena Russian	1406	1228
LMArena French	1410	—
LMArena German	1416	—
LMArena Japanese	1409	—
LMArena Korean	1360	—
LMArena Spanish	1399	—

Instruction Following GPT-5 leads

GPT-5: 73.8 (#113), Qwen2.5-Coder-32B: 61.4 (#245)

Instruction Following benchmarks
Benchmark	GPT-5	Qwen2.5-Coder-32B
LMArena Instruction Following	1388	1223
LiveBench Instruction Following	—	58.7%
IFEval	87.5%	—

Long Context GPT-5 leads

GPT-5: 69.5 (#2), Qwen2.5-Coder-32B: 38.0 (#208)

Long Context benchmarks
Benchmark	GPT-5	Qwen2.5-Coder-32B
LMArena Longer Query	1399	1251
Fiction.LiveBench	97.2%	—

Writing & Preference GPT-5 leads

GPT-5: 63.4 (#65), Qwen2.5-Coder-32B: 41.6 (#240)

Writing & Preference benchmarks
Benchmark	GPT-5	Qwen2.5-Coder-32B
LMArena Text	1406	1230
LMArena Creative Writing	1365	1174
LMArena Multi-Turn	1426	1222
Short-Story Creative Writing	86%	—
EQ-Bench Creative Writing	1627	—
WildBench	85.7%	—
LiveBench Language	—	23.3%

Frequently asked questions

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

Which is cheaper, GPT-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 lists at $1.25 and $10.

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

GPT-5 scores higher on coding benchmarks: 50.3 versus 22.6 in the Noometry coding category.

Which has the bigger context window?

GPT-5 does, with 400K tokens against 33K.

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

15 benchmarks have published results for both models. GPT-5 has 69 scored results on Noometry and Qwen2.5-Coder-32B has 31.