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Gemini 2.5 Flash vs GPT-5.4

GPT-5.4 leads Gemini 2.5 Flash on the Artificial Analysis Intelligence Index, 39.0 to 15.5, and Gemini 2.5 Flash costs 6.0x less per output token ($2.50 vs $15.00 per million). Updated October 22, 2026; 62 benchmarks have a result for both.

Gemini 2.5 FlashGoogle
Coding
No data
Overall
34.3
Output $/M
$2.50
3
GPT-5.4OpenAI
Coding
64.4
Overall
69.6
Output $/M
$15.00
59
vs boards won of 62 shared

Gemini 2.5 Flash or GPT-5.4: which to pick

Only GPT-5.4 has a coding score, 64.4 on the Vibe Coding Index; Gemini 2.5 Flash has none, so the two cannot be ranked on coding.

Pick Gemini 2.5 Flash for output tokens 6.0x cheaper ($2.50 vs $15.00 per million) and a lower cost per solved task ($0.466 against $1.02).

Shared results
62
Gemini 2.5 Flash only
11
GPT-5.4 only
48
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.

At a glance

Coding index
Gemini 2.5 Flash not benchmarked, GPT-5.4 64.4 from 3 benchmarks.
Widest category
Agentic work, 45.3 points apart on average over 2 shared benchmarks.
Widest single gap
ARC-AGI-2: Gemini 2.5 Flash 2.5% against GPT-5.4 74.0%, 71.4 points apart on a 0 to 100 scale.
Output price
Gemini 2.5 Flash $2.50 against GPT-5.4 $15.00 per million output tokens, 6.0x apart.
Context window
Gemini 2.5 Flash takes 1.05M tokens, GPT-5.4 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
CategoryGemini 2.5 FlashBarsGPT-5.4Ties
Coding04
Intelligence02
Agentic work02
Reasoning010
Math02
Knowledge05
Web design07
General327
All shared boards359
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for Gemini 2.5 Flash and GPT-5.4
BenchmarkGemini 2.5 FlashGPT-5.4Leader
SWE-bench Verified28.7%Not measuredOnly Gemini 2.5 Flash measured
LMArena WebDevNot measured1464Only GPT-5.4 measured
AA Coding IndexNot measured71.1Only GPT-5.4 measured
LiveCodeBench (Vals AI)Not measured84.1%Only GPT-5.4 measured
SWE-bench Verified (Vals AI)Not measured78.2%Only GPT-5.4 measured
Vibe Code Bench (Vals AI)Not measured67.4%Only GPT-5.4 measured
Code migration (Vals AI)Not measured35.0%Only GPT-5.4 measured
ProgramBench (Vals AI)Not measured0.5%Only GPT-5.4 measured
SkillsBench (Vals AI)Not measured51.7%Only GPT-5.4 measured
LiveCodeBench (Artificial Analysis run)69.5%Not measuredOnly Gemini 2.5 Flash measured
Terminal-Bench Hard (Artificial Analysis run)13.6%57.6%GPT-5.4
Terminal-Bench17.1%81.8%GPT-5.4
GSO-BenchNot measured31.4%Only GPT-5.4 measured
WeirdMLNot measured77.7%Only GPT-5.4 measured
MirrorCodeNot measured15.6%Only GPT-5.4 measured
ALE-BenchNot measured1607.0Only GPT-5.4 measured
LMArena Text: coding14231496GPT-5.4
LMArena Text: software and IT14251491GPT-5.4
LMArena WebDev: ReactNot measured1454Only GPT-5.4 measured
LMArena WebDev: HTMLNot measured1478Only GPT-5.4 measured
LMArena WebDev: from an imageNot measured1436Only GPT-5.4 measured
Neither model has a result on the other 18 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Gemini 2.5 Flash and GPT-5.4
BenchmarkGemini 2.5 FlashGPT-5.4Leader
AA Intelligence Index15.539.0GPT-5.4
Epoch Capabilities Index142.9157.3GPT-5.4
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for Gemini 2.5 Flash and GPT-5.4
BenchmarkGemini 2.5 FlashGPT-5.4Leader
Design Arena: agents building web appsNot measured1059Only GPT-5.4 measured
Design Arena: agents building full-stack appsNot measured1017Only GPT-5.4 measured
Design Arena: agents building mobile appsNot measured1086Only GPT-5.4 measured
Design Arena: agents building Android appsNot measured1032Only GPT-5.4 measured
Design Arena: agents building Godot gamesNot measured1135Only GPT-5.4 measured
τ²-Bench Telecom31.6%87.1%GPT-5.4
APEX-AgentsNot measured52.4%Only GPT-5.4 measured
METR time horizonsNot measured74.3%Only GPT-5.4 measured
BALROG33.5%Not measuredOnly Gemini 2.5 Flash measured
DeepResearch BenchNot measured35.1%Only GPT-5.4 measured
PostTrainBenchNot measured19.0%Only GPT-5.4 measured
Vending-Bench 2$549$6,144GPT-5.4
Berkeley Function Calling Leaderboard (BFCL)56.2%Not measuredOnly Gemini 2.5 Flash measured
BrowseComp-Plus39.9%Not measuredOnly Gemini 2.5 Flash measured
Neither model has a result on the other 27 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index010
Reasoning benchmarks for Gemini 2.5 Flash and GPT-5.4
BenchmarkGemini 2.5 FlashGPT-5.4Leader
GPQA DiamondNot measured91.7%Only GPT-5.4 measured
MMMU Pro (Vals AI)Not measured87.5%Only GPT-5.4 measured
GPQA Diamond (Artificial Analysis run)79.0%92.0%GPT-5.4
Humanity's Last Exam (Artificial Analysis run)12.1%43.7%GPT-5.4
GPQA Diamond (Epoch AI run)Not measured93.3%Only GPT-5.4 measured
ARC-AGI33.3%93.7%GPT-5.4
ARC-AGI-22.5%74.0%GPT-5.4
SimpleBench41.2%Not measuredOnly Gemini 2.5 Flash measured
Chess puzzlesNot measured48.0%Only GPT-5.4 measured
Mystery game puzzlesNot measured37.0%Only GPT-5.4 measured
EBR-benchNot measured25.4%Only GPT-5.4 measured
LMArena Text: hard prompts14221485GPT-5.4
LMArena Text: expert14261507GPT-5.4
LMArena Text: hard prompts in English14201479GPT-5.4
LMArena Vision: diagrams12451327GPT-5.4
LMArena Vision: homework12441336GPT-5.4
Kagi LLM Benchmark56.8%63.8%GPT-5.4
Neither model has a result on the other 5 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Gemini 2.5 Flash and GPT-5.4
BenchmarkGemini 2.5 FlashGPT-5.4Leader
AIMENot measured96.7%Only GPT-5.4 measured
AA Math Index73.3Not measuredOnly Gemini 2.5 Flash measured
AIME 2025 (Artificial Analysis run)73.3%Not measuredOnly Gemini 2.5 Flash measured
OTIS Mock AIMENot measured97.8%Only GPT-5.4 measured
FrontierMath, tiers 1 to 3Not measured78.6%Only GPT-5.4 measured
FrontierMath, tier 4Not measured49.0%Only GPT-5.4 measured
ProofBenchNot measured56.0%Only GPT-5.4 measured
LMArena Text: math14111488GPT-5.4
LMArena Text: mathematical work14221497GPT-5.4
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for Gemini 2.5 Flash and GPT-5.4
BenchmarkGemini 2.5 FlashGPT-5.4Leader
MMLU ProNot measured87.5%Only GPT-5.4 measured
LegalBench (Vals AI)Not measured86.0%Only GPT-5.4 measured
CaseLaw (Vals AI)Not measured63.8%Only GPT-5.4 measured
CorpFin (Vals AI)Not measured65.3%Only GPT-5.4 measured
TaxEval (Vals AI)Not measured74.0%Only GPT-5.4 measured
MortgageTax (Vals AI)Not measured68.3%Only GPT-5.4 measured
MedQA (Vals AI)Not measured96.1%Only GPT-5.4 measured
MedCode (Vals AI)40.4%41.3%GPT-5.4
MMLU Pro (Artificial Analysis run)83.2%Not measuredOnly Gemini 2.5 Flash measured
SimpleQA VerifiedNot measured45.1%Only GPT-5.4 measured
LMArena Text: science14331479GPT-5.4
LMArena Text: medicine and healthcare14281458GPT-5.4
LMArena Text: legal and government14301486GPT-5.4
LMArena Text: business and finance14041474GPT-5.4
FACTS Benchmark Suite47.2%Not measuredOnly Gemini 2.5 Flash measured
Neither model has a result on the other 3 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for Gemini 2.5 Flash and GPT-5.4
BenchmarkGemini 2.5 FlashGPT-5.4Leader
Design Arena: websites10741198GPT-5.4
Design Arena: UI components10031213GPT-5.4
Design Arena: game dev10041194GPT-5.4
Design Arena: data viz10601196GPT-5.4
Design Arena: 3D10201065GPT-5.4
Design Arena: all code categories10581179GPT-5.4
Design Arena: SVG10201141GPT-5.4
Design Arena: ASCII artNot measured1188Only GPT-5.4 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index327
General benchmarks for Gemini 2.5 Flash and GPT-5.4
BenchmarkGemini 2.5 FlashGPT-5.4Leader
SAGE (Vals AI)44.8%43.3%Gemini 2.5 Flash
MedScribe (Vals AI)83.0%77.5%Gemini 2.5 Flash
IFBench50.3%73.9%GPT-5.4
Long context reasoning (AA-LCR)65.3%82.0%GPT-5.4
DTBench76.5%94.4%GPT-5.4
LMCA27.5%52.0%GPT-5.4
CL-benchNot measured27.9%Only GPT-5.4 measured
Furniture assemblyNot measured37.5%Only GPT-5.4 measured
CL-bench LifeNot measured21.7%Only GPT-5.4 measured
LMArena Text14171469GPT-5.4
LMArena Text: instruction following14051469GPT-5.4
LMArena Text: creative writing14001439GPT-5.4
LMArena Vision12351303GPT-5.4
LMArena Text: English14181467GPT-5.4
LMArena Text: non-English14091465GPT-5.4
LMArena Text: Chinese14491520GPT-5.4
LMArena Text: German14171472GPT-5.4
LMArena Text: Japanese13961485GPT-5.4
LMArena Text: Korean13851449GPT-5.4
LMArena Text: French14321493GPT-5.4
LMArena Text: Spanish14221452GPT-5.4
LMArena Text: Russian14151480GPT-5.4
LMArena Text: Polish14131472GPT-5.4
LMArena Text: multi-turn14081482GPT-5.4
LMArena Text: longer queries14191473GPT-5.4
LMArena Text: writing and language14051458GPT-5.4
LMArena Text: entertainment and media13931438GPT-5.4
LMArena Vision: English12391301GPT-5.4
LMArena Vision: Chinese12841354GPT-5.4
LMArena Vision: reading text12371312GPT-5.4
LMArena Vision: humor12251284GPT-5.4
LMArena Vision: captioning1277Not measuredOnly Gemini 2.5 Flash measured
LMArena Vision: recognizing things12521251Gemini 2.5 Flash
LMArena Vision: creative writing12411283GPT-5.4
LMArena DocumentNot measured1471Only GPT-5.4 measured
Neither model has a result on the other 11 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemini 2.5 Flash and GPT-5.4
BenchmarkGemini 2.5 FlashGPT-5.4Leader
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 2.5 Flash$0.0015Fits in one request
GPT-5.4$0.010Fits in one request

Gemini 2.5 Flash costs less

Repository review

50K input + 3K output tokens

Gemini 2.5 Flash$0.022Fits in one request
GPT-5.4$0.170Fits in one request

Gemini 2.5 Flash costs less

Agent loop

200K input + 10K output tokens

Gemini 2.5 Flash$0.085Fits in one request
GPT-5.4$0.650Fits in one request

Gemini 2.5 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 2.5 Flash and GPT-5.4
SpecificationGoogle Gemini 2.5 FlashOpenAI GPT-5.4
MakerGoogleOpenAI
WeightsNo public weights listedNo public weights listed
ReleasedJune 17, 2025March 5, 2026
Context window1.05M1.05M
Input $/Mtok$0.30$2.50
Output $/Mtok$2.50$15.00
Coding indexNot benchmarked64.4
Independent measurements behind it03
Cost per solved task$0.466$1.02
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 2.5 Flash better than GPT-5.4 for coding?

Only GPT-5.4 has a coding score, 64.4 on the Vibe Coding Index; Gemini 2.5 Flash has none, so the two cannot be ranked on coding. Pick Gemini 2.5 Flash for output tokens 6.0x cheaper ($2.50 vs $15.00 per million) and a lower cost per solved task ($0.466 against $1.02).

Which is cheaper, Gemini 2.5 Flash or GPT-5.4?

Gemini 2.5 Flash costs $0.30 per million input tokens and $2.50 per million output tokens; GPT-5.4 costs $2.50 and $15.00. Per solved SWE-bench Verified task, Gemini 2.5 Flash costs $0.466 and GPT-5.4 $1.02.

Which has the bigger context window, Gemini 2.5 Flash or GPT-5.4?

GPT-5.4, with 1.05M tokens against 1.05M.

How many benchmarks is the Gemini 2.5 Flash vs GPT-5.4 comparison based on?

62 benchmarks have a result for both models as of October 22, 2026. Gemini 2.5 Flash's coding index rests on 0 independent benchmarks and GPT-5.4's on 3. Where only one model has a result, the page says so instead of naming a winner.

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