Claude Opus 4.7 vs DeepSeek V4 Flash 0731

Claude Opus 4.7 leads DeepSeek V4 Flash 0731 on coding, 67.5 to 51.8 on the Vibe Coding Index, and DeepSeek V4 Flash 0731 costs 63.1x less per output token ($0.396 vs $25.00 per million). Updated October 22, 2026; 39 benchmarks have a result for both.

Replace Claude Opus 4.7 Replace DeepSeek V4 Flash 0731

Claude Opus 4.7Anthropic

Coding: 67.5
Overall: 71.6
Output $/M: $25.00

DeepSeek V4 Flash 0731DeepSeek

Coding: 51.8
Overall: 55.2
Output $/M: $0.396

vs boards won of 39 shared, 1 tied

Claude Opus 4.7 or DeepSeek V4 Flash 0731: which to pick

Pick Claude Opus 4.7 for coding: it scores 67.5 on the Vibe Coding Index against 51.8 for DeepSeek V4 Flash 0731.

Pick DeepSeek V4 Flash 0731 for output tokens 63.1x cheaper ($0.396 vs $25.00 per million) and a lower cost per solved task ($0.01 against $2.95).

Model AClaude Opus 4.7Anthropic67.5/1004 independent benchmarks$2.95 per solved task Model BDeepSeek V4 Flash 0731DeepSeek51.8/1004 independent benchmarks$0.01 per solved task

396416

Shared results: 39
Claude Opus 4.7 only: 64
DeepSeek V4 Flash 0731 only: 16
Like-for-like categories: 7 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
Claude Opus 4.7
Claude Opus 4.7 scores 67.5 against 51.8.
4 benchmarks behind each score
Intelligence
AA Intelligence Index
Claude Opus 4.7
Claude Opus 4.7 leads, 40.7 against 34.3.
Same board, both measured
Agentic work
AA Agentic Index
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 leads, 41.0 against 38.6.
Same board, both measured
Reasoning
GPQA Diamond
Claude Opus 4.7
Claude Opus 4.7 leads, 90.2% against 89.9%.
Same board, both measured
Math
AIME
No call
Only Claude Opus 4.7 has a AIME result, 96.3%.
Directional only
Knowledge
MMLU Pro
Claude Opus 4.7
Claude Opus 4.7 leads, 89.9% against 86.2%.
Same board, both measured
Web design
Design Arena: websites
No call
Only DeepSeek V4 Flash 0731 has a Design Arena: websites result, 1237.
Directional only
Long documents
Listed context window
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Flash 0731
Claude Opus 4.7 $0.018, DeepSeek V4 Flash 0731 $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Flash 0731
Claude Opus 4.7 $2.95, DeepSeek V4 Flash 0731 $0.01.
Published attempt cost and pass rate

At a glance

Coding index: Claude Opus 4.7 67.5 from 4 benchmarks, DeepSeek V4 Flash 0731 51.8 from 4 benchmarks.
Widest category: Knowledge, 8.6 points apart on average over 6 shared benchmarks.
Widest single gap: FrontierCode: Claude Opus 4.7 38.5% against DeepSeek V4 Flash 0731 18.8%, 30.4 points apart on a 0 to 100 scale.
Output price: Claude Opus 4.7 $25.00 against DeepSeek V4 Flash 0731 $0.396 per million output tokens, 63.1x apart.
Context window: Claude Opus 4.7 takes 1M tokens, DeepSeek V4 Flash 0731 1.05M.

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	Claude Opus 4.7	DeepSeek V4 Flash 0731	Ties
Coding	5	3	1 tie
Intelligence	2	0
Agentic work	3	2
Reasoning	7	2
Math	3	1
Knowledge	6	0
General	3	1
All shared boards	29	9	1 tie

CodingLike-for-likeSix of these boards feed the Vibe Coding Index53

Coding benchmarks for Claude Opus 4.7 and DeepSeek V4 Flash 0731
Benchmark	Claude Opus 4.7	DeepSeek V4 Flash 0731	Leader
LMArena WebDev	1558	Not measured	Only Claude Opus 4.7 measured
AA Coding Index	73.6	69.1	Claude Opus 4.7
LiveCodeBench (Vals AI)	85.1%	87.3%	DeepSeek V4 Flash 0731
SWE-bench Verified (Vals AI)	82.0%	88.8%	DeepSeek V4 Flash 0731
Terminal-Bench 4 (Vals AI)	Not measured	18.7%	Only DeepSeek V4 Flash 0731 measured
Vibe Code Bench (Vals AI)	71.0%	74.7%	DeepSeek V4 Flash 0731
Code migration (Vals AI)	43.9%	38.6%	Claude Opus 4.7
ProgramBench (Vals AI)	0.0%	0.0%	Tie
IOI (Vals AI)	Not measured	32.7%	Only DeepSeek V4 Flash 0731 measured
Terminal-Bench 2.1 (Vals AI)	68.5%	67.0%	Claude Opus 4.7
SkillsBench (Vals AI)	Not measured	50.7%	Only DeepSeek V4 Flash 0731 measured
CyberBench (Vals AI)	Not measured	72.1%	Only DeepSeek V4 Flash 0731 measured
SciCode	Not measured	50.3%	Only DeepSeek V4 Flash 0731 measured
Terminal-Bench Hard (Artificial Analysis run)	54.5%	Not measured	Only Claude Opus 4.7 measured
Terminal-Bench	80.2%	Not measured	Only Claude Opus 4.7 measured
FrontierCode	38.5%	18.8%	Claude Opus 4.7
GSO-Bench	44.1%	Not measured	Only Claude Opus 4.7 measured
WeirdML	76.4%	63.0%	Claude Opus 4.7
MirrorCode	31.1%	Not measured	Only Claude Opus 4.7 measured
ALE-Bench	Not measured	1306.1	Only DeepSeek V4 Flash 0731 measured
LMArena Text: coding	1519	Not measured	Only Claude Opus 4.7 measured
LMArena Text: software and IT	1516	Not measured	Only Claude Opus 4.7 measured
LMArena WebDev: React	1560	Not measured	Only Claude Opus 4.7 measured
LMArena WebDev: HTML	1554	Not measured	Only Claude Opus 4.7 measured
LMArena WebDev: from an image	1577	Not measured	Only Claude Opus 4.7 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 Claude Opus 4.7 and DeepSeek V4 Flash 0731
Benchmark	Claude Opus 4.7	DeepSeek V4 Flash 0731	Leader
AA Intelligence Index	40.7	34.3	Claude Opus 4.7
Epoch Capabilities Index	156.8	154.8	Claude Opus 4.7

Agentic workLike-for-likePublic boards, shown as published, kept out of the index32

Agentic work benchmarks for Claude Opus 4.7 and DeepSeek V4 Flash 0731
Benchmark	Claude Opus 4.7	DeepSeek V4 Flash 0731	Leader
AA Agentic Index	38.6	41.0	DeepSeek V4 Flash 0731
Finance Agent (Vals AI)	51.5%	49.5%	Claude Opus 4.7
Tax Agent Bench (Vals AI)	23.7%	28.1%	DeepSeek V4 Flash 0731
Legal Research Bench (Vals AI)	38.5%	30.3%	Claude Opus 4.7
Excel Modeling Benchmark (Vals AI)	63.7%	57.0%	Claude Opus 4.7
BioMysteryBench (Vals AI)	Not measured	64.4%	Only DeepSeek V4 Flash 0731 measured
Terminal-Bench-Science (Vals AI)	Not measured	4.3%	Only DeepSeek V4 Flash 0731 measured
τ²-Bench Telecom	88.6%	Not measured	Only Claude Opus 4.7 measured
APEX-Agents	49.2%	Not measured	Only Claude Opus 4.7 measured
OSWorld 2.0	18.2%	Not measured	Only Claude Opus 4.7 measured
PostTrainBench	28.6%	Not measured	Only Claude Opus 4.7 measured
Vending-Bench 2	$10,937	Not measured	Only Claude Opus 4.7 measured
LMArena Search	1230	Not measured	Only Claude Opus 4.7 measured
Neither model has a result on the other 28 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index72

Reasoning benchmarks for Claude Opus 4.7 and DeepSeek V4 Flash 0731
Benchmark	Claude Opus 4.7	DeepSeek V4 Flash 0731	Leader
GPQA Diamond	90.2%	89.9%	Claude Opus 4.7
MMMU Pro (Vals AI)	85.5%	Not measured	Only Claude Opus 4.7 measured
GPQA Diamond (Artificial Analysis run)	91.4%	90.8%	Claude Opus 4.7
Humanity's Last Exam (Artificial Analysis run)	42.3%	38.6%	Claude Opus 4.7
GPQA Diamond (Epoch AI run)	90.2%	91.0%	DeepSeek V4 Flash 0731
ARC-AGI	93.5%	89.0%	Claude Opus 4.7
ARC-AGI-2	75.8%	61.4%	Claude Opus 4.7
SimpleBench	61.7%	61.1%	Claude Opus 4.7
Chess puzzles	41.0%	32.0%	Claude Opus 4.7
Mystery game puzzles	28.0%	34.0%	DeepSeek V4 Flash 0731
EBR-bench	19.0%	Not measured	Only Claude Opus 4.7 measured
LMArena Text: hard prompts	1506	Not measured	Only Claude Opus 4.7 measured
LMArena Text: expert	1521	Not measured	Only Claude Opus 4.7 measured
LMArena Text: hard prompts in English	1508	Not measured	Only Claude Opus 4.7 measured
LMArena Vision: diagrams	1336	Not measured	Only Claude Opus 4.7 measured
LMArena Vision: homework	1337	Not measured	Only Claude Opus 4.7 measured
Kagi LLM Benchmark	80.7%	Not measured	Only Claude Opus 4.7 measured
Neither model has a result on the other 5 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index31

Math benchmarks for Claude Opus 4.7 and DeepSeek V4 Flash 0731
Benchmark	Claude Opus 4.7	DeepSeek V4 Flash 0731	Leader
AIME	96.3%	Not measured	Only Claude Opus 4.7 measured
OTIS Mock AIME	97.8%	94.4%	Claude Opus 4.7
FrontierMath, tiers 1 to 3	70.2%	57.5%	Claude Opus 4.7
FrontierMath, tier 4	31.7%	24.4%	Claude Opus 4.7
ProofBench	54.0%	56.0%	DeepSeek V4 Flash 0731
LMArena Text: math	1498	Not measured	Only Claude Opus 4.7 measured
LMArena Text: mathematical work	1509	Not measured	Only Claude Opus 4.7 measured
Neither model has a result on the other 6 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index60

Knowledge benchmarks for Claude Opus 4.7 and DeepSeek V4 Flash 0731
Benchmark	Claude Opus 4.7	DeepSeek V4 Flash 0731	Leader
MMLU Pro	89.9%	86.2%	Claude Opus 4.7
LegalBench (Vals AI)	85.3%	77.7%	Claude Opus 4.7
CaseLaw (Vals AI)	68.4%	Not measured	Only Claude Opus 4.7 measured
CorpFin (Vals AI)	66.1%	61.9%	Claude Opus 4.7
TaxEval (Vals AI)	75.3%	70.7%	Claude Opus 4.7
MortgageTax (Vals AI)	70.3%	Not measured	Only Claude Opus 4.7 measured
MedCode (Vals AI)	54.9%	41.4%	Claude Opus 4.7
SimpleQA Verified	51.7%	33.6%	Claude Opus 4.7
LMArena Text: science	1508	Not measured	Only Claude Opus 4.7 measured
LMArena Text: medicine and healthcare	1495	Not measured	Only Claude Opus 4.7 measured
LMArena Text: legal and government	1504	Not measured	Only Claude Opus 4.7 measured
LMArena Text: business and finance	1490	Not measured	Only Claude Opus 4.7 measured
FACTS Benchmark Suite	45.5%	Not measured	Only Claude Opus 4.7 measured
Neither model has a result on the other 5 boards here

Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared

Web design benchmarks for Claude Opus 4.7 and DeepSeek V4 Flash 0731
Benchmark	Claude Opus 4.7	DeepSeek V4 Flash 0731	Leader
Design Arena: websites	Not measured	1237	Only DeepSeek V4 Flash 0731 measured
Design Arena: UI components	Not measured	1233	Only DeepSeek V4 Flash 0731 measured
Design Arena: game dev	Not measured	1207	Only DeepSeek V4 Flash 0731 measured
Design Arena: data viz	Not measured	1180	Only DeepSeek V4 Flash 0731 measured
Design Arena: 3D	Not measured	1208	Only DeepSeek V4 Flash 0731 measured
Design Arena: all code categories	Not measured	1229	Only DeepSeek V4 Flash 0731 measured
Design Arena: SVG	Not measured	1184	Only DeepSeek V4 Flash 0731 measured
Design Arena: ASCII art	Not measured	1090	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 index31

General benchmarks for Claude Opus 4.7 and DeepSeek V4 Flash 0731
Benchmark	Claude Opus 4.7	DeepSeek V4 Flash 0731	Leader
SAGE (Vals AI)	56.1%	Not measured	Only Claude Opus 4.7 measured
MedScribe (Vals AI)	83.0%	80.4%	Claude Opus 4.7
Vals Multimodal Index	67.4%	Not measured	Only Claude Opus 4.7 measured
IFBench	58.6%	Not measured	Only Claude Opus 4.7 measured
Long context reasoning (AA-LCR)	78.7%	79.7%	DeepSeek V4 Flash 0731
DTBench	94.7%	90.9%	Claude Opus 4.7
LMCA	52.2%	41.7%	Claude Opus 4.7
Furniture assembly	33.3%	Not measured	Only Claude Opus 4.7 measured
ExploitBench	26.5%	Not measured	Only Claude Opus 4.7 measured
LMArena Text	1490	Not measured	Only Claude Opus 4.7 measured
LMArena Text: instruction following	1498	Not measured	Only Claude Opus 4.7 measured
LMArena Text: creative writing	1486	Not measured	Only Claude Opus 4.7 measured
LMArena Vision	1317	Not measured	Only Claude Opus 4.7 measured
LMArena Text: English	1496	Not measured	Only Claude Opus 4.7 measured
LMArena Text: non-English	1480	Not measured	Only Claude Opus 4.7 measured
LMArena Text: Chinese	1531	Not measured	Only Claude Opus 4.7 measured
LMArena Text: German	1495	Not measured	Only Claude Opus 4.7 measured
LMArena Text: Japanese	1474	Not measured	Only Claude Opus 4.7 measured
LMArena Text: Korean	1464	Not measured	Only Claude Opus 4.7 measured
LMArena Text: French	1503	Not measured	Only Claude Opus 4.7 measured
LMArena Text: Spanish	1494	Not measured	Only Claude Opus 4.7 measured
LMArena Text: Russian	1494	Not measured	Only Claude Opus 4.7 measured
LMArena Text: Polish	1489	Not measured	Only Claude Opus 4.7 measured
LMArena Text: multi-turn	1505	Not measured	Only Claude Opus 4.7 measured
LMArena Text: longer queries	1505	Not measured	Only Claude Opus 4.7 measured
LMArena Text: writing and language	1492	Not measured	Only Claude Opus 4.7 measured
LMArena Text: entertainment and media	1474	Not measured	Only Claude Opus 4.7 measured
LMArena Vision: English	1322	Not measured	Only Claude Opus 4.7 measured
LMArena Vision: Chinese	1371	Not measured	Only Claude Opus 4.7 measured
LMArena Vision: reading text	1327	Not measured	Only Claude Opus 4.7 measured
LMArena Vision: humor	1309	Not measured	Only Claude Opus 4.7 measured
LMArena Vision: recognizing things	1278	Not measured	Only Claude Opus 4.7 measured
LMArena Vision: creative writing	1326	Not measured	Only Claude Opus 4.7 measured
LMArena Document	1495	Not measured	Only Claude Opus 4.7 measured
Neither model has a result on the other 12 boards here

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

Image generation benchmarks for Claude Opus 4.7 and DeepSeek V4 Flash 0731
Benchmark	Claude Opus 4.7	DeepSeek V4 Flash 0731	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

Claude Opus 4.7$0.018Fits in one request

DeepSeek V4 Flash 0731$0.0003Fits in one request

DeepSeek V4 Flash 0731 costs less

Repository review

50K input + 3K output tokens

Claude Opus 4.7$0.325Fits in one request

DeepSeek V4 Flash 0731$0.0082Fits in one request

DeepSeek V4 Flash 0731 costs less

Agent loop

200K input + 10K output tokens

Claude Opus 4.7$1.25Fits in one request

DeepSeek V4 Flash 0731$0.032Fits 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 Claude Opus 4.7 and DeepSeek V4 Flash 0731
Specification	Anthropic Claude Opus 4.7	DeepSeek V4 Flash 0731
Maker	Anthropic	DeepSeek
Weights	No public weights listed	Open weights
Released	April 16, 2026	July 31, 2026
Context window	1M	1.05M
Input $/Mtok	$5.00	$0.14
Output $/Mtok	$25.00	$0.396
Coding index	67.5	51.8
Independent measurements behind it	4	4
Cost per solved task	$2.95	$0.01
Source	OpenRouter listing	OpenRouter listing

Questions

Is Claude Opus 4.7 better than DeepSeek V4 Flash 0731 for coding?

Pick Claude Opus 4.7 for coding: it scores 67.5 on the Vibe Coding Index against 51.8 for DeepSeek V4 Flash 0731. Pick DeepSeek V4 Flash 0731 for output tokens 63.1x cheaper ($0.396 vs $25.00 per million) and a lower cost per solved task ($0.01 against $2.95).

Which is cheaper, Claude Opus 4.7 or DeepSeek V4 Flash 0731?

Claude Opus 4.7 costs $5.00 per million input tokens and $25.00 per million output tokens; DeepSeek V4 Flash 0731 costs $0.14 and $0.396. Per solved SWE-bench Verified task, Claude Opus 4.7 costs $2.95 and DeepSeek V4 Flash 0731 $0.01.

Which has the bigger context window, Claude Opus 4.7 or DeepSeek V4 Flash 0731?

DeepSeek V4 Flash 0731, with 1.05M tokens against 1M.

How many benchmarks is the Claude Opus 4.7 vs DeepSeek V4 Flash 0731 comparison based on?

39 benchmarks have a result for both models as of October 22, 2026. Claude Opus 4.7's coding index rests on 4 independent benchmarks and DeepSeek V4 Flash 0731's on 4. Where only one model has a result, the page says so instead of naming a winner.

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Claude Opus 4.7 or DeepSeek V4 Flash 0731: which to pick

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

Math

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