Model comparison

DeepSeek-V3 vs Mercury 2.5

DeepSeek-V3 is the stronger model overall, scoring 39.5 to 33.5 on the Noometry Index. Mercury 2.5 costs 6.0× less per token, which makes it the better buy when DeepSeek-V3's lead doesn't matter for your workload.

Last verified October 10, 2026. 2 shared benchmarks.

DeepSeek-V3

DeepSeek

39.5

Rank #166 Confirmed

Mercury 2.5 Inception

33.5

Rank #242 Reported

Summary

They share 2 benchmarks with published results for both. DeepSeek-V3 scores higher in 2 categories and Mercury 2.5 in 1 category; 3 gaps are clear of the uncertainty.
The widest gap is in math, where DeepSeek-V3 leads 32.1 to 23.3.
Mercury 2.5 is cheaper at $0.04 / $0.15 per million input/output tokens, against $0.24 / $0.90 for DeepSeek-V3.
Mercury 2.5 accepts more context: 260K tokens versus 164K.
DeepSeek-V3 has downloadable open weights; the other is API-only.

Side by side

DeepSeek-V3 and Mercury 2.5 specifications
	DeepSeek-V3	Mercury 2.5
Provider	DeepSeek	Inception
Noometry Index	39.5	33.5
Released	2024-12-26	2026-09-08
Weights	Open	Proprietary
Context window	164K	260K
Max output	164K	66K
Input $ / M tokens	$0.24	$0.04
Output $ / M tokens	$0.90	$0.15
Results tracked	60	4

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Category by category

Coding DeepSeek-V3 leads

DeepSeek-V3: 42.3 (#106), Mercury 2.5: 39.5 (#156)

Coding benchmarks
Benchmark	DeepSeek-V3	Mercury 2.5
SciCode	35.8%	38.5%
Aider Polyglot	55.1%	—
WeirdML	36.1%	—
BigCodeBench Instruct	50%	—
LiveBench Coding	70.9%	—
LMArena Coding	1368	—
BigCodeBench Complete	62.2%	—
ALE-Bench	—	301.65
HumanEval+	86.6%	—
MBPP+	73%	—

Agentic & Tool Use Not comparable

DeepSeek-V3: —, Mercury 2.5: —

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3	Mercury 2.5
METR Time Horizons	49.6%	—

Reasoning Mercury 2.5 leads

DeepSeek-V3: 20.5 (#236), Mercury 2.5: 22.4 (#193)

Reasoning benchmarks
Benchmark	DeepSeek-V3	Mercury 2.5
CritPt	0%	0%
SimpleBench	27.2%	—
Kagi LLM Benchmark	52.3%	—
LiveBench Reasoning	65.8%	—
LMArena Hard Prompts	1365	—
DTBench	64.8%	—
LiveBench Data Analysis	60.9%	—
LMCA	15.5%	—
BIG-Bench Hard	87.5%	—
Epoch Capabilities Index	135.94	—
ForecastBench	59.1	—
HellaSwag	88.9%	—
LiveBench	66.9%	—
PIQA	84.7%	—
WinoGrande	85.2%	—

Math DeepSeek-V3 leads

DeepSeek-V3: 32.1 (#219), Mercury 2.5: 23.3 (#272)

Math benchmarks
Benchmark	DeepSeek-V3	Mercury 2.5
OTIS Mock AIME 2024-2025	37.8%	—
ProofBench	—	3%
Omni-MATH	40.3%	—
LiveBench Math	73.5%	—
LMArena Math	1373	—
MATH Level 5	75.5%	—
FrontierMath (Feb 2025 set)	1.7%	—

Knowledge Not comparable

DeepSeek-V3: 37.5 (#155), Mercury 2.5: —

Knowledge benchmarks
Benchmark	DeepSeek-V3	Mercury 2.5
GPQA Diamond	67.6%	—
MMLU-Pro	72.3%	—
Confabulations	26.1%	—
Vectara Hallucination Rate	6.1%	—
GPQA (HELM)	53.8%	—
LMArena Expert	1351	—
ARC (AI2) Challenge	95.3%	—
MMLU	87.2%	—
TriviaQA	82.9%	—

Multilingual Not comparable

DeepSeek-V3: 48.5 (#143), Mercury 2.5: —

Multilingual benchmarks
Benchmark	DeepSeek-V3	Mercury 2.5
LMArena Non-English	1358	—
LMArena Chinese	1391	—
LMArena French	1385	—
LMArena German	1374	—
LMArena Japanese	1333	—
LMArena Korean	1319	—
LMArena Russian	1373	—
LMArena Spanish	1358	—

Instruction Following Not comparable

DeepSeek-V3: 72.8 (#130), Mercury 2.5: —

Instruction Following benchmarks
Benchmark	DeepSeek-V3	Mercury 2.5
LiveBench Instruction Following	81.5%	—
IFEval	83.2%	—
LMArena Instruction Following	1345	—

Long Context Not comparable

DeepSeek-V3: 34.0 (#253), Mercury 2.5: —

Long Context benchmarks
Benchmark	DeepSeek-V3	Mercury 2.5
Fiction.LiveBench	50%	—
LMArena Longer Query	1352	—

Writing & Preference Not comparable

DeepSeek-V3: 57.4 (#130), Mercury 2.5: —

Writing & Preference benchmarks
Benchmark	DeepSeek-V3	Mercury 2.5
LMArena Text	1375	—
LMArena Creative Writing	1364	—
Short-Story Creative Writing	77%	—
EQ-Bench Creative Writing	1472	—
WildBench	83%	—
LMArena Multi-Turn	1389	—
LiveBench Language	49.1%	—

Frequently asked questions

Is DeepSeek-V3 better than Mercury 2.5?

Which is cheaper, DeepSeek-V3 or Mercury 2.5?

Mercury 2.5 is cheaper. It lists at $0.04 per million input tokens and $0.15 per million output tokens; DeepSeek-V3 lists at $0.24 and $0.90.

Is DeepSeek-V3 or Mercury 2.5 better for coding?

DeepSeek-V3 scores higher on coding benchmarks: 42.3 versus 39.5 in the Noometry coding category.

Which has the bigger context window?

Mercury 2.5 does, with 260K tokens against 164K.

How many benchmarks do DeepSeek-V3 and Mercury 2.5 share?

2 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Mercury 2.5 has 4.