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Gemini 2.5 Pro vs Beam 1.0 501B A23B

Updated October 21, 2026. 27 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Gemini 2.5 Pro Replace Beam 1.0 501B A23B
Gemini 2.5 ProGoogle
Coding
37.0
Overall
16.1
Output $/M
$10.00
19
R Beam 1.0 501B A23BReflection
Coding
33.8
Overall
No data
Output $/M
$1.00
8
vs boards won of 27 shared

Decision reading

Pick Gemini 2.5 Pro for coding: it scores 37.0 on the Vibe Coding Index against 33.8 for Beam 1.0 501B A23B.

Pick Beam 1.0 501B A23B when output tokens cost 10.0x less, or when what matters is LMArena WebDev or LMArena Text: coding, where it leads.

Model AGemini 2.5 ProGoogle37.0/1002 independent benchmarks$0.64 per solved taskModel BBeam 1.0 501B A23BReflection33.8/1001 independent benchmarkNo cost per solved task
Shared results
27
Gemini 2.5 Pro only
60
Beam 1.0 501B A23B only
1
Like-for-like categories
6 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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 37.0 from 2 benchmarks, Beam 1.0 501B A23B 33.8 from 1 benchmark.
Widest category
Coding, 16.9 points apart on average over 4 shared benchmarks.
Widest single gap
LMArena WebDev: HTML: Gemini 2.5 Pro 1235 against Beam 1.0 501B A23B 1436, 33.6 points apart on a 0 to 100 scale.
Output price
Gemini 2.5 Pro $10.00 against Beam 1.0 501B A23B $1.00 per million output tokens, 10.0x apart.
Context window
Both take 1.05M tokens.

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 ProBarsBeam 1.0 501B A23BTies
Coding04
Reasoning30
Math02
Knowledge40
Web design02
General120
All shared boards198
CodingLike-for-likeFour of these boards feed the Vibe Coding Index04
Coding benchmarks for Gemini 2.5 Pro and Beam 1.0 501B A23B
BenchmarkGemini 2.5 ProBeam 1.0 501B A23BLeader
SWE-bench Verified53.6%Not measuredOnly Gemini 2.5 Pro measured
LMArena WebDev12261437Beam 1.0 501B A23B
AA Coding Index33.3Not measuredOnly Gemini 2.5 Pro measured
SWE-bench Verified (Vals AI)54.4%Not measuredOnly Gemini 2.5 Pro measured
Vibe Code Bench (Vals AI)0.4%Not measuredOnly Gemini 2.5 Pro measured
LiveCodeBench (Artificial Analysis run)80.1%Not measuredOnly Gemini 2.5 Pro measured
SciCode46.3%Not measuredOnly Gemini 2.5 Pro measured
Terminal-Bench Hard (Artificial Analysis run)26.5%Not measuredOnly Gemini 2.5 Pro measured
Terminal-Bench32.6%Not measuredOnly Gemini 2.5 Pro measured
GSO-Bench3.9%Not measuredOnly Gemini 2.5 Pro measured
WeirdML54.0%Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: coding14521473Beam 1.0 501B A23B
LMArena Text: software and IT14591461Beam 1.0 501B A23B
LMArena WebDev: ReactNot measured1443Only Beam 1.0 501B A23B measured
LMArena WebDev: HTML12351436Beam 1.0 501B A23B
LMArena WebDev: from an image1285Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 22 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for Gemini 2.5 Pro and Beam 1.0 501B A23B
BenchmarkGemini 2.5 ProBeam 1.0 501B A23BLeader
AA Intelligence Index16.1Not measuredOnly Gemini 2.5 Pro measured
Epoch Capabilities Index145.1Not measuredOnly Gemini 2.5 Pro measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
Agentic work benchmarks for Gemini 2.5 Pro and Beam 1.0 501B A23B
BenchmarkGemini 2.5 ProBeam 1.0 501B A23BLeader
AA Agentic Index1.6Not measuredOnly Gemini 2.5 Pro measured
τ²-Bench Telecom54.1%Not measuredOnly Gemini 2.5 Pro measured
GDPval23.3%Not measuredOnly Gemini 2.5 Pro measured
DeepResearch Bench41.5%Not measuredOnly Gemini 2.5 Pro measured
Vending-Bench 2$574Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
Reasoning benchmarks for Gemini 2.5 Pro and Beam 1.0 501B A23B
BenchmarkGemini 2.5 ProBeam 1.0 501B A23BLeader
GPQA Diamond (Artificial Analysis run)84.4%Not measuredOnly Gemini 2.5 Pro measured
Humanity's Last Exam (Artificial Analysis run)22.5%Not measuredOnly Gemini 2.5 Pro measured
GPQA Diamond (Epoch AI run)85.3%Not measuredOnly Gemini 2.5 Pro measured
ARC-AGI41.0%Not measuredOnly Gemini 2.5 Pro measured
ARC-AGI-24.9%Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: hard prompts14551442Gemini 2.5 Pro
LMArena Text: expert14521447Gemini 2.5 Pro
LMArena Text: hard prompts in English14531451Gemini 2.5 Pro
LMArena Vision: diagrams1274Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: homework1281Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Gemini 2.5 Pro and Beam 1.0 501B A23B
BenchmarkGemini 2.5 ProBeam 1.0 501B A23BLeader
AA Math Index87.7Not measuredOnly Gemini 2.5 Pro measured
AIME 2025 (Artificial Analysis run)87.7%Not measuredOnly Gemini 2.5 Pro measured
OTIS Mock AIME84.7%Not measuredOnly Gemini 2.5 Pro measured
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: math14501459Beam 1.0 501B A23B
LMArena Text: mathematical work14541475Beam 1.0 501B A23B
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for Gemini 2.5 Pro and Beam 1.0 501B A23B
BenchmarkGemini 2.5 ProBeam 1.0 501B A23BLeader
CaseLaw (Vals AI)63.9%Not measuredOnly Gemini 2.5 Pro measured
CorpFin (Vals AI)60.8%Not measuredOnly Gemini 2.5 Pro measured
MortgageTax (Vals AI)68.9%Not measuredOnly Gemini 2.5 Pro measured
MedCode (Vals AI)50.6%Not measuredOnly Gemini 2.5 Pro measured
MMLU Pro (Artificial Analysis run)86.2%Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: science14801430Gemini 2.5 Pro
LMArena Text: medicine and healthcare14761376Gemini 2.5 Pro
LMArena Text: legal and government14801433Gemini 2.5 Pro
LMArena Text: business and finance14391430Gemini 2.5 Pro
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index02
Web design benchmarks for Gemini 2.5 Pro and Beam 1.0 501B A23B
BenchmarkGemini 2.5 ProBeam 1.0 501B A23BLeader
Design Arena: websites11671253Beam 1.0 501B A23B
Design Arena: UI components1139Not measuredOnly Gemini 2.5 Pro measured
Design Arena: game dev11121222Beam 1.0 501B A23B
Design Arena: data viz1222Not measuredOnly Gemini 2.5 Pro measured
Design Arena: 3D1080Not measuredOnly Gemini 2.5 Pro measured
Design Arena: all code categories1152Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 3 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index120
General benchmarks for Gemini 2.5 Pro and Beam 1.0 501B A23B
BenchmarkGemini 2.5 ProBeam 1.0 501B A23BLeader
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%Not measuredOnly Gemini 2.5 Pro measured
Long context reasoning (AA-LCR)69.0%Not measuredOnly Gemini 2.5 Pro measured
DTBench82.4%Not measuredOnly Gemini 2.5 Pro measured
LMCA34.8%Not measuredOnly Gemini 2.5 Pro measured
Creative writing (Lech Mazur)83.8%Not measuredOnly Gemini 2.5 Pro measured
LMArena Text14581425Gemini 2.5 Pro
LMArena Text: instruction following14371430Gemini 2.5 Pro
LMArena Text: creative writing14541390Gemini 2.5 Pro
LMArena Vision1263Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: English14581436Gemini 2.5 Pro
LMArena Text: non-English14521413Gemini 2.5 Pro
LMArena Text: Chinese15071424Gemini 2.5 Pro
LMArena Text: German1487Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: Japanese1461Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: Korean1434Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: French14731461Gemini 2.5 Pro
LMArena Text: Spanish1474Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: Russian14611412Gemini 2.5 Pro
LMArena Text: Polish1479Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: multi-turn14531414Gemini 2.5 Pro
LMArena Text: longer queries14491429Gemini 2.5 Pro
LMArena Text: writing and language14541400Gemini 2.5 Pro
LMArena Text: entertainment and media14411399Gemini 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 13 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 Beam 1.0 501B A23B
BenchmarkGemini 2.5 ProBeam 1.0 501B A23BLeader
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
Beam 1.0 501B A23B$0.0008Fits in one request

Beam 1.0 501B A23B costs less

Repository review

50K input + 3K output tokens

Gemini 2.5 Pro$0.092Fits in one request
Beam 1.0 501B A23B$0.018Fits in one request

Beam 1.0 501B A23B costs less

Agent loop

200K input + 10K output tokens

Gemini 2.5 Pro$0.350Fits in one request
Beam 1.0 501B A23B$0.070Fits in one request

Beam 1.0 501B A23B 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 Beam 1.0 501B A23B
SpecificationGoogle Gemini 2.5 ProReflection Beam 1.0 501B A23B
MakerGoogleReflection
WeightsNo public weights listedOpen weights
ReleasedJune 17, 2025October 16, 2026
Context window1.05M1.05M
Input $/Mtok$1.25$0.30
Output $/Mtok$10.00$1.00
Coding index37.033.8
Independent measurements behind it21
Cost per solved task$0.64Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 2.5 Pro better than Beam 1.0 501B A23B for coding?

Pick Gemini 2.5 Pro for coding: it scores 37.0 on the Vibe Coding Index against 33.8 for Beam 1.0 501B A23B. Pick Beam 1.0 501B A23B when output tokens cost 10.0x less, or when what matters is LMArena WebDev or LMArena Text: coding, where it leads.

Which is cheaper, Gemini 2.5 Pro or Beam 1.0 501B A23B?

Gemini 2.5 Pro costs $1.25 per million input tokens and $10.00 per million output tokens; Beam 1.0 501B A23B costs $0.30 and $1.00. Cost per solved task is known for Gemini 2.5 Pro only.

Which has the bigger context window, Gemini 2.5 Pro or Beam 1.0 501B A23B?

Neither: both take 1.05M tokens.

How many benchmarks is the Gemini 2.5 Pro vs Beam 1.0 501B A23B comparison based on?

27 benchmarks have a result for both models as of October 21, 2026. Gemini 2.5 Pro's coding index rests on 2 independent benchmarks and Beam 1.0 501B A23B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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