Model comparison

GPT-5.5 vs Qwen3-Coder 480B-A35B Instruct

GPT-5.5 is the stronger model overall, scoring 63.4 to 38.1 on the Noometry Index. Qwen3-Coder 480B-A35B Instruct costs 3.8× 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. 23 shared benchmarks.

GPT-5.5 OpenAI

63.4

Rank #9 Confirmed

Qwen3-Coder 480B-A35B Instruct

Alibaba (Qwen)

38.1

Rank #190 Confirmed

Summary

They share 23 benchmarks with published results for both. GPT-5.5 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-5.5 leads 72.8 to 25.5.
The biggest single-benchmark swing is Terminal-Bench: 84.7% for GPT-5.5 and 27.2% for Qwen3-Coder 480B-A35B Instruct.
Qwen3-Coder 480B-A35B Instruct is cheaper at $1.50 / $7.50 per million input/output tokens, against $5 / $30 for GPT-5.5.
GPT-5.5 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-5.5 and Qwen3-Coder 480B-A35B Instruct specifications
	GPT-5.5	Qwen3-Coder 480B-A35B Instruct
Provider	OpenAI	Alibaba (Qwen)
Noometry Index	63.4	38.1
Released	2026-04-23	2025-04
Weights	Proprietary	Open
Context window	1.05M	262K
Max output	128K	66K
Input $ / M tokens	$5	$1.50
Output $ / M tokens	$30	$7.50
Results tracked	71	25

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Category by category

Coding GPT-5.5 leads

GPT-5.5: 58.2 (#17), Qwen3-Coder 480B-A35B Instruct: 35.5 (#223)

Coding benchmarks
Benchmark	GPT-5.5	Qwen3-Coder 480B-A35B Instruct
LMArena WebDev	1513	1275
GSO	40.2%	4.9%
WeirdML	84.9%	41.2%
LMArena Coding	1494	1412
ALE-Bench	1,943	461.45
SWE-bench Verified	80.6%	—
DeepSWE	67%	—
FrontierCode	43%	—
SWE-bench Verified (bash only)	—	55.4%
SciCode	56.1%	—
MirrorCode	10%	—
AlgoTune	—	1.44

Agentic & Tool Use GPT-5.5 leads

GPT-5.5: 50.7 (#6), Qwen3-Coder 480B-A35B Instruct: 23.9 (#123)

Agentic & Tool Use benchmarks
Benchmark	GPT-5.5	Qwen3-Coder 480B-A35B Instruct
Terminal-Bench	84.7%	27.2%
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), Qwen3-Coder 480B-A35B Instruct: 25.5 (#149)

Reasoning benchmarks
Benchmark	GPT-5.5	Qwen3-Coder 480B-A35B Instruct
Kagi LLM Benchmark	88.8%	49.5%
LMArena Hard Prompts	1489	1372
ARC-AGI-2	85%	—
SimpleBench	69%	—
NYT Connections (extended)	96.2%	—
ARC-AGI-1	95%	—
CritPt	27.1%	—
Chess Puzzles	54%	—
EBR-Bench	34.3%	—
Mystery Game Puzzles	56%	—
DTBench	96%	—
LMCA	54.3%	—
Surface Evolver Bench	88.1%	—
Bench to the Future 3	0.14	—
Epoch Capabilities Index	159.1	—
ForecastBench	60.6	—

Math GPT-5.5 leads

GPT-5.5: 81.7 (#11), Qwen3-Coder 480B-A35B Instruct: 37.6 (#150)

Math benchmarks
Benchmark	GPT-5.5	Qwen3-Coder 480B-A35B Instruct
LMArena Math	1486	1365
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%	—
FrontierMath (Feb 2025 set)	51.7%	—
FrontierMath Erdős	0%	—
FrontierMath Tier 4 (v1)	35.4%	—

Knowledge GPT-5.5 leads

GPT-5.5: 64.4 (#17), Qwen3-Coder 480B-A35B Instruct: 37.0 (#162)

Knowledge benchmarks
Benchmark	GPT-5.5	Qwen3-Coder 480B-A35B Instruct
LMArena Expert	1508	1338
GPQA Diamond	94%	—
SimpleQA Verified	63%	—
Vectara Hallucination Rate	9.3%	—

Multimodal Not comparable

GPT-5.5: 46.9 (#12), Qwen3-Coder 480B-A35B Instruct: —

Multimodal benchmarks
Benchmark	GPT-5.5	Qwen3-Coder 480B-A35B Instruct
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), Qwen3-Coder 480B-A35B Instruct: 47.7 (#148)

Multilingual benchmarks
Benchmark	GPT-5.5	Qwen3-Coder 480B-A35B Instruct
LMArena Non-English	1467	1346
LMArena Chinese	1533	1357
LMArena French	1486	1398
LMArena German	1480	1325
LMArena Japanese	1498	1310
LMArena Korean	1460	1305
LMArena Russian	1473	1366
LMArena Spanish	1468	1360

Instruction Following GPT-5.5 leads

GPT-5.5: 77.5 (#18), Qwen3-Coder 480B-A35B Instruct: 71.6 (#147)

Instruction Following benchmarks
Benchmark	GPT-5.5	Qwen3-Coder 480B-A35B Instruct
LMArena Instruction Following	1479	1355

Long Context GPT-5.5 leads

GPT-5.5: 48.3 (#12), Qwen3-Coder 480B-A35B Instruct: 42.0 (#131)

Long Context benchmarks
Benchmark	GPT-5.5	Qwen3-Coder 480B-A35B Instruct
LMArena Longer Query	1484	1378
CL-bench Life	22.2%	—

Writing & Preference GPT-5.5 leads

GPT-5.5: 72.7 (#13), Qwen3-Coder 480B-A35B Instruct: 55.3 (#147)

Writing & Preference benchmarks
Benchmark	GPT-5.5	Qwen3-Coder 480B-A35B Instruct
LMArena Text	1472	1357
LMArena Creative Writing	1455	1333
LMArena Multi-Turn	1476	1365
EQ-Bench Creative Writing	1844	—
EQ-Bench 4	1315	—

Frequently asked questions

Is GPT-5.5 better than Qwen3-Coder 480B-A35B Instruct?

Which is cheaper, GPT-5.5 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-5.5 lists at $5 and $30.

Is GPT-5.5 or Qwen3-Coder 480B-A35B Instruct better for coding?

GPT-5.5 scores higher on coding benchmarks: 58.2 versus 35.5 in the Noometry coding category.

Which has the bigger context window?

GPT-5.5 does, with 1.05M tokens against 262K.

How many benchmarks do GPT-5.5 and Qwen3-Coder 480B-A35B Instruct share?

23 benchmarks have published results for both models. GPT-5.5 has 71 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.