Model comparison

GPT-5.6 Sol vs Qwen2.5-Coder-32B

GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 11× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.

Last verified October 11, 2026. 13 shared benchmarks.

GPT-5.6 Sol OpenAI

65.0

Rank #7 Confirmed

Qwen2.5-Coder-32B

Alibaba (Qwen)

33.4

Rank #245 Confirmed

Summary

They share 13 benchmarks with published results for both. GPT-5.6 Sol 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-5.6 Sol leads 74.8 to 21.2.
Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
GPT-5.6 Sol 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-5.6 Sol and Qwen2.5-Coder-32B specifications
	GPT-5.6 Sol	Qwen2.5-Coder-32B
Provider	OpenAI	Alibaba (Qwen)
Noometry Index	65.0	33.4
Released	2026-07-09	2024-09-18
Weights	Proprietary	Open
Context window	1.05M	33K
Max output	128K	29K
Input $ / M tokens	$4	$0.66
Output $ / M tokens	$20	$1
Results tracked	65	31

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Category by category

Coding GPT-5.6 Sol leads

GPT-5.6 Sol: 65.1 (#7), Qwen2.5-Coder-32B: 22.6 (#333)

Coding benchmarks
Benchmark	GPT-5.6 Sol	Qwen2.5-Coder-32B
LMArena Coding	1498	1276
DeepSWE	72.7%	—
FrontierCode	47.5%	—
SWE-bench Verified (bash only)	—	9%
Aider Polyglot	—	16.4%
CursorBench	41.7%	—
LMArena WebDev	1618	—
FrontierSWE	32.2%	—
SciCode	57.1%	—
GSO	76.5%	—
WeirdML	89.4%	—
BigCodeBench Instruct	—	49%
LiveBench Coding	—	56.9%
MirrorCode	20%	—
BigCodeBench Complete	—	58%
ALE-Bench	2,177	—
HumanEval+	—	87.2%
MBPP+	—	77%

Agentic & Tool Use Not comparable

GPT-5.6 Sol: 50.3 (#7), Qwen2.5-Coder-32B: —

Agentic & Tool Use benchmarks
Benchmark	GPT-5.6 Sol	Qwen2.5-Coder-32B
APEX-Agents	51.4%	—
OSWorld 2.0	27.3%	—
τ²-bench Banking	46.9%	—
PostTrainBench	36.2%	—
BALROG	60%	—
GBAEval	52.6%	—
GDP.pdf	30.7%	—
LMArena Search	1257	—
Vending-Bench 2	9,619	—

Reasoning GPT-5.6 Sol leads

GPT-5.6 Sol: 74.8 (#8), Qwen2.5-Coder-32B: 21.2 (#225)

Reasoning benchmarks
Benchmark	GPT-5.6 Sol	Qwen2.5-Coder-32B
LMArena Hard Prompts	1484	1251
Epoch Capabilities Index	161.66	119.49
ARC-AGI-2	92.5%	—
SimpleBench	71.7%	—
Kagi LLM Benchmark	67%	—
NYT Connections (extended)	93.8%	—
ARC-AGI-1	97.5%	—
CritPt	32.3%	—
Chess Puzzles	64%	—
EnigmaEval	37.1%	—
EBR-Bench	44.8%	—
LiveBench Reasoning	—	42.1%
Mystery Game Puzzles	58%	—
DTBench	96%	—
LiveBench Data Analysis	—	49.9%
LMCA	59.2%	—
Surface Evolver Bench	93.1%	—
Bench to the Future 3	0.14	—
HellaSwag	—	83%
LiveBench	—	46.2%
WinoGrande	—	80.8%

Math GPT-5.6 Sol leads

GPT-5.6 Sol: 85.6 (#9), Qwen2.5-Coder-32B: 33.3 (#204)

Math benchmarks
Benchmark	GPT-5.6 Sol	Qwen2.5-Coder-32B
LMArena Math	1474	1251
FrontierMath (Tiers 1-3)	89.1%	—
FrontierMath Tier 4	82.9%	—
OTIS Mock AIME 2024-2025	100%	—
ProofBench	83%	—
LiveBench Math	—	46.6%
FrontierMath Erdős	0%	—
GSM8K	—	93%

Knowledge GPT-5.6 Sol leads

GPT-5.6 Sol: 64.3 (#18), Qwen2.5-Coder-32B: 33.4 (#203)

Knowledge benchmarks
Benchmark	GPT-5.6 Sol	Qwen2.5-Coder-32B
LMArena Expert	1516	1221
GPQA Diamond	93.5%	—
SimpleQA Verified	69.7%	—
Vectara Hallucination Rate	12.4%	—
ARC (AI2) Challenge	—	70.5%
MMLU	—	79.1%

Multimodal Not comparable

GPT-5.6 Sol: 48.6 (#9), Qwen2.5-Coder-32B: —

Multimodal benchmarks
Benchmark	GPT-5.6 Sol	Qwen2.5-Coder-32B
LMArena Vision	1281	—
Blueprint-Bench 2	33.6%	—
Furniture Assembly	56.7%	—
LMArena Document	1483	—

Multilingual GPT-5.6 Sol leads

GPT-5.6 Sol: 55.3 (#32), Qwen2.5-Coder-32B: 37.8 (#235)

Multilingual benchmarks
Benchmark	GPT-5.6 Sol	Qwen2.5-Coder-32B
LMArena Non-English	1452	1205
LMArena Chinese	1527	1222
LMArena Russian	1468	1228
LMArena French	1477	—
LMArena German	1476	—
LMArena Japanese	1471	—
LMArena Korean	1442	—
LMArena Spanish	1441	—

Instruction Following GPT-5.6 Sol leads

GPT-5.6 Sol: 77.7 (#16), Qwen2.5-Coder-32B: 61.4 (#245)

Instruction Following benchmarks
Benchmark	GPT-5.6 Sol	Qwen2.5-Coder-32B
LMArena Instruction Following	1482	1223
LiveBench Instruction Following	—	58.7%

Long Context GPT-5.6 Sol leads

GPT-5.6 Sol: 45.4 (#42), Qwen2.5-Coder-32B: 38.0 (#208)

Long Context benchmarks
Benchmark	GPT-5.6 Sol	Qwen2.5-Coder-32B
LMArena Longer Query	1480	1251

Writing & Preference GPT-5.6 Sol leads

GPT-5.6 Sol: 73.3 (#12), Qwen2.5-Coder-32B: 41.6 (#240)

Writing & Preference benchmarks
Benchmark	GPT-5.6 Sol	Qwen2.5-Coder-32B
LMArena Text	1457	1230
LMArena Creative Writing	1448	1174
LMArena Multi-Turn	1460	1222
EQ-Bench Creative Writing	1972	—
EQ-Bench 4	1250	—
LiveBench Language	—	23.3%

Frequently asked questions

Is GPT-5.6 Sol better than Qwen2.5-Coder-32B?

Which is cheaper, GPT-5.6 Sol 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-5.6 Sol lists at $4 and $20.

Is GPT-5.6 Sol or Qwen2.5-Coder-32B better for coding?

GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 22.6 in the Noometry coding category.

Which has the bigger context window?

GPT-5.6 Sol does, with 1.05M tokens against 33K.

How many benchmarks do GPT-5.6 Sol and Qwen2.5-Coder-32B share?

13 benchmarks have published results for both models. GPT-5.6 Sol has 65 scored results on Noometry and Qwen2.5-Coder-32B has 31.