DeepSeek V3.1 Terminus vs Gemini 2.5 Pro

Gemini 2.5 Pro leads DeepSeek V3.1 Terminus on the Artificial Analysis Intelligence Index, 16.1 to 14.8, and DeepSeek V3.1 Terminus costs 9.9x less per output token ($1.0142 vs $10.00 per million). Updated October 22, 2026; 43 benchmarks have a result for both.

Replace DeepSeek V3.1 Terminus Replace Gemini 2.5 Pro

DeepSeek V3.1 TerminusDeepSeek

Coding: No data
Overall: No data
Output $/M: $1.0142

Gemini 2.5 ProGoogle

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

vs boards won of 43 shared

DeepSeek V3.1 Terminus or Gemini 2.5 Pro: which to pick

Only Gemini 2.5 Pro has a coding score, 19.1 on the Vibe Coding Index; DeepSeek V3.1 Terminus has none, so the two cannot be ranked on coding.

Pick DeepSeek V3.1 Terminus for output tokens 9.9x cheaper ($1.0142 vs $10.00 per million) and work measured by AA Coding Index or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model ADeepSeek V3.1 TerminusDeepSeekNot benchmarkedNo coding benchmarkNo cost per solved task Model BGemini 2.5 ProGoogle19.1/1003 independent benchmarks$0.64 per solved task

43247

Shared results: 43
DeepSeek V3.1 Terminus only: 2
Gemini 2.5 Pro only: 47
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
No call
Only Gemini 2.5 Pro has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Gemini 2.5 Pro
Gemini 2.5 Pro leads, 16.1 against 14.8.
Same board, both measured
Agentic work
AA Agentic Index
DeepSeek V3.1 Terminus
DeepSeek V3.1 Terminus leads, 6.8 against 1.6.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V3.1 Terminus
DeepSeek V3.1 Terminus leads, 1187 against 1167.
Same board, both measured
Long documents
Listed context window
Gemini 2.5 Pro
Gemini 2.5 Pro takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.1 Terminus
DeepSeek V3.1 Terminus $0.0008, Gemini 2.5 Pro $0.0063 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Gemini 2.5 Pro has the number, $0.64.
Directional only

At a glance

Coding index: DeepSeek V3.1 Terminus not benchmarked, Gemini 2.5 Pro 19.1 from 3 benchmarks.
Widest category: Agentic work, 11.1 points apart on average over 2 shared benchmarks.
Widest single gap: τ²-Bench Telecom: DeepSeek V3.1 Terminus 37.1% against Gemini 2.5 Pro 54.1%, 17.0 points apart on a 0 to 100 scale.
Output price: DeepSeek V3.1 Terminus $1.0142 against Gemini 2.5 Pro $10.00 per million output tokens, 9.9x apart.
Context window: DeepSeek V3.1 Terminus takes 164K tokens, Gemini 2.5 Pro 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	DeepSeek V3.1 Terminus	Gemini 2.5 Pro
Coding	2	4
Intelligence	0	1
Agentic work	1	1
Reasoning	0	5
Math	2	1
Knowledge	0	5
Web design	5	1
General	2	13
All shared boards	12	31

CodingLike-for-likeSix of these boards feed the Vibe Coding Index24

Coding benchmarks for DeepSeek V3.1 Terminus and Gemini 2.5 Pro
Benchmark	DeepSeek V3.1 Terminus	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	43.5	33.3	DeepSeek V3.1 Terminus
SWE-bench Verified (Vals AI)	Not measured	54.4%	Only Gemini 2.5 Pro measured
Vibe Code Bench (Vals AI)	Not measured	0.4%	Only Gemini 2.5 Pro measured
LiveCodeBench (Artificial Analysis run)	79.8%	80.1%	Gemini 2.5 Pro
SciCode	38.0%	46.3%	Gemini 2.5 Pro
Terminal-Bench Hard (Artificial Analysis run)	31.8%	26.5%	DeepSeek V3.1 Terminus
Terminal-Bench	Not measured	32.6%	Only Gemini 2.5 Pro measured
GSO-Bench	Not measured	3.9%	Only Gemini 2.5 Pro measured
WeirdML	Not measured	54.0%	Only Gemini 2.5 Pro measured
ALE-Bench	745.2	Not measured	Only DeepSeek V3.1 Terminus measured
LMArena Text: coding	1425	1452	Gemini 2.5 Pro
LMArena Text: software and IT	1426	1459	Gemini 2.5 Pro
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 23 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01

Intelligence benchmarks for DeepSeek V3.1 Terminus and Gemini 2.5 Pro
Benchmark	DeepSeek V3.1 Terminus	Gemini 2.5 Pro	Leader
AA Intelligence Index	14.8	16.1	Gemini 2.5 Pro
Epoch Capabilities Index	Not measured	145.1	Only Gemini 2.5 Pro measured

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

Agentic work benchmarks for DeepSeek V3.1 Terminus and Gemini 2.5 Pro
Benchmark	DeepSeek V3.1 Terminus	Gemini 2.5 Pro	Leader
AA Agentic Index	6.8	1.6	DeepSeek V3.1 Terminus
τ²-Bench Telecom	37.1%	54.1%	Gemini 2.5 Pro
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 35 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index05

Reasoning benchmarks for DeepSeek V3.1 Terminus and Gemini 2.5 Pro
Benchmark	DeepSeek V3.1 Terminus	Gemini 2.5 Pro	Leader
GPQA Diamond (Artificial Analysis run)	79.2%	84.4%	Gemini 2.5 Pro
Humanity's Last Exam (Artificial Analysis run)	16.4%	22.5%	Gemini 2.5 Pro
GPQA Diamond (Epoch AI run)	Not measured	85.3%	Only Gemini 2.5 Pro measured
ARC-AGI	Not measured	41.0%	Only Gemini 2.5 Pro measured
ARC-AGI-2	Not measured	4.9%	Only Gemini 2.5 Pro measured
LMArena Text: hard prompts	1426	1455	Gemini 2.5 Pro
LMArena Text: expert	Not measured	1452	Only Gemini 2.5 Pro measured
LMArena Text: hard prompts in English	1432	1453	Gemini 2.5 Pro
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	57.4%	70.3%	Gemini 2.5 Pro
Neither model has a result on the other 11 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index21

Math benchmarks for DeepSeek V3.1 Terminus and Gemini 2.5 Pro
Benchmark	DeepSeek V3.1 Terminus	Gemini 2.5 Pro	Leader
AA Math Index	89.7	87.7	DeepSeek V3.1 Terminus
AIME 2025 (Artificial Analysis run)	89.7%	87.7%	DeepSeek V3.1 Terminus
OTIS Mock AIME	Not measured	84.7%	Only Gemini 2.5 Pro measured
FrontierMath, tiers 1 to 3	Not measured	24.6%	Only Gemini 2.5 Pro measured
FrontierMath, tier 4	Not measured	0.0%	Only Gemini 2.5 Pro measured
LMArena Text: math	1402	1450	Gemini 2.5 Pro
LMArena Text: mathematical work	Not measured	1454	Only Gemini 2.5 Pro measured
Neither model has a result on the other 6 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05

Knowledge benchmarks for DeepSeek V3.1 Terminus and Gemini 2.5 Pro
Benchmark	DeepSeek V3.1 Terminus	Gemini 2.5 Pro	Leader
CaseLaw (Vals AI)	Not measured	63.9%	Only Gemini 2.5 Pro measured
CorpFin (Vals AI)	Not measured	60.8%	Only Gemini 2.5 Pro measured
MortgageTax (Vals AI)	Not measured	68.9%	Only Gemini 2.5 Pro measured
MedCode (Vals AI)	Not measured	50.6%	Only Gemini 2.5 Pro measured
MMLU Pro (Artificial Analysis run)	85.1%	86.2%	Gemini 2.5 Pro
LMArena Text: science	1451	1480	Gemini 2.5 Pro
LMArena Text: medicine and healthcare	1468	1476	Gemini 2.5 Pro
LMArena Text: legal and government	1457	1480	Gemini 2.5 Pro
LMArena Text: business and finance	1414	1439	Gemini 2.5 Pro
FACTS Benchmark Suite	Not measured	61.5%	Only Gemini 2.5 Pro measured
Neither model has a result on the other 8 boards here

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

Web design benchmarks for DeepSeek V3.1 Terminus and Gemini 2.5 Pro
Benchmark	DeepSeek V3.1 Terminus	Gemini 2.5 Pro	Leader
Design Arena: websites	1187	1167	DeepSeek V3.1 Terminus
Design Arena: UI components	1182	1137	DeepSeek V3.1 Terminus
Design Arena: game dev	1131	1111	DeepSeek V3.1 Terminus
Design Arena: data viz	1168	1222	Gemini 2.5 Pro
Design Arena: 3D	1141	1080	DeepSeek V3.1 Terminus
Design Arena: all code categories	1175	1152	DeepSeek V3.1 Terminus
Design Arena: SVG	1065	Not measured	Only DeepSeek V3.1 Terminus measured
Neither model has a result on the other 2 boards here

GeneralLike-for-likePublic boards, shown as published, kept out of the index213

General benchmarks for DeepSeek V3.1 Terminus and Gemini 2.5 Pro
Benchmark	DeepSeek V3.1 Terminus	Gemini 2.5 Pro	Leader
SAGE (Vals AI)	Not measured	41.9%	Only Gemini 2.5 Pro measured
MedScribe (Vals AI)	Not measured	73.5%	Only Gemini 2.5 Pro measured
IFBench	57.0%	48.7%	DeepSeek V3.1 Terminus
Long context reasoning (AA-LCR)	69.3%	69.0%	DeepSeek V3.1 Terminus
DTBench	81.3%	82.4%	Gemini 2.5 Pro
LMCA	28.6%	34.8%	Gemini 2.5 Pro
Creative writing (Lech Mazur)	Not measured	83.8%	Only Gemini 2.5 Pro measured
LMArena Text	1419	1458	Gemini 2.5 Pro
LMArena Text: instruction following	1404	1437	Gemini 2.5 Pro
LMArena Text: creative writing	1403	1454	Gemini 2.5 Pro
LMArena Vision	Not measured	1263	Only Gemini 2.5 Pro measured
LMArena Text: English	1429	1458	Gemini 2.5 Pro
LMArena Text: non-English	1408	1452	Gemini 2.5 Pro
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	1435	1461	Gemini 2.5 Pro
LMArena Text: Polish	1427	1479	Gemini 2.5 Pro
LMArena Text: multi-turn	1411	1453	Gemini 2.5 Pro
LMArena Text: longer queries	1422	1449	Gemini 2.5 Pro
LMArena Text: writing and language	1409	1454	Gemini 2.5 Pro
LMArena Text: entertainment and media	1391	1441	Gemini 2.5 Pro
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 V3.1 Terminus and Gemini 2.5 Pro
Benchmark	DeepSeek V3.1 Terminus	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 V3.1 Terminus$0.0008Fits in one request

Gemini 2.5 Pro$0.0063Fits in one request

DeepSeek V3.1 Terminus costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.1 Terminus$0.018Fits in one request

Gemini 2.5 Pro$0.092Fits in one request

DeepSeek V3.1 Terminus costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.1 Terminus$0.071Exceeds the context window

Gemini 2.5 Pro$0.350Fits in one request

DeepSeek V3.1 Terminus 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 V3.1 Terminus and Gemini 2.5 Pro
Specification	DeepSeek V3.1 Terminus	Google Gemini 2.5 Pro
Maker	DeepSeek	Google
Weights	Open weights	No public weights listed
Released	September 22, 2025	June 17, 2025
Context window	164K	1.05M
Input $/Mtok	$0.3063	$1.25
Output $/Mtok	$1.0142	$10.00
Coding index	Not benchmarked	19.1
Independent measurements behind it	0	3
Cost per solved task	Not computable	$0.64
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V3.1 Terminus better than Gemini 2.5 Pro for coding?

Only Gemini 2.5 Pro has a coding score, 19.1 on the Vibe Coding Index; DeepSeek V3.1 Terminus has none, so the two cannot be ranked on coding. Pick DeepSeek V3.1 Terminus for output tokens 9.9x cheaper ($1.0142 vs $10.00 per million) and work measured by AA Coding Index or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, DeepSeek V3.1 Terminus or Gemini 2.5 Pro?

DeepSeek V3.1 Terminus costs $0.3063 per million input tokens and $1.0142 per million output tokens; Gemini 2.5 Pro costs $1.25 and $10.00. Cost per solved task is known for Gemini 2.5 Pro only.

Which has the bigger context window, DeepSeek V3.1 Terminus or Gemini 2.5 Pro?

Gemini 2.5 Pro, with 1.05M tokens against 164K.

How many benchmarks is the DeepSeek V3.1 Terminus vs Gemini 2.5 Pro comparison based on?

43 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.1 Terminus's coding index rests on 0 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.

DeepSeek V3.1 Terminus or Gemini 2.5 Pro: which to pick

Which one for your work

Coding

Intelligence

Agentic work

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

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