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Gemini 2.5 Pro vs gpt-oss-120b

Gemini 2.5 Pro leads gpt-oss-120b on the Artificial Analysis Intelligence Index, 16.1 to 11.6, and gpt-oss-120b costs 20.0x less per output token ($0.50 vs $10.00 per million). Updated October 22, 2026; 63 benchmarks have a result for both.

Replace Gemini 2.5 Pro Replace gpt-oss-120b
Gemini 2.5 ProGoogle
Coding
19.1
Overall
41.8
Output $/M
$10.00
55
gpt-oss-120bOpenAI
Coding
No data
Overall
50.0
Output $/M
$0.50
8
vs boards won of 63 shared

Gemini 2.5 Pro or gpt-oss-120b: which to pick

Only Gemini 2.5 Pro has a coding score, 19.1 on the Vibe Coding Index; gpt-oss-120b has none, so the two cannot be ranked on coding.

Pick gpt-oss-120b for output tokens 20.0x cheaper ($0.50 vs $10.00 per million) and a lower cost per solved task ($0.51 against $0.64).

Model AGemini 2.5 ProGoogle19.1/1003 independent benchmarks$0.64 per solved taskModel Bgpt-oss-120bOpenAINot benchmarked1 independent benchmark$0.51 per solved task
Shared results
63
Gemini 2.5 Pro only
27
gpt-oss-120b only
18
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 Pro 19.1 from 3 benchmarks, gpt-oss-120b not benchmarked.
Widest category
Web design, 23.7 points apart on average over 6 shared benchmarks.
Widest single gap
BrowseComp-Plus: Gemini 2.5 Pro 29.5% against gpt-oss-120b 67.0%, 37.5 points apart on a 0 to 100 scale.
Output price
Gemini 2.5 Pro $10.00 against gpt-oss-120b $0.50 per million output tokens, 20.0x apart.
Context window
Gemini 2.5 Pro takes 1.05M tokens, gpt-oss-120b 131K.

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 ProBarsgpt-oss-120bTies
Coding91
Intelligence20
Agentic work13
Reasoning70
Math23
Knowledge70
Web design60
General211
All shared boards558
CodingLike-for-likeSix of these boards feed the Vibe Coding Index91
Coding benchmarks for Gemini 2.5 Pro and gpt-oss-120b
BenchmarkGemini 2.5 Progpt-oss-120bLeader
Aider polyglotNot measured41.8%Only gpt-oss-120b measured
SWE-bench Verified53.6%26.0%Gemini 2.5 Pro
LMArena WebDev1226Not measuredOnly Gemini 2.5 Pro measured
AA Coding Index33.330.4Gemini 2.5 Pro
LiveCodeBench (Vals AI)Not measured83.2%Only gpt-oss-120b measured
SWE-bench Verified (Vals AI)54.4%33.6%Gemini 2.5 Pro
Vibe Code Bench (Vals AI)0.4%Not measuredOnly Gemini 2.5 Pro measured
LiveCodeBench (Artificial Analysis run)80.1%87.8%gpt-oss-120b
SciCode46.3%34.0%Gemini 2.5 Pro
Terminal-Bench Hard (Artificial Analysis run)26.5%23.5%Gemini 2.5 Pro
Terminal-Bench32.6%18.7%Gemini 2.5 Pro
GSO-Bench3.9%Not measuredOnly Gemini 2.5 Pro measured
WeirdML54.0%48.2%Gemini 2.5 Pro
ALE-BenchNot measured575.6Only gpt-oss-120b measured
LMArena Text: coding14521379Gemini 2.5 Pro
LMArena Text: software and IT14591387Gemini 2.5 Pro
LMArena WebDev: HTML1235Not measuredOnly Gemini 2.5 Pro measured
LMArena WebDev: from an image1285Not measuredOnly Gemini 2.5 Pro measured
SciCodeNot measured7.7%Only gpt-oss-120b measured
Neither model has a result on the other 20 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for Gemini 2.5 Pro and gpt-oss-120b
BenchmarkGemini 2.5 Progpt-oss-120bLeader
AA Intelligence Index16.111.6Gemini 2.5 Pro
Epoch Capabilities Index145.1139.8Gemini 2.5 Pro
Agentic workLike-for-likePublic boards, shown as published, kept out of the index13
Agentic work benchmarks for Gemini 2.5 Pro and gpt-oss-120b
BenchmarkGemini 2.5 Progpt-oss-120bLeader
AA Agentic Index1.63.7gpt-oss-120b
τ²-Bench Telecom54.1%65.8%gpt-oss-120b
APEX-AgentsNot measured4.4%Only gpt-oss-120b measured
METR time horizonsNot measured56.6%Only gpt-oss-120b measured
GDPval23.3%Not measuredOnly Gemini 2.5 Pro measured
DeepResearch Bench41.5%Not measuredOnly Gemini 2.5 Pro measured
Vending-Bench 2$574-$22Gemini 2.5 Pro
BrowseComp-Plus29.5%67.0%gpt-oss-120b
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index70
Reasoning benchmarks for Gemini 2.5 Pro and gpt-oss-120b
BenchmarkGemini 2.5 Progpt-oss-120bLeader
GPQA DiamondNot measured78.5%Only gpt-oss-120b measured
GPQA Diamond (Artificial Analysis run)84.4%78.2%Gemini 2.5 Pro
Humanity's Last Exam (Artificial Analysis run)22.5%19.6%Gemini 2.5 Pro
GPQA Diamond (Epoch AI run)85.3%75.8%Gemini 2.5 Pro
ARC-AGI41.0%Not measuredOnly Gemini 2.5 Pro measured
ARC-AGI-24.9%Not measuredOnly Gemini 2.5 Pro measured
SimpleBenchNot measured22.1%Only gpt-oss-120b measured
Mystery game puzzlesNot measured2.0%Only gpt-oss-120b measured
LMArena Text: hard prompts14551364Gemini 2.5 Pro
LMArena Text: expert14521356Gemini 2.5 Pro
LMArena Text: hard prompts in English14531368Gemini 2.5 Pro
LMArena Vision: diagrams1274Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: homework1281Not measuredOnly Gemini 2.5 Pro measured
Kagi LLM Benchmark70.3%58.6%Gemini 2.5 Pro
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index23
Math benchmarks for Gemini 2.5 Pro and gpt-oss-120b
BenchmarkGemini 2.5 Progpt-oss-120bLeader
AIMENot measured92.6%Only gpt-oss-120b measured
MATH 500 (Vals AI)Not measured94.8%Only gpt-oss-120b measured
MGSM (Vals AI)Not measured92.0%Only gpt-oss-120b measured
AA Math Index87.793.4gpt-oss-120b
AIME 2025 (Artificial Analysis run)87.7%93.4%gpt-oss-120b
OTIS Mock AIME84.7%88.9%gpt-oss-120b
FrontierMath, tiers 1 to 324.6%Not measuredOnly Gemini 2.5 Pro measured
FrontierMath, tier 40.0%Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: math14501389Gemini 2.5 Pro
LMArena Text: mathematical work14541386Gemini 2.5 Pro
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index70
Knowledge benchmarks for Gemini 2.5 Pro and gpt-oss-120b
BenchmarkGemini 2.5 Progpt-oss-120bLeader
MMLU ProNot measured79.2%Only gpt-oss-120b measured
LegalBench (Vals AI)Not measured75.9%Only gpt-oss-120b measured
CaseLaw (Vals AI)63.9%48.8%Gemini 2.5 Pro
CorpFin (Vals AI)60.8%58.2%Gemini 2.5 Pro
TaxEval (Vals AI)Not measured71.6%Only gpt-oss-120b measured
MortgageTax (Vals AI)68.9%Not measuredOnly Gemini 2.5 Pro measured
MedQA (Vals AI)Not measured91.4%Only gpt-oss-120b measured
MedCode (Vals AI)50.6%Not measuredOnly Gemini 2.5 Pro measured
MMLU Pro (Artificial Analysis run)86.2%80.8%Gemini 2.5 Pro
LMArena Text: science14801382Gemini 2.5 Pro
LMArena Text: medicine and healthcare14761388Gemini 2.5 Pro
LMArena Text: legal and government14801355Gemini 2.5 Pro
LMArena Text: business and finance14391359Gemini 2.5 Pro
FACTS Benchmark Suite61.5%Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index60
Web design benchmarks for Gemini 2.5 Pro and gpt-oss-120b
BenchmarkGemini 2.5 Progpt-oss-120bLeader
Design Arena: websites1167967Gemini 2.5 Pro
Design Arena: UI components1137924Gemini 2.5 Pro
Design Arena: game dev1111992Gemini 2.5 Pro
Design Arena: data viz1222987Gemini 2.5 Pro
Design Arena: 3D1080901Gemini 2.5 Pro
Design Arena: all code categories1152962Gemini 2.5 Pro
Neither model has a result on the other 3 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index211
General benchmarks for Gemini 2.5 Pro and gpt-oss-120b
BenchmarkGemini 2.5 Progpt-oss-120bLeader
SAGE (Vals AI)41.9%Not measuredOnly Gemini 2.5 Pro measured
MedScribe (Vals AI)73.5%Not measuredOnly Gemini 2.5 Pro measured
IFBench48.7%69.0%gpt-oss-120b
Long context reasoning (AA-LCR)69.0%52.0%Gemini 2.5 Pro
DTBench82.4%76.3%Gemini 2.5 Pro
LMCA34.8%22.1%Gemini 2.5 Pro
Fiction.LiveBenchNot measured44.4%Only gpt-oss-120b measured
Creative writing (Lech Mazur)83.8%77.1%Gemini 2.5 Pro
Surface Evolver BenchNot measured25.0%Only gpt-oss-120b measured
LMArena Text14581366Gemini 2.5 Pro
LMArena Text: instruction following14371318Gemini 2.5 Pro
LMArena Text: creative writing14541274Gemini 2.5 Pro
LMArena Vision1263Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: English14581375Gemini 2.5 Pro
LMArena Text: non-English14521351Gemini 2.5 Pro
LMArena Text: Chinese15071385Gemini 2.5 Pro
LMArena Text: German14871353Gemini 2.5 Pro
LMArena Text: Japanese14611331Gemini 2.5 Pro
LMArena Text: Korean14341282Gemini 2.5 Pro
LMArena Text: French14731370Gemini 2.5 Pro
LMArena Text: Spanish14741389Gemini 2.5 Pro
LMArena Text: Russian14611343Gemini 2.5 Pro
LMArena Text: Polish14791353Gemini 2.5 Pro
LMArena Text: multi-turn14531340Gemini 2.5 Pro
LMArena Text: longer queries14491319Gemini 2.5 Pro
LMArena Text: writing and language14541306Gemini 2.5 Pro
LMArena Text: entertainment and media14411292Gemini 2.5 Pro
LMArena Vision: English1261Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: Chinese1300Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: reading text1270Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: humor1262Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: captioning1304Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: recognizing things1283Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: creative writing1265Not measuredOnly Gemini 2.5 Pro measured
LMArena Document1421Not measuredOnly Gemini 2.5 Pro 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 Pro and gpt-oss-120b
BenchmarkGemini 2.5 Progpt-oss-120bLeader
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$0.0063Fits in one request
gpt-oss-120b$0.0003Fits in one request

gpt-oss-120b costs less

Repository review

50K input + 3K output tokens

Gemini 2.5 Pro$0.092Fits in one request
gpt-oss-120b$0.0065Fits in one request

gpt-oss-120b costs less

Agent loop

200K input + 10K output tokens

Gemini 2.5 Pro$0.350Fits in one request
gpt-oss-120b$0.025Exceeds the context window

gpt-oss-120b 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 and gpt-oss-120b
SpecificationGoogle Gemini 2.5 ProOpenAI gpt-oss-120b
MakerGoogleOpenAI
WeightsNo public weights listedOpen weights
ReleasedJune 17, 2025August 5, 2025
Context window1.05M131K
Input $/Mtok$1.25$0.10
Output $/Mtok$10.00$0.50
Coding index19.1Not benchmarked
Independent measurements behind it31
Cost per solved task$0.64$0.51
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 2.5 Pro better than gpt-oss-120b for coding?

Only Gemini 2.5 Pro has a coding score, 19.1 on the Vibe Coding Index; gpt-oss-120b has none, so the two cannot be ranked on coding. Pick gpt-oss-120b for output tokens 20.0x cheaper ($0.50 vs $10.00 per million) and a lower cost per solved task ($0.51 against $0.64).

Which is cheaper, Gemini 2.5 Pro or gpt-oss-120b?

Gemini 2.5 Pro costs $1.25 per million input tokens and $10.00 per million output tokens; gpt-oss-120b costs $0.10 and $0.50. Per solved SWE-bench Verified task, Gemini 2.5 Pro costs $0.64 and gpt-oss-120b $0.51.

Which has the bigger context window, Gemini 2.5 Pro or gpt-oss-120b?

Gemini 2.5 Pro, with 1.05M tokens against 131K.

How many benchmarks is the Gemini 2.5 Pro vs gpt-oss-120b comparison based on?

63 benchmarks have a result for both models as of October 22, 2026. Gemini 2.5 Pro's coding index rests on 3 independent benchmarks and gpt-oss-120b's on 1. Where only one model has a result, the page says so instead of naming a winner.

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