Model comparison

DeepSeek-V3 vs GPT-5.6 Sol

GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 39.5 on the Noometry Index. DeepSeek-V3 costs 20× 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. 29 shared benchmarks.

DeepSeek-V3

DeepSeek

39.5

Rank #166 Confirmed

GPT-5.6 Sol OpenAI

65.0

Rank #7 Confirmed

Summary

They share 29 benchmarks with published results for both. DeepSeek-V3 scores higher in 0 categories and GPT-5.6 Sol in 8 categories; 8 gaps are clear of the uncertainty.
The widest gap is in reasoning, where GPT-5.6 Sol leads 74.8 to 20.5.
The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for DeepSeek-V3 and 100% for GPT-5.6 Sol.
DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
GPT-5.6 Sol accepts more context: 1.05M tokens versus 164K.
DeepSeek-V3 has downloadable open weights; the other is API-only.

Side by side

DeepSeek-V3 and GPT-5.6 Sol specifications
	DeepSeek-V3	GPT-5.6 Sol
Provider	DeepSeek	OpenAI
Noometry Index	39.5	65.0
Released	2024-12-26	2026-07-09
Weights	Open	Proprietary
Context window	164K	1.05M
Max output	164K	128K
Input $ / M tokens	$0.24	$4
Output $ / M tokens	$0.90	$20
Results tracked	60	65

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Category by category

Coding GPT-5.6 Sol leads

DeepSeek-V3: 42.3 (#106), GPT-5.6 Sol: 65.1 (#7)

Coding benchmarks
Benchmark	DeepSeek-V3	GPT-5.6 Sol
SciCode	35.8%	57.1%
WeirdML	36.1%	89.4%
LMArena Coding	1368	1498
DeepSWE	—	72.7%
FrontierCode	—	47.5%
Aider Polyglot	55.1%	—
CursorBench	—	41.7%
LMArena WebDev	—	1618
FrontierSWE	—	32.2%
GSO	—	76.5%
BigCodeBench Instruct	50%	—
LiveBench Coding	70.9%	—
MirrorCode	—	20%
BigCodeBench Complete	62.2%	—
ALE-Bench	—	2,177
HumanEval+	86.6%	—
MBPP+	73%	—

Agentic & Tool Use Not comparable

DeepSeek-V3: —, GPT-5.6 Sol: 50.3 (#7)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3	GPT-5.6 Sol
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
METR Time Horizons	49.6%	—
Vending-Bench 2	—	9,619

Reasoning GPT-5.6 Sol leads

DeepSeek-V3: 20.5 (#236), GPT-5.6 Sol: 74.8 (#8)

Reasoning benchmarks
Benchmark	DeepSeek-V3	GPT-5.6 Sol
SimpleBench	27.2%	71.7%
Kagi LLM Benchmark	52.3%	67%
CritPt	0%	32.3%
LMArena Hard Prompts	1365	1484
DTBench	64.8%	96%
LMCA	15.5%	59.2%
Epoch Capabilities Index	135.94	161.66
ARC-AGI-2	—	92.5%
NYT Connections (extended)	—	93.8%
ARC-AGI-1	—	97.5%
Chess Puzzles	—	64%
EnigmaEval	—	37.1%
EBR-Bench	—	44.8%
LiveBench Reasoning	65.8%	—
Mystery Game Puzzles	—	58%
LiveBench Data Analysis	60.9%	—
Surface Evolver Bench	—	93.1%
Bench to the Future 3	—	0.14
BIG-Bench Hard	87.5%	—
ForecastBench	59.1	—
HellaSwag	88.9%	—
LiveBench	66.9%	—
PIQA	84.7%	—
WinoGrande	85.2%	—

Math GPT-5.6 Sol leads

DeepSeek-V3: 32.1 (#219), GPT-5.6 Sol: 85.6 (#9)

Math benchmarks
Benchmark	DeepSeek-V3	GPT-5.6 Sol
OTIS Mock AIME 2024-2025	37.8%	100%
LMArena Math	1373	1474
FrontierMath (Tiers 1-3)	—	89.1%
FrontierMath Tier 4	—	82.9%
ProofBench	—	83%
Omni-MATH	40.3%	—
LiveBench Math	73.5%	—
MATH Level 5	75.5%	—
FrontierMath (Feb 2025 set)	1.7%	—
FrontierMath Erdős	—	0%

Knowledge GPT-5.6 Sol leads

DeepSeek-V3: 37.5 (#155), GPT-5.6 Sol: 64.3 (#18)

Knowledge benchmarks
Benchmark	DeepSeek-V3	GPT-5.6 Sol
GPQA Diamond	67.6%	93.5%
Vectara Hallucination Rate	6.1%	12.4%
LMArena Expert	1351	1516
SimpleQA Verified	—	69.7%
MMLU-Pro	72.3%	—
Confabulations	26.1%	—
GPQA (HELM)	53.8%	—
ARC (AI2) Challenge	95.3%	—
MMLU	87.2%	—
TriviaQA	82.9%	—

Multimodal Not comparable

DeepSeek-V3: —, GPT-5.6 Sol: 48.6 (#9)

Multimodal benchmarks
Benchmark	DeepSeek-V3	GPT-5.6 Sol
LMArena Vision	—	1281
Blueprint-Bench 2	—	33.6%
Furniture Assembly	—	56.7%
LMArena Document	—	1483

Multilingual GPT-5.6 Sol leads

DeepSeek-V3: 48.5 (#143), GPT-5.6 Sol: 55.3 (#32)

Multilingual benchmarks
Benchmark	DeepSeek-V3	GPT-5.6 Sol
LMArena Non-English	1358	1452
LMArena Chinese	1391	1527
LMArena French	1385	1477
LMArena German	1374	1476
LMArena Japanese	1333	1471
LMArena Korean	1319	1442
LMArena Russian	1373	1468
LMArena Spanish	1358	1441

Instruction Following GPT-5.6 Sol leads

DeepSeek-V3: 72.8 (#130), GPT-5.6 Sol: 77.7 (#16)

Instruction Following benchmarks
Benchmark	DeepSeek-V3	GPT-5.6 Sol
LMArena Instruction Following	1345	1482
LiveBench Instruction Following	81.5%	—
IFEval	83.2%	—

Long Context GPT-5.6 Sol leads

DeepSeek-V3: 34.0 (#253), GPT-5.6 Sol: 45.4 (#42)

Long Context benchmarks
Benchmark	DeepSeek-V3	GPT-5.6 Sol
LMArena Longer Query	1352	1480
Fiction.LiveBench	50%	—

Writing & Preference GPT-5.6 Sol leads

DeepSeek-V3: 57.4 (#130), GPT-5.6 Sol: 73.3 (#12)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3	GPT-5.6 Sol
LMArena Text	1375	1457
LMArena Creative Writing	1364	1448
EQ-Bench Creative Writing	1472	1972
LMArena Multi-Turn	1389	1460
Short-Story Creative Writing	77%	—
WildBench	83%	—
EQ-Bench 4	—	1250
LiveBench Language	49.1%	—

Frequently asked questions

Is DeepSeek-V3 better than GPT-5.6 Sol?

Which is cheaper, DeepSeek-V3 or GPT-5.6 Sol?

DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; GPT-5.6 Sol lists at $4 and $20.

Is DeepSeek-V3 or GPT-5.6 Sol better for coding?

GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 42.3 in the Noometry coding category.

Which has the bigger context window?

GPT-5.6 Sol does, with 1.05M tokens against 164K.

How many benchmarks do DeepSeek-V3 and GPT-5.6 Sol share?

29 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and GPT-5.6 Sol has 65.