Gemini 3.8 Flash vs GPT-6.1 Terra

GPT-6.1 Terra leads Gemini 3.8 Flash on coding, 77.4 to 61.6 on the Vibe Coding Index, and Gemini 3.8 Flash costs 1.3x less per output token ($3.75 vs $5.00 per million). Updated October 29, 2026; 34 benchmarks have a result for both.

Replace Gemini 3.8 Flash Replace GPT-6.1 Terra

Gemini 3.8 FlashGoogle

Coding: 61.6
Overall: 65.5
Output $/M: $3.75

GPT-6.1 TerraOpenAI

Coding: 77.4
Overall: 76.8
Output $/M: $5.00

vs boards won of 34 shared

Gemini 3.8 Flash or GPT-6.1 Terra: which to pick

Pick GPT-6.1 Terra for coding: it scores 77.4 on the Vibe Coding Index against 61.6 for Gemini 3.8 Flash, though on 4 benchmarks against 6.

Pick Gemini 3.8 Flash for output tokens 1.3x cheaper ($3.75 vs $5.00 per million) and work measured by SciCode or LiveBench research, where it leads.

Model AGemini 3.8 FlashGoogle61.6/1006 independent benchmarks$2.74 per solved task Model BGPT-6.1 TerraOpenAI77.4/1004 independent benchmarksNo cost per solved task

341022

Shared results: 34
Gemini 3.8 Flash only: 102
GPT-6.1 Terra only: 2
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
GPT-6.1 Terra
GPT-6.1 Terra scores 77.4 against 61.6.
6 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-6.1 Terra
GPT-6.1 Terra leads, 51.0 against 30.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Gemini 3.8 Flash has a AA Agentic Index result, 40.2.
Directional only
Reasoning
GPQA Diamond
No call
Only Gemini 3.8 Flash has a GPQA Diamond result, 94.4%.
Directional only
Knowledge
MMLU Pro
No call
Only Gemini 3.8 Flash has a MMLU Pro result, 90.2%.
Directional only
Web design
Design Arena: websites
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 1299 against 1283.
Same board, both measured
General
Vals Index
GPT-6.1 Terra
GPT-6.1 Terra leads, 60.5% against 56.0%.
Same board, both measured
Long documents
Listed context window
GPT-6.1 Terra
GPT-6.1 Terra takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemini 3.8 Flash
Gemini 3.8 Flash $0.0026, GPT-6.1 Terra $0.0035 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Gemini 3.8 Flash has the number, $2.74.
Directional only

At a glance

Coding index: Gemini 3.8 Flash 61.6 from 6 benchmarks, GPT-6.1 Terra 77.4 from 4 benchmarks.
Widest category: Math, 31.4 points apart on average over 3 shared benchmarks.
Widest single gap: FrontierMath, tier 4: Gemini 3.8 Flash 22.0% against GPT-6.1 Terra 92.7%, 70.7 points apart on a 0 to 100 scale.
Output price: Gemini 3.8 Flash $3.75 against GPT-6.1 Terra $5.00 per million output tokens, 1.3x apart.
Context window: Gemini 3.8 Flash takes 1.05M tokens, GPT-6.1 Terra 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	Gemini 3.8 Flash	GPT-6.1 Terra
Coding	1	7
Intelligence	0	1
Agentic work	4	3
Reasoning	3	2
Math	0	3
Knowledge	1	0
Web design	3	2
General	1	3
All shared boards	13	21

CodingLike-for-likeSix of these boards feed the Vibe Coding Index17

Coding benchmarks for Gemini 3.8 Flash and GPT-6.1 Terra
Benchmark	Gemini 3.8 Flash	GPT-6.1 Terra	Leader
LiveBench coding	82.2%	94.3%	GPT-6.1 Terra
LMArena WebDev	1583	1673	GPT-6.1 Terra
AA Coding Index	76.3	Not measured	Only Gemini 3.8 Flash measured
LiveCodeBench (Vals AI)	89.5%	Not measured	Only Gemini 3.8 Flash measured
SWE-bench Verified (Vals AI)	80.0%	Not measured	Only Gemini 3.8 Flash measured
Terminal-Bench 4 (Vals AI)	19.7%	49.5%	GPT-6.1 Terra
Vibe Code Bench (Vals AI)	78.7%	84.0%	GPT-6.1 Terra
Code migration (Vals AI)	36.5%	Not measured	Only Gemini 3.8 Flash measured
ProgramBench (Vals AI)	1.0%	Not measured	Only Gemini 3.8 Flash measured
IOI (Vals AI)	56.9%	Not measured	Only Gemini 3.8 Flash measured
Terminal-Bench 2.1 (Vals AI)	81.3%	Not measured	Only Gemini 3.8 Flash measured
SWE-bench Multimodal (Vals AI)	40.8%	Not measured	Only Gemini 3.8 Flash measured
Vibe Code Bench 1-100 (Vals AI)	18.8%	Not measured	Only Gemini 3.8 Flash measured
SkillsBench (Vals AI)	58.0%	Not measured	Only Gemini 3.8 Flash measured
CyberBench (Vals AI)	43.8%	Not measured	Only Gemini 3.8 Flash measured
SciCode	56.6%	55.1%	Gemini 3.8 Flash
FrontierCode	41.2%	Not measured	Only Gemini 3.8 Flash measured
FrontierSWE	19.6%	Not measured	Only Gemini 3.8 Flash measured
WeirdML	84.8%	Not measured	Only Gemini 3.8 Flash measured
ALE-Bench	1269.7	2608.8	GPT-6.1 Terra
LMArena Text: coding	1509	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: software and IT	1507	Not measured	Only Gemini 3.8 Flash measured
LMArena WebDev: React	1577	1657	GPT-6.1 Terra
LMArena WebDev: HTML	1612	1719	GPT-6.1 Terra
LMArena WebDev: from an image	1593	Not measured	Only Gemini 3.8 Flash measured
SciCode	12.3%	Not measured	Only Gemini 3.8 Flash measured
SWE-Bench Pro V2	94.9%	Not measured	Only Gemini 3.8 Flash measured
SWE Atlas: Codebase QnA	47.0%	Not measured	Only Gemini 3.8 Flash measured
SWE Atlas: Refactoring	44.8%	Not measured	Only Gemini 3.8 Flash measured
SWE Atlas: Test Writing	53.7%	Not measured	Only Gemini 3.8 Flash measured
Neither model has a result on the other 13 boards here

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

Intelligence benchmarks for Gemini 3.8 Flash and GPT-6.1 Terra
Benchmark	Gemini 3.8 Flash	GPT-6.1 Terra	Leader
AA Intelligence Index	30.5	51.0	GPT-6.1 Terra
Epoch Capabilities Index	157.4	Not measured	Only Gemini 3.8 Flash measured

Agentic workLike-for-likePublic boards, shown as published, kept out of the index43

Agentic work benchmarks for Gemini 3.8 Flash and GPT-6.1 Terra
Benchmark	Gemini 3.8 Flash	GPT-6.1 Terra	Leader
AA Agentic Index	40.2	Not measured	Only Gemini 3.8 Flash measured
Design Arena: agents building web apps	1240	Not measured	Only Gemini 3.8 Flash measured
Design Arena: agents building full-stack apps	1244	Not measured	Only Gemini 3.8 Flash measured
Design Arena: agents building mobile apps	1235	Not measured	Only Gemini 3.8 Flash measured
Design Arena: agents building Android apps	1080	Not measured	Only Gemini 3.8 Flash measured
Design Arena: agents building Godot games	1273	Not measured	Only Gemini 3.8 Flash measured
Design Arena: PowerPoint slides	1138	Not measured	Only Gemini 3.8 Flash measured
Design Arena: spreadsheets	1058	Not measured	Only Gemini 3.8 Flash measured
LiveBench research	79.1%	73.9%	Gemini 3.8 Flash
LiveBench browser	45.1%	93.1%	GPT-6.1 Terra
LiveBench long agentic	16.5%	48.6%	GPT-6.1 Terra
SREBench (Vals AI)	11.1%	Not measured	Only Gemini 3.8 Flash measured
Finance Agent (Vals AI)	61.4%	Not measured	Only Gemini 3.8 Flash measured
Tax Agent Bench (Vals AI)	32.1%	15.2%	Gemini 3.8 Flash
Legal Research Bench (Vals AI)	38.9%	26.0%	Gemini 3.8 Flash
Harvey's Legal Agent Benchmark (Vals AI)	12.1%	1.7%	Gemini 3.8 Flash
Excel Modeling Benchmark (Vals AI)	72.2%	Not measured	Only Gemini 3.8 Flash measured
BioMysteryBench (Vals AI)	62.2%	Not measured	Only Gemini 3.8 Flash measured
Terminal-Bench-Science (Vals AI)	8.6%	Not measured	Only Gemini 3.8 Flash measured
Vals Science Index	35.8%	Not measured	Only Gemini 3.8 Flash measured
Time Horizon Index (Vals AI)	18.8%	Not measured	Only Gemini 3.8 Flash measured
CUA-bench (Vals AI)	4.2%	Not measured	Only Gemini 3.8 Flash measured
APEX-Agents	64.3%	68.3%	GPT-6.1 Terra
Remote Labor Index	5.8%	Not measured	Only Gemini 3.8 Flash measured
Vending-Bench 2	$5,094	Not measured	Only Gemini 3.8 Flash measured
Neither model has a result on the other 17 boards here

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

Reasoning benchmarks for Gemini 3.8 Flash and GPT-6.1 Terra
Benchmark	Gemini 3.8 Flash	GPT-6.1 Terra	Leader
GPQA Diamond	94.4%	Not measured	Only Gemini 3.8 Flash measured
LiveBench reasoning	89.3%	88.7%	Gemini 3.8 Flash
MysteryMechanism (Vals AI)	36.5%	Not measured	Only Gemini 3.8 Flash measured
MMMU Pro (Vals AI)	89.1%	Not measured	Only Gemini 3.8 Flash measured
GPQA Diamond (Artificial Analysis run)	95.3%	Not measured	Only Gemini 3.8 Flash measured
Humanity's Last Exam (Artificial Analysis run)	47.8%	46.2%	Gemini 3.8 Flash
GPQA Diamond (Epoch AI run)	95.4%	94.3%	Gemini 3.8 Flash
ARC-AGI	98.5%	Not measured	Only Gemini 3.8 Flash measured
ARC-AGI-2	89.2%	Not measured	Only Gemini 3.8 Flash measured
Chess puzzles	58.0%	Not measured	Only Gemini 3.8 Flash measured
Mystery game puzzles	47.0%	74.0%	GPT-6.1 Terra
EBR-bench	Not measured	78.1%	Only GPT-6.1 Terra measured
LMArena Text: hard prompts	1508	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: expert	1526	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: hard prompts in English	1504	Not measured	Only Gemini 3.8 Flash measured
LMArena Vision: diagrams	1345	Not measured	Only Gemini 3.8 Flash measured
LMArena Vision: homework	1351	Not measured	Only Gemini 3.8 Flash measured
Humanity's Last Exam (Scale run)	34.3%	35.7%	GPT-6.1 Terra
Neither model has a result on the other 5 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index03

Math benchmarks for Gemini 3.8 Flash and GPT-6.1 Terra
Benchmark	Gemini 3.8 Flash	GPT-6.1 Terra	Leader
OTIS Mock AIME	98.9%	100.0%	GPT-6.1 Terra
FrontierMath, tiers 1 to 3	68.4%	90.9%	GPT-6.1 Terra
FrontierMath, tier 4	22.0%	92.7%	GPT-6.1 Terra
ProofBench	48.0%	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: math	1530	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: mathematical work	1519	Not measured	Only Gemini 3.8 Flash measured
MathArena	33.6%	Not measured	Only Gemini 3.8 Flash measured
Neither model has a result on the other 7 boards here

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

Knowledge benchmarks for Gemini 3.8 Flash and GPT-6.1 Terra
Benchmark	Gemini 3.8 Flash	GPT-6.1 Terra	Leader
MMLU Pro	90.2%	Not measured	Only Gemini 3.8 Flash measured
LegalBench (Vals AI)	87.0%	Not measured	Only Gemini 3.8 Flash measured
TaxEval (Vals AI)	74.5%	Not measured	Only Gemini 3.8 Flash measured
MortgageTax (Vals AI)	65.3%	Not measured	Only Gemini 3.8 Flash measured
MedCode (Vals AI)	48.1%	Not measured	Only Gemini 3.8 Flash measured
SimpleQA Verified	69.7%	57.3%	Gemini 3.8 Flash
LMArena Text: science	1523	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: medicine and healthcare	1506	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: legal and government	1500	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: business and finance	1480	Not measured	Only Gemini 3.8 Flash measured
FACTS Benchmark Suite	68.5%	Not measured	Only Gemini 3.8 Flash measured
PRBench Finance	49.5%	Not measured	Only Gemini 3.8 Flash measured
PRBench Legal	48.9%	Not measured	Only Gemini 3.8 Flash measured
Neither model has a result on the other 7 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index32

Web design benchmarks for Gemini 3.8 Flash and GPT-6.1 Terra
Benchmark	Gemini 3.8 Flash	GPT-6.1 Terra	Leader
Design Arena: websites	1299	1283	Gemini 3.8 Flash
Design Arena: UI components	1314	1277	Gemini 3.8 Flash
Design Arena: game dev	1304	1348	GPT-6.1 Terra
Design Arena: data viz	1274	Not measured	Only Gemini 3.8 Flash measured
Design Arena: 3D	1292	Not measured	Only Gemini 3.8 Flash measured
Design Arena: all code categories	1302	1295	Gemini 3.8 Flash
Design Arena: SVG	1292	Not measured	Only Gemini 3.8 Flash measured
Design Arena: ASCII art	1274	Not measured	Only Gemini 3.8 Flash measured
LiveBench design	72.7%	82.3%	GPT-6.1 Terra

GeneralLike-for-likePublic boards, shown as published, kept out of the index13

General benchmarks for Gemini 3.8 Flash and GPT-6.1 Terra
Benchmark	Gemini 3.8 Flash	GPT-6.1 Terra	Leader
Vals Index	56.0%	60.5%	GPT-6.1 Terra
LiveBench data analysis	54.0%	82.3%	GPT-6.1 Terra
Public Benefits Bench (Vals AI)	65.3%	Not measured	Only Gemini 3.8 Flash measured
SAGE (Vals AI)	88.2%	Not measured	Only Gemini 3.8 Flash measured
MedScribe (Vals AI)	84.5%	Not measured	Only Gemini 3.8 Flash measured
Long context reasoning (AA-LCR)	84.0%	82.7%	Gemini 3.8 Flash
DTBench	95.7%	Not measured	Only Gemini 3.8 Flash measured
LMCA	52.9%	Not measured	Only Gemini 3.8 Flash measured
Surface Evolver Bench	76.9%	Not measured	Only Gemini 3.8 Flash measured
Furniture assembly	31.7%	79.2%	GPT-6.1 Terra
LMArena Text	1498	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: instruction following	1493	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: creative writing	1491	Not measured	Only Gemini 3.8 Flash measured
LMArena Vision	1315	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: English	1498	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: non-English	1492	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: Chinese	1550	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: German	1507	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: Japanese	1508	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: Korean	1477	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: French	1494	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: Spanish	1487	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: Russian	1516	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: Polish	1499	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: multi-turn	1504	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: longer queries	1509	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: writing and language	1495	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: entertainment and media	1481	Not measured	Only Gemini 3.8 Flash measured
LMArena Vision: English	1318	Not measured	Only Gemini 3.8 Flash measured
LMArena Vision: Chinese	1416	Not measured	Only Gemini 3.8 Flash measured
LMArena Vision: reading text	1333	Not measured	Only Gemini 3.8 Flash measured
LMArena Vision: humor	1321	Not measured	Only Gemini 3.8 Flash measured
LMArena Vision: creative writing	1299	Not measured	Only Gemini 3.8 Flash measured
MultiChallenge	Not measured	74.2%	Only GPT-6.1 Terra measured
Neither model has a result on the other 14 boards here

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

Image generation benchmarks for Gemini 3.8 Flash and GPT-6.1 Terra
Benchmark	Gemini 3.8 Flash	GPT-6.1 Terra	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

Gemini 3.8 Flash$0.0026Fits in one request

GPT-6.1 Terra$0.0035Fits in one request

Gemini 3.8 Flash costs less

Repository review

50K input + 3K output tokens

Gemini 3.8 Flash$0.049Fits in one request

GPT-6.1 Terra$0.065Fits in one request

Gemini 3.8 Flash costs less

Agent loop

200K input + 10K output tokens

Gemini 3.8 Flash$0.188Fits in one request

GPT-6.1 Terra$0.250Fits in one request

Gemini 3.8 Flash 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 Gemini 3.8 Flash and GPT-6.1 Terra
Specification	Google Gemini 3.8 Flash	OpenAI GPT-6.1 Terra
Maker	Google	OpenAI
Weights	No public weights listed	No public weights listed
Released	September 2, 2026	October 26, 2026
Context window	1.05M	1.05M
Input $/Mtok	$0.75	$1.00
Output $/Mtok	$3.75	$5.00
Coding index	61.6	77.4
Independent measurements behind it	6	4
Cost per solved task	$2.74	Not computable
Source	OpenRouter listing	OpenRouter listing

Questions

Is Gemini 3.8 Flash better than GPT-6.1 Terra for coding?

Pick GPT-6.1 Terra for coding: it scores 77.4 on the Vibe Coding Index against 61.6 for Gemini 3.8 Flash, though on 4 benchmarks against 6. Pick Gemini 3.8 Flash for output tokens 1.3x cheaper ($3.75 vs $5.00 per million) and work measured by SciCode or LiveBench research, where it leads.

Which is cheaper, Gemini 3.8 Flash or GPT-6.1 Terra?

Gemini 3.8 Flash costs $0.75 per million input tokens and $3.75 per million output tokens; GPT-6.1 Terra costs $1.00 and $5.00. Cost per solved task is known for Gemini 3.8 Flash only.

Which has the bigger context window, Gemini 3.8 Flash or GPT-6.1 Terra?

GPT-6.1 Terra, with 1.05M tokens against 1.05M.

How many benchmarks is the Gemini 3.8 Flash vs GPT-6.1 Terra comparison based on?

34 benchmarks have a result for both models as of October 29, 2026. Gemini 3.8 Flash's coding index rests on 6 independent benchmarks and GPT-6.1 Terra's on 4. Where only one model has a result, the page says so instead of naming a winner.

Compare next

Embed this comparison on your site

Free under CC BY 4.0, while the source link stays visible. Preview the frame · Terms and other embeds

<iframe src="https://llmbenchmark.ai/embed/compare/gemini-3-8-flash-vs-gpt-6-1-terra" title="Gemini 3.8 Flash vs GPT-6.1 Terra, LLMBenchmark.ai" width="100%" height="360" style="border:0;max-width:100%" loading="lazy"></iframe>
<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/gemini-3-8-flash-vs-gpt-6-1-terra">LLMBenchmark.ai, Gemini 3.8 Flash vs GPT-6.1 Terra</a></p>

Gemini 3.8 Flash or GPT-6.1 Terra: which to pick

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

Knowledge

Web design

General

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