DeepSeek V4 Flash 0731 vs Gemini 2.5 Pro

DeepSeek V4 Flash 0731 leads Gemini 2.5 Pro on coding, 51.8 to 19.1 on the Vibe Coding Index, and DeepSeek V4 Flash 0731 costs 35.7x less per output token ($0.28 vs $10.00 per million). Updated October 21, 2026; 28 benchmarks have a result for both.

Replace DeepSeek V4 Flash 0731 Replace Gemini 2.5 Pro

DeepSeek V4 Flash 0731DeepSeek

Coding: 51.8
Overall: 55.2
Output $/M: $0.28

Gemini 2.5 ProGoogle

Coding: 19.1
Overall: 41.8
Output $/M: $10.00

vs boards won of 28 shared

DeepSeek V4 Flash 0731 or Gemini 2.5 Pro: which to pick

Pick DeepSeek V4 Flash 0731 for coding: it scores 51.8 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro.

Pick Gemini 2.5 Pro for work measured by MedCode (Vals AI) or Design Arena: data viz, where it leads.

Model ADeepSeek V4 Flash 0731DeepSeek51.8/1004 independent benchmarks$0.01 per solved task Model BGemini 2.5 ProGoogle19.1/1003 independent benchmarks$0.64 per solved task

282762

Shared results: 28
DeepSeek V4 Flash 0731 only: 27
Gemini 2.5 Pro only: 62
Like-for-like categories: 8 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

Coding
Vibe Coding Index, six public coding benchmarks
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 scores 51.8 against 19.1.
4 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 leads, 34.3 against 16.1.
Same board, both measured
Agentic work
AA Agentic Index
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 leads, 41.0 against 1.6.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only DeepSeek V4 Flash 0731 has a GPQA Diamond result, 89.9%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V4 Flash 0731 has a MMLU Pro result, 86.2%.
Directional only
Web design
Design Arena: websites
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 leads, 1238 against 1167.
Same board, both measured
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 $0.0003, Gemini 2.5 Pro $0.0063 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 $0.01, Gemini 2.5 Pro $0.64.
Published attempt cost and pass rate

At a glance

Coding index: DeepSeek V4 Flash 0731 51.8 from 4 benchmarks, Gemini 2.5 Pro 19.1 from 3 benchmarks.
Widest category: Agentic work, 39.4 points apart on average over 1 shared benchmark.
Widest single gap: Vibe Code Bench (Vals AI): DeepSeek V4 Flash 0731 74.7% against Gemini 2.5 Pro 0.4%, 74.3 points apart on a 0 to 100 scale.
Output price: DeepSeek V4 Flash 0731 $0.28 against Gemini 2.5 Pro $10.00 per million output tokens, 35.7x apart.
Context window: Both take 1.05M tokens.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
Category	DeepSeek V4 Flash 0731	Gemini 2.5 Pro
Coding	5	0
Intelligence	2	0
Agentic work	1	0
Reasoning	5	0
Math	3	0
Knowledge	1	1
Web design	5	1
General	4	0
All shared boards	26	2

CodingLike-for-likeSix of these boards feed the Vibe Coding Index50

Coding benchmarks for DeepSeek V4 Flash 0731 and Gemini 2.5 Pro
Benchmark	DeepSeek V4 Flash 0731	Gemini 2.5 Pro	Leader
SWE-bench Verified	Not measured	53.6%	Only Gemini 2.5 Pro measured
LMArena WebDev	Not measured	1226	Only Gemini 2.5 Pro measured
AA Coding Index	69.1	33.3	DeepSeek V4 Flash 0731
LiveCodeBench (Vals AI)	87.3%	Not measured	Only DeepSeek V4 Flash 0731 measured
SWE-bench Verified (Vals AI)	88.8%	54.4%	DeepSeek V4 Flash 0731
Terminal-Bench 4 (Vals AI)	18.7%	Not measured	Only DeepSeek V4 Flash 0731 measured
Vibe Code Bench (Vals AI)	74.7%	0.4%	DeepSeek V4 Flash 0731
Code migration (Vals AI)	38.6%	Not measured	Only DeepSeek V4 Flash 0731 measured
ProgramBench (Vals AI)	0.0%	Not measured	Only DeepSeek V4 Flash 0731 measured
IOI (Vals AI)	32.7%	Not measured	Only DeepSeek V4 Flash 0731 measured
Terminal-Bench 2.1 (Vals AI)	67.0%	Not measured	Only DeepSeek V4 Flash 0731 measured
SkillsBench (Vals AI)	50.7%	Not measured	Only DeepSeek V4 Flash 0731 measured
CyberBench (Vals AI)	72.1%	Not measured	Only DeepSeek V4 Flash 0731 measured
LiveCodeBench (Artificial Analysis run)	Not measured	80.1%	Only Gemini 2.5 Pro measured
SciCode	50.3%	46.3%	DeepSeek V4 Flash 0731
Terminal-Bench Hard (Artificial Analysis run)	Not measured	26.5%	Only Gemini 2.5 Pro measured
Terminal-Bench	Not measured	32.6%	Only Gemini 2.5 Pro measured
FrontierCode	18.8%	Not measured	Only DeepSeek V4 Flash 0731 measured
GSO-Bench	Not measured	3.9%	Only Gemini 2.5 Pro measured
WeirdML	63.0%	54.0%	DeepSeek V4 Flash 0731
ALE-Bench	1306.1	Not measured	Only DeepSeek V4 Flash 0731 measured
LMArena Text: coding	Not measured	1452	Only Gemini 2.5 Pro measured
LMArena Text: software and IT	Not measured	1459	Only Gemini 2.5 Pro measured
LMArena WebDev: HTML	Not measured	1235	Only Gemini 2.5 Pro measured
LMArena WebDev: from an image	Not measured	1285	Only Gemini 2.5 Pro measured
Neither model has a result on the other 14 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20

Intelligence benchmarks for DeepSeek V4 Flash 0731 and Gemini 2.5 Pro
Benchmark	DeepSeek V4 Flash 0731	Gemini 2.5 Pro	Leader
AA Intelligence Index	34.3	16.1	DeepSeek V4 Flash 0731
Epoch Capabilities Index	154.8	145.1	DeepSeek V4 Flash 0731

Agentic workLike-for-likePublic boards, shown as published, kept out of the index10

Agentic work benchmarks for DeepSeek V4 Flash 0731 and Gemini 2.5 Pro
Benchmark	DeepSeek V4 Flash 0731	Gemini 2.5 Pro	Leader
AA Agentic Index	41.0	1.6	DeepSeek V4 Flash 0731
Finance Agent (Vals AI)	49.5%	Not measured	Only DeepSeek V4 Flash 0731 measured
Tax Agent Bench (Vals AI)	28.1%	Not measured	Only DeepSeek V4 Flash 0731 measured
Legal Research Bench (Vals AI)	30.3%	Not measured	Only DeepSeek V4 Flash 0731 measured
Excel Modeling Benchmark (Vals AI)	57.0%	Not measured	Only DeepSeek V4 Flash 0731 measured
BioMysteryBench (Vals AI)	64.4%	Not measured	Only DeepSeek V4 Flash 0731 measured
Terminal-Bench-Science (Vals AI)	4.3%	Not measured	Only DeepSeek V4 Flash 0731 measured
τ²-Bench Telecom	Not measured	54.1%	Only Gemini 2.5 Pro measured
GDPval	Not measured	23.3%	Only Gemini 2.5 Pro measured
DeepResearch Bench	Not measured	41.5%	Only Gemini 2.5 Pro measured
Vending-Bench 2	Not measured	$574	Only Gemini 2.5 Pro measured
BrowseComp-Plus	Not measured	29.5%	Only Gemini 2.5 Pro measured
Neither model has a result on the other 29 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index50

Reasoning benchmarks for DeepSeek V4 Flash 0731 and Gemini 2.5 Pro
Benchmark	DeepSeek V4 Flash 0731	Gemini 2.5 Pro	Leader
GPQA Diamond	89.9%	Not measured	Only DeepSeek V4 Flash 0731 measured
GPQA Diamond (Artificial Analysis run)	90.8%	84.4%	DeepSeek V4 Flash 0731
Humanity's Last Exam (Artificial Analysis run)	38.6%	22.5%	DeepSeek V4 Flash 0731
GPQA Diamond (Epoch AI run)	91.0%	85.3%	DeepSeek V4 Flash 0731
ARC-AGI	89.0%	41.0%	DeepSeek V4 Flash 0731
ARC-AGI-2	61.4%	4.9%	DeepSeek V4 Flash 0731
SimpleBench	61.1%	Not measured	Only DeepSeek V4 Flash 0731 measured
Chess puzzles	32.0%	Not measured	Only DeepSeek V4 Flash 0731 measured
Mystery game puzzles	34.0%	Not measured	Only DeepSeek V4 Flash 0731 measured
LMArena Text: hard prompts	Not measured	1455	Only Gemini 2.5 Pro measured
LMArena Text: expert	Not measured	1452	Only Gemini 2.5 Pro measured
LMArena Text: hard prompts in English	Not measured	1453	Only Gemini 2.5 Pro measured
LMArena Vision: diagrams	Not measured	1274	Only Gemini 2.5 Pro measured
LMArena Vision: homework	Not measured	1281	Only Gemini 2.5 Pro measured
Kagi LLM Benchmark	Not measured	70.3%	Only Gemini 2.5 Pro measured
Neither model has a result on the other 7 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index30

Math benchmarks for DeepSeek V4 Flash 0731 and Gemini 2.5 Pro
Benchmark	DeepSeek V4 Flash 0731	Gemini 2.5 Pro	Leader
AA Math Index	Not measured	87.7	Only Gemini 2.5 Pro measured
AIME 2025 (Artificial Analysis run)	Not measured	87.7%	Only Gemini 2.5 Pro measured
OTIS Mock AIME	94.4%	84.7%	DeepSeek V4 Flash 0731
FrontierMath, tiers 1 to 3	57.5%	24.6%	DeepSeek V4 Flash 0731
FrontierMath, tier 4	24.4%	0.0%	DeepSeek V4 Flash 0731
ProofBench	56.0%	Not measured	Only DeepSeek V4 Flash 0731 measured
LMArena Text: math	Not measured	1450	Only Gemini 2.5 Pro measured
LMArena Text: mathematical work	Not measured	1454	Only Gemini 2.5 Pro measured
Neither model has a result on the other 5 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index11

Knowledge benchmarks for DeepSeek V4 Flash 0731 and Gemini 2.5 Pro
Benchmark	DeepSeek V4 Flash 0731	Gemini 2.5 Pro	Leader
MMLU Pro	86.2%	Not measured	Only DeepSeek V4 Flash 0731 measured
LegalBench (Vals AI)	77.7%	Not measured	Only DeepSeek V4 Flash 0731 measured
CaseLaw (Vals AI)	Not measured	63.9%	Only Gemini 2.5 Pro measured
CorpFin (Vals AI)	61.9%	60.8%	DeepSeek V4 Flash 0731
TaxEval (Vals AI)	70.7%	Not measured	Only DeepSeek V4 Flash 0731 measured
MortgageTax (Vals AI)	Not measured	68.9%	Only Gemini 2.5 Pro measured
MedCode (Vals AI)	41.4%	50.6%	Gemini 2.5 Pro
MMLU Pro (Artificial Analysis run)	Not measured	86.2%	Only Gemini 2.5 Pro measured
SimpleQA Verified	33.6%	Not measured	Only DeepSeek V4 Flash 0731 measured
LMArena Text: science	Not measured	1480	Only Gemini 2.5 Pro measured
LMArena Text: medicine and healthcare	Not measured	1476	Only Gemini 2.5 Pro measured
LMArena Text: legal and government	Not measured	1480	Only Gemini 2.5 Pro measured
LMArena Text: business and finance	Not measured	1439	Only Gemini 2.5 Pro measured
FACTS Benchmark Suite	Not measured	61.5%	Only Gemini 2.5 Pro measured
Neither model has a result on the other 4 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index51

Web design benchmarks for DeepSeek V4 Flash 0731 and Gemini 2.5 Pro
Benchmark	DeepSeek V4 Flash 0731	Gemini 2.5 Pro	Leader
Design Arena: websites	1238	1167	DeepSeek V4 Flash 0731
Design Arena: UI components	1235	1140	DeepSeek V4 Flash 0731
Design Arena: game dev	1208	1111	DeepSeek V4 Flash 0731
Design Arena: data viz	1180	1221	Gemini 2.5 Pro
Design Arena: 3D	1209	1081	DeepSeek V4 Flash 0731
Design Arena: all code categories	1230	1152	DeepSeek V4 Flash 0731
Design Arena: SVG	1185	Not measured	Only DeepSeek V4 Flash 0731 measured
Design Arena: ASCII art	1090	Not measured	Only DeepSeek V4 Flash 0731 measured
Neither model has a result on the other 1 board here

GeneralLike-for-likePublic boards, shown as published, kept out of the index40

General benchmarks for DeepSeek V4 Flash 0731 and Gemini 2.5 Pro
Benchmark	DeepSeek V4 Flash 0731	Gemini 2.5 Pro	Leader
SAGE (Vals AI)	Not measured	41.9%	Only Gemini 2.5 Pro measured
MedScribe (Vals AI)	80.4%	73.5%	DeepSeek V4 Flash 0731
IFBench	Not measured	48.7%	Only Gemini 2.5 Pro measured
Long context reasoning (AA-LCR)	79.7%	69.0%	DeepSeek V4 Flash 0731
DTBench	90.9%	82.4%	DeepSeek V4 Flash 0731
LMCA	41.7%	34.8%	DeepSeek V4 Flash 0731
Creative writing (Lech Mazur)	Not measured	83.8%	Only Gemini 2.5 Pro measured
LMArena Text	Not measured	1458	Only Gemini 2.5 Pro measured
LMArena Text: instruction following	Not measured	1437	Only Gemini 2.5 Pro measured
LMArena Text: creative writing	Not measured	1454	Only Gemini 2.5 Pro measured
LMArena Vision	Not measured	1263	Only Gemini 2.5 Pro measured
LMArena Text: English	Not measured	1458	Only Gemini 2.5 Pro measured
LMArena Text: non-English	Not measured	1452	Only Gemini 2.5 Pro measured
LMArena Text: Chinese	Not measured	1507	Only Gemini 2.5 Pro measured
LMArena Text: German	Not measured	1487	Only Gemini 2.5 Pro measured
LMArena Text: Japanese	Not measured	1461	Only Gemini 2.5 Pro measured
LMArena Text: Korean	Not measured	1434	Only Gemini 2.5 Pro measured
LMArena Text: French	Not measured	1473	Only Gemini 2.5 Pro measured
LMArena Text: Spanish	Not measured	1474	Only Gemini 2.5 Pro measured
LMArena Text: Russian	Not measured	1461	Only Gemini 2.5 Pro measured
LMArena Text: Polish	Not measured	1479	Only Gemini 2.5 Pro measured
LMArena Text: multi-turn	Not measured	1453	Only Gemini 2.5 Pro measured
LMArena Text: longer queries	Not measured	1449	Only Gemini 2.5 Pro measured
LMArena Text: writing and language	Not measured	1454	Only Gemini 2.5 Pro measured
LMArena Text: entertainment and media	Not measured	1441	Only Gemini 2.5 Pro measured
LMArena Vision: English	Not measured	1261	Only Gemini 2.5 Pro measured
LMArena Vision: Chinese	Not measured	1300	Only Gemini 2.5 Pro measured
LMArena Vision: reading text	Not measured	1270	Only Gemini 2.5 Pro measured
LMArena Vision: humor	Not measured	1262	Only Gemini 2.5 Pro measured
LMArena Vision: captioning	Not measured	1304	Only Gemini 2.5 Pro measured
LMArena Vision: recognizing things	Not measured	1283	Only Gemini 2.5 Pro measured
LMArena Vision: creative writing	Not measured	1265	Only Gemini 2.5 Pro measured
LMArena Document	Not measured	1421	Only Gemini 2.5 Pro measured
Neither model has a result on the other 13 boards here

Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared

Image generation benchmarks for DeepSeek V4 Flash 0731 and Gemini 2.5 Pro
Benchmark	DeepSeek V4 Flash 0731	Gemini 2.5 Pro	Leader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

DeepSeek V4 Flash 0731$0.0003Fits in one request

Gemini 2.5 Pro$0.0063Fits in one request

DeepSeek V4 Flash 0731 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Flash 0731$0.0078Fits in one request

Gemini 2.5 Pro$0.092Fits in one request

DeepSeek V4 Flash 0731 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Flash 0731$0.031Fits in one request

Gemini 2.5 Pro$0.350Fits in one request

DeepSeek V4 Flash 0731 costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of DeepSeek V4 Flash 0731 and Gemini 2.5 Pro
Specification	DeepSeek V4 Flash 0731	Google Gemini 2.5 Pro
Maker	DeepSeek	Google
Weights	Open weights	No public weights listed
Released	July 31, 2026	June 17, 2025
Context window	1.05M	1.05M
Input $/Mtok	$0.14	$1.25
Output $/Mtok	$0.28	$10.00
Coding index	51.8	19.1
Independent measurements behind it	4	3
Cost per solved task	$0.01	$0.64
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4 Flash 0731 better than Gemini 2.5 Pro for coding?

Pick DeepSeek V4 Flash 0731 for coding: it scores 51.8 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro. Pick Gemini 2.5 Pro for work measured by MedCode (Vals AI) or Design Arena: data viz, where it leads.

Which is cheaper, DeepSeek V4 Flash 0731 or Gemini 2.5 Pro?

DeepSeek V4 Flash 0731 costs $0.14 per million input tokens and $0.28 per million output tokens; Gemini 2.5 Pro costs $1.25 and $10.00. Per solved SWE-bench Verified task, DeepSeek V4 Flash 0731 costs $0.01 and Gemini 2.5 Pro $0.64.

Which has the bigger context window, DeepSeek V4 Flash 0731 or Gemini 2.5 Pro?

Neither: both take 1.05M tokens.

How many benchmarks is the DeepSeek V4 Flash 0731 vs Gemini 2.5 Pro comparison based on?

28 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Flash 0731's coding index rests on 4 independent benchmarks and Gemini 2.5 Pro's on 3. Where only one model has a result, the page says so instead of naming a winner.

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DeepSeek V4 Flash 0731 or Gemini 2.5 Pro: which to pick

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

Knowledge

Web design

Long documents

Everyday cost

Cost per solved task

At a glance

Benchmark by benchmark

What each workload costs

Chat turn

Repository review

Agent loop

Specification differences

Questions

Compare next