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Gemini 2.5 Pro Preview 06-05 vs GPT-4

Gemini 2.5 Pro Preview 06-05 costs 6.0x less per output token than GPT-4 ($10.00 vs $60.00 per million). Updated October 22, 2026; 2 benchmarks have a result for both.

Gemini 2.5 Pro Preview 06-05Google
Coding
No data
Overall
No data
Output $/M
$10.00
2
GPT-4OpenAI
Coding
No data
Overall
No data
Output $/M
$60.00
0
vs boards won of 2 shared

Gemini 2.5 Pro Preview 06-05 or GPT-4: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick Gemini 2.5 Pro Preview 06-05 for output tokens 6.0x cheaper ($10.00 vs $60.00 per million) and a larger context window (1.05M against 8K tokens).

Shared results
2
Gemini 2.5 Pro Preview 06-05 only
13
GPT-4 only
43
Like-for-like categories
2 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 Pro Preview 06-05 not benchmarked, GPT-4 not benchmarked.
Widest category
Reasoning, 48.7 points apart on average over 1 shared benchmark.
Widest single gap
GPQA Diamond (Artificial Analysis run): Gemini 2.5 Pro Preview 06-05 83.6% against GPT-4 34.9%, 48.7 points apart on a 0 to 100 scale.
Output price
Gemini 2.5 Pro Preview 06-05 $10.00 against GPT-4 $60.00 per million output tokens, 6.0x apart.
Context window
Gemini 2.5 Pro Preview 06-05 takes 1.05M tokens, GPT-4 8K.

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 Pro Preview 06-05BarsGPT-4Ties
Reasoning10
Knowledge10
All shared boards20
CodingDirectional onlySix of these boards feed the Vibe Coding Index11 boards, none shared
Coding benchmarks for Gemini 2.5 Pro Preview 06-05 and GPT-4
BenchmarkGemini 2.5 Pro Preview 06-05GPT-4Leader
Aider polyglot83.1%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
SWE-bench VerifiedNot measured22.4%Only GPT-4 measured
SWE-bench LiteNot measured39.7%Only GPT-4 measured
SWE-bench (full test set)Not measured12.5%Only GPT-4 measured
AA Coding IndexNot measured13.1Only GPT-4 measured
LiveCodeBench (Vals AI)79.2%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
LiveCodeBench (Artificial Analysis run)77.8%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
GSO-Bench3.9%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
LMArena Text: codingNot measured1209Only GPT-4 measured
LMArena Text: software and ITNot measured1195Only GPT-4 measured
SciCode7.7%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
Neither model has a result on the other 28 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for Gemini 2.5 Pro Preview 06-05 and GPT-4
BenchmarkGemini 2.5 Pro Preview 06-05GPT-4Leader
AA Intelligence IndexNot measured6.7Only GPT-4 measured
Epoch Capabilities IndexNot measured125.7Only GPT-4 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Agentic work benchmarks for Gemini 2.5 Pro Preview 06-05 and GPT-4
BenchmarkGemini 2.5 Pro Preview 06-05GPT-4Leader
METR time horizons55.4%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
Remote Labor Index0.8%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
DeepResearch Bench42.8%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
Reasoning benchmarks for Gemini 2.5 Pro Preview 06-05 and GPT-4
BenchmarkGemini 2.5 Pro Preview 06-05GPT-4Leader
GPQA Diamond (Artificial Analysis run)83.6%34.9%Gemini 2.5 Pro Preview 06-05
Humanity's Last Exam (Artificial Analysis run)19.9%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
GPQA Diamond (Epoch AI run)84.8%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
SimpleBench62.4%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
VPCT46.4%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
BIG-Bench HardNot measured75.1%Only GPT-4 measured
LMArena Text: hard promptsNot measured1200Only GPT-4 measured
LMArena Text: expertNot measured1149Only GPT-4 measured
LMArena Text: hard prompts in EnglishNot measured1209Only GPT-4 measured
Neither model has a result on the other 13 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Math benchmarks for Gemini 2.5 Pro Preview 06-05 and GPT-4
BenchmarkGemini 2.5 Pro Preview 06-05GPT-4Leader
GSM8KNot measured92.0%Only GPT-4 measured
LMArena Text: mathNot measured1230Only GPT-4 measured
LMArena Text: mathematical workNot measured1228Only GPT-4 measured
Neither model has a result on the other 10 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
Knowledge benchmarks for Gemini 2.5 Pro Preview 06-05 and GPT-4
BenchmarkGemini 2.5 Pro Preview 06-05GPT-4Leader
MMLU Pro (Artificial Analysis run)85.8%56.2%Gemini 2.5 Pro Preview 06-05
MMLUNot measured86.4%Only GPT-4 measured
AI2 Reasoning ChallengeNot measured96.3%Only GPT-4 measured
LMArena Text: scienceNot measured1177Only GPT-4 measured
LMArena Text: medicine and healthcareNot measured1145Only GPT-4 measured
LMArena Text: legal and governmentNot measured1191Only GPT-4 measured
LMArena Text: business and financeNot measured1145Only GPT-4 measured
Neither model has a result on the other 11 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for Gemini 2.5 Pro Preview 06-05 and GPT-4
BenchmarkGemini 2.5 Pro Preview 06-05GPT-4Leader
Neither model has a result on 9 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index23 boards, none shared
General benchmarks for Gemini 2.5 Pro Preview 06-05 and GPT-4
BenchmarkGemini 2.5 Pro Preview 06-05GPT-4Leader
IFBenchNot measured33.2%Only GPT-4 measured
DTBenchNot measured62.7%Only GPT-4 measured
LMCANot measured17.1%Only GPT-4 measured
Fiction.LiveBench91.7%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
HellaSwagNot measured95.3%Only GPT-4 measured
WinoGrandeNot measured87.5%Only GPT-4 measured
LMArena TextNot measured1206Only GPT-4 measured
LMArena Text: instruction followingNot measured1201Only GPT-4 measured
LMArena Text: creative writingNot measured1191Only GPT-4 measured
LMArena Text: EnglishNot measured1220Only GPT-4 measured
LMArena Text: non-EnglishNot measured1194Only GPT-4 measured
LMArena Text: ChineseNot measured1184Only GPT-4 measured
LMArena Text: GermanNot measured1198Only GPT-4 measured
LMArena Text: JapaneseNot measured1137Only GPT-4 measured
LMArena Text: KoreanNot measured1087Only GPT-4 measured
LMArena Text: FrenchNot measured1220Only GPT-4 measured
LMArena Text: SpanishNot measured1197Only GPT-4 measured
LMArena Text: RussianNot measured1195Only GPT-4 measured
LMArena Text: PolishNot measured1265Only GPT-4 measured
LMArena Text: multi-turnNot measured1206Only GPT-4 measured
LMArena Text: longer queriesNot measured1190Only GPT-4 measured
LMArena Text: writing and languageNot measured1219Only GPT-4 measured
LMArena Text: entertainment and mediaNot measured1184Only GPT-4 measured
Neither model has a result on the other 23 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemini 2.5 Pro Preview 06-05 and GPT-4
BenchmarkGemini 2.5 Pro Preview 06-05GPT-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 Pro Preview 06-05$0.0063Fits in one request
GPT-4$0.060Fits in one request

Gemini 2.5 Pro Preview 06-05 costs less

Repository review

50K input + 3K output tokens

Gemini 2.5 Pro Preview 06-05$0.092Fits in one request
GPT-4$1.68Exceeds the context window

Gemini 2.5 Pro Preview 06-05 costs less

Agent loop

200K input + 10K output tokens

Gemini 2.5 Pro Preview 06-05$0.350Fits in one request
GPT-4$6.60Exceeds the context window

Gemini 2.5 Pro Preview 06-05 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 Pro Preview 06-05 and GPT-4
SpecificationGoogle Gemini 2.5 Pro Preview 06-05OpenAI GPT-4
MakerGoogleOpenAI
WeightsNo public weights listedNo public weights listed
ReleasedJune 5, 2025May 28, 2023
Context window1.05M8K
Input $/Mtok$1.25$30.00
Output $/Mtok$10.00$60.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 2.5 Pro Preview 06-05 better than GPT-4 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemini 2.5 Pro Preview 06-05 for output tokens 6.0x cheaper ($10.00 vs $60.00 per million) and a larger context window (1.05M against 8K tokens).

Which is cheaper, Gemini 2.5 Pro Preview 06-05 or GPT-4?

Gemini 2.5 Pro Preview 06-05 costs $1.25 per million input tokens and $10.00 per million output tokens; GPT-4 costs $30.00 and $60.00. Cost per solved task is known for neither of them.

Which has the bigger context window, Gemini 2.5 Pro Preview 06-05 or GPT-4?

Gemini 2.5 Pro Preview 06-05, with 1.05M tokens against 8K.

How many benchmarks is the Gemini 2.5 Pro Preview 06-05 vs GPT-4 comparison based on?

2 benchmarks have a result for both models as of October 22, 2026. Gemini 2.5 Pro Preview 06-05's coding index rests on 0 independent benchmarks and GPT-4's on 0. Where only one model has a result, the page says so instead of naming a winner.

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