Model comparison

GPT-5.6 Sol vs Qwen3-Coder 480B-A35B Instruct

GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 38.1 on the Noometry Index. Qwen3-Coder 480B-A35B Instruct costs 2.7× 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 10, 2026. 22 shared benchmarks.

GPT-5.6 Sol OpenAI

65.0

Rank #7 Confirmed

Qwen3-Coder 480B-A35B Instruct

Alibaba (Qwen)

38.1

Rank #190 Confirmed

Summary

They share 22 benchmarks with published results for both. GPT-5.6 Sol scores higher in 9 categories and Qwen3-Coder 480B-A35B Instruct in 0 categories; 9 gaps are clear of the uncertainty.
The widest gap is in reasoning, where GPT-5.6 Sol leads 74.8 to 25.5.
The biggest single-benchmark swing is GSO: 76.5% for GPT-5.6 Sol and 4.9% for Qwen3-Coder 480B-A35B Instruct.
Qwen3-Coder 480B-A35B Instruct is cheaper at $1.50 / $7.50 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
GPT-5.6 Sol accepts more context: 1.05M tokens versus 262K.
Qwen3-Coder 480B-A35B Instruct has downloadable open weights; the other is API-only.

Side by side

GPT-5.6 Sol and Qwen3-Coder 480B-A35B Instruct specifications
	GPT-5.6 Sol	Qwen3-Coder 480B-A35B Instruct
Provider	OpenAI	Alibaba (Qwen)
Noometry Index	65.0	38.1
Released	2026-07-09	2025-04
Weights	Proprietary	Open
Context window	1.05M	262K
Max output	128K	66K
Input $ / M tokens	$4	$1.50
Output $ / M tokens	$20	$7.50
Results tracked	65	25

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Category by category

Coding GPT-5.6 Sol leads

GPT-5.6 Sol: 65.1 (#7), Qwen3-Coder 480B-A35B Instruct: 35.5 (#223)

Coding benchmarks
Benchmark	GPT-5.6 Sol	Qwen3-Coder 480B-A35B Instruct
LMArena WebDev	1618	1275
GSO	76.5%	4.9%
WeirdML	89.4%	41.2%
LMArena Coding	1498	1412
ALE-Bench	2,177	461.45
DeepSWE	72.7%	—
FrontierCode	47.5%	—
SWE-bench Verified (bash only)	—	55.4%
CursorBench	41.7%	—
FrontierSWE	32.2%	—
SciCode	57.1%	—
MirrorCode	20%	—
AlgoTune	—	1.44

Agentic & Tool Use GPT-5.6 Sol leads

GPT-5.6 Sol: 50.3 (#7), Qwen3-Coder 480B-A35B Instruct: 23.9 (#123)

Agentic & Tool Use benchmarks
Benchmark	GPT-5.6 Sol	Qwen3-Coder 480B-A35B Instruct
Terminal-Bench	—	27.2%
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), Qwen3-Coder 480B-A35B Instruct: 25.5 (#149)

Reasoning benchmarks
Benchmark	GPT-5.6 Sol	Qwen3-Coder 480B-A35B Instruct
Kagi LLM Benchmark	67%	49.5%
LMArena Hard Prompts	1484	1372
ARC-AGI-2	92.5%	—
SimpleBench	71.7%	—
NYT Connections (extended)	93.8%	—
ARC-AGI-1	97.5%	—
CritPt	32.3%	—
Chess Puzzles	64%	—
EnigmaEval	37.1%	—
EBR-Bench	44.8%	—
Mystery Game Puzzles	58%	—
DTBench	96%	—
LMCA	59.2%	—
Surface Evolver Bench	93.1%	—
Bench to the Future 3	0.14	—
Epoch Capabilities Index	161.66	—

Math GPT-5.6 Sol leads

GPT-5.6 Sol: 85.6 (#9), Qwen3-Coder 480B-A35B Instruct: 37.6 (#150)

Math benchmarks
Benchmark	GPT-5.6 Sol	Qwen3-Coder 480B-A35B Instruct
LMArena Math	1474	1365
FrontierMath (Tiers 1-3)	89.1%	—
FrontierMath Tier 4	82.9%	—
OTIS Mock AIME 2024-2025	100%	—
ProofBench	83%	—
FrontierMath Erdős	0%	—

Knowledge GPT-5.6 Sol leads

GPT-5.6 Sol: 64.3 (#18), Qwen3-Coder 480B-A35B Instruct: 37.0 (#162)

Knowledge benchmarks
Benchmark	GPT-5.6 Sol	Qwen3-Coder 480B-A35B Instruct
LMArena Expert	1516	1338
GPQA Diamond	93.5%	—
SimpleQA Verified	69.7%	—
Vectara Hallucination Rate	12.4%	—

Multimodal Not comparable

GPT-5.6 Sol: 48.6 (#9), Qwen3-Coder 480B-A35B Instruct: —

Multimodal benchmarks
Benchmark	GPT-5.6 Sol	Qwen3-Coder 480B-A35B Instruct
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), Qwen3-Coder 480B-A35B Instruct: 47.7 (#148)

Multilingual benchmarks
Benchmark	GPT-5.6 Sol	Qwen3-Coder 480B-A35B Instruct
LMArena Non-English	1452	1346
LMArena Chinese	1527	1357
LMArena French	1477	1398
LMArena German	1476	1325
LMArena Japanese	1471	1310
LMArena Korean	1442	1305
LMArena Russian	1468	1366
LMArena Spanish	1441	1360

Instruction Following GPT-5.6 Sol leads

GPT-5.6 Sol: 77.7 (#16), Qwen3-Coder 480B-A35B Instruct: 71.6 (#147)

Instruction Following benchmarks
Benchmark	GPT-5.6 Sol	Qwen3-Coder 480B-A35B Instruct
LMArena Instruction Following	1482	1355

Long Context GPT-5.6 Sol leads

GPT-5.6 Sol: 45.4 (#42), Qwen3-Coder 480B-A35B Instruct: 42.0 (#131)

Long Context benchmarks
Benchmark	GPT-5.6 Sol	Qwen3-Coder 480B-A35B Instruct
LMArena Longer Query	1480	1378

Writing & Preference GPT-5.6 Sol leads

GPT-5.6 Sol: 73.3 (#12), Qwen3-Coder 480B-A35B Instruct: 55.3 (#147)

Writing & Preference benchmarks
Benchmark	GPT-5.6 Sol	Qwen3-Coder 480B-A35B Instruct
LMArena Text	1457	1357
LMArena Creative Writing	1448	1333
LMArena Multi-Turn	1460	1365
EQ-Bench Creative Writing	1972	—
EQ-Bench 4	1250	—

Frequently asked questions

Is GPT-5.6 Sol better than Qwen3-Coder 480B-A35B Instruct?

Which is cheaper, GPT-5.6 Sol or Qwen3-Coder 480B-A35B Instruct?

Qwen3-Coder 480B-A35B Instruct is cheaper. It lists at $1.50 per million input tokens and $7.50 per million output tokens; GPT-5.6 Sol lists at $4 and $20.

Is GPT-5.6 Sol or Qwen3-Coder 480B-A35B Instruct better for coding?

GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 35.5 in the Noometry coding category.

Which has the bigger context window?

GPT-5.6 Sol does, with 1.05M tokens against 262K.

How many benchmarks do GPT-5.6 Sol and Qwen3-Coder 480B-A35B Instruct share?

22 benchmarks have published results for both models. GPT-5.6 Sol has 65 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.