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GPT-4o-mini (2024-07-18) vs Beam 1.0 501B A23B

Updated October 21, 2026. 23 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GPT-4o-mini (2024-07-18) Replace Beam 1.0 501B A23B
GPT-4o-mini (2024-07-18)OpenAI
Coding
3.6
Overall
No data
Output $/M
$0.60
0
R Beam 1.0 501B A23BReflection
Coding
33.8
Overall
No data
Output $/M
$1.00
23
vs boards won of 23 shared

Decision reading

Pick Beam 1.0 501B A23B for coding: it scores 33.8 on the Vibe Coding Index against 3.6 for GPT-4o-mini (2024-07-18).

Pick GPT-4o-mini (2024-07-18) when output tokens cost 1.7x less.

Model AGPT-4o-mini (2024-07-18)OpenAI3.6/1001 independent benchmarkNo cost per solved taskModel BBeam 1.0 501B A23BReflection33.8/1001 independent benchmarkNo cost per solved task
Shared results
23
GPT-4o-mini (2024-07-18) only
23
Beam 1.0 501B A23B only
5
Like-for-like categories
5 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
GPT-4o-mini (2024-07-18) 3.6 from 1 benchmark, Beam 1.0 501B A23B 33.8 from 1 benchmark.
Widest category
Math, 32.9 points apart on average over 2 shared benchmarks.
Widest single gap
LMArena Text: expert: GPT-4o-mini (2024-07-18) 1234 against Beam 1.0 501B A23B 1447, 35.4 points apart on a 0 to 100 scale.
Output price
GPT-4o-mini (2024-07-18) $0.60 against Beam 1.0 501B A23B $1.00 per million output tokens, 1.7x apart.
Context window
GPT-4o-mini (2024-07-18) takes 128K tokens, Beam 1.0 501B A23B 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
CategoryGPT-4o-mini (2024-07-18)BarsBeam 1.0 501B A23BTies
Coding02
Reasoning03
Math02
Knowledge04
General012
All shared boards023
CodingLike-for-likeFour of these boards feed the Vibe Coding Index02
Coding benchmarks for GPT-4o-mini (2024-07-18) and Beam 1.0 501B A23B
BenchmarkGPT-4o-mini (2024-07-18)Beam 1.0 501B A23BLeader
Aider polyglot3.6%Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena WebDevNot measured1437Only Beam 1.0 501B A23B measured
WeirdML11.8%Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena Text: coding12901473Beam 1.0 501B A23B
LMArena Text: software and IT12911461Beam 1.0 501B A23B
LMArena WebDev: ReactNot measured1443Only Beam 1.0 501B A23B measured
LMArena WebDev: HTMLNot measured1436Only Beam 1.0 501B A23B measured
Neither model has a result on the other 31 boards here
IntelligenceNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Intelligence benchmarks for GPT-4o-mini (2024-07-18) and Beam 1.0 501B A23B
BenchmarkGPT-4o-mini (2024-07-18)Beam 1.0 501B A23BLeader
Neither model has a result on 2 boards here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Agentic work benchmarks for GPT-4o-mini (2024-07-18) and Beam 1.0 501B A23B
BenchmarkGPT-4o-mini (2024-07-18)Beam 1.0 501B A23BLeader
BALROG17.4%Not measuredOnly GPT-4o-mini (2024-07-18) measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
Reasoning benchmarks for GPT-4o-mini (2024-07-18) and Beam 1.0 501B A23B
BenchmarkGPT-4o-mini (2024-07-18)Beam 1.0 501B A23BLeader
GPQA Diamond (Epoch AI run)37.7%Not measuredOnly GPT-4o-mini (2024-07-18) measured
SimpleBench10.7%Not measuredOnly GPT-4o-mini (2024-07-18) measured
Chess puzzles3.0%Not measuredOnly GPT-4o-mini (2024-07-18) measured
Mystery game puzzles12.0%Not measuredOnly GPT-4o-mini (2024-07-18) measured
VPCT34.0%Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena Text: hard prompts12671442Beam 1.0 501B A23B
LMArena Text: expert12341447Beam 1.0 501B A23B
LMArena Text: hard prompts in English12781451Beam 1.0 501B A23B
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GPT-4o-mini (2024-07-18) and Beam 1.0 501B A23B
BenchmarkGPT-4o-mini (2024-07-18)Beam 1.0 501B A23BLeader
OTIS Mock AIME6.9%Not measuredOnly GPT-4o-mini (2024-07-18) measured
FrontierMath, tiers 1 to 30.7%Not measuredOnly GPT-4o-mini (2024-07-18) measured
MATH level 552.6%Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena Text: math12671459Beam 1.0 501B A23B
LMArena Text: mathematical work12711475Beam 1.0 501B A23B
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GPT-4o-mini (2024-07-18) and Beam 1.0 501B A23B
BenchmarkGPT-4o-mini (2024-07-18)Beam 1.0 501B A23BLeader
SimpleQA Verified8.3%Not measuredOnly GPT-4o-mini (2024-07-18) measured
MMLU76.7%Not measuredOnly GPT-4o-mini (2024-07-18) measured
GeoBench64.0%Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena Text: science12861430Beam 1.0 501B A23B
LMArena Text: medicine and healthcare12821376Beam 1.0 501B A23B
LMArena Text: legal and government13051433Beam 1.0 501B A23B
LMArena Text: business and finance12651430Beam 1.0 501B A23B
Neither model has a result on the other 10 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Web design benchmarks for GPT-4o-mini (2024-07-18) and Beam 1.0 501B A23B
BenchmarkGPT-4o-mini (2024-07-18)Beam 1.0 501B A23BLeader
Design Arena: websitesNot measured1253Only Beam 1.0 501B A23B measured
Design Arena: game devNot measured1222Only Beam 1.0 501B A23B measured
Neither model has a result on the other 7 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index012
General benchmarks for GPT-4o-mini (2024-07-18) and Beam 1.0 501B A23B
BenchmarkGPT-4o-mini (2024-07-18)Beam 1.0 501B A23BLeader
Creative writing (Lech Mazur)67.2%Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena Text12861425Beam 1.0 501B A23B
LMArena Text: instruction following12581430Beam 1.0 501B A23B
LMArena Text: creative writing12681390Beam 1.0 501B A23B
LMArena Vision1066Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena Text: English13011436Beam 1.0 501B A23B
LMArena Text: non-English12661413Beam 1.0 501B A23B
LMArena Text: Chinese12651424Beam 1.0 501B A23B
LMArena Text: German1272Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena Text: Japanese1216Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena Text: Korean1195Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena Text: French12971461Beam 1.0 501B A23B
LMArena Text: Spanish1276Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena Text: Russian12751412Beam 1.0 501B A23B
LMArena Text: Polish1244Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena Text: multi-turn12851414Beam 1.0 501B A23B
LMArena Text: longer queries12891429Beam 1.0 501B A23B
LMArena Text: writing and language12821400Beam 1.0 501B A23B
LMArena Text: entertainment and media12531399Beam 1.0 501B A23B
LMArena Vision: English1072Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena Vision: Chinese1033Not measuredOnly GPT-4o-mini (2024-07-18) measured
Neither model has a result on the other 25 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4o-mini (2024-07-18) and Beam 1.0 501B A23B
BenchmarkGPT-4o-mini (2024-07-18)Beam 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

GPT-4o-mini (2024-07-18)$0.0004Fits in one request
Beam 1.0 501B A23B$0.0008Fits in one request

GPT-4o-mini (2024-07-18) costs less

Repository review

50K input + 3K output tokens

GPT-4o-mini (2024-07-18)$0.0093Fits in one request
Beam 1.0 501B A23B$0.018Fits in one request

GPT-4o-mini (2024-07-18) costs less

Agent loop

200K input + 10K output tokens

GPT-4o-mini (2024-07-18)$0.036Exceeds the context window
Beam 1.0 501B A23B$0.070Fits in one request

GPT-4o-mini (2024-07-18) 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 GPT-4o-mini (2024-07-18) and Beam 1.0 501B A23B
SpecificationOpenAI GPT-4o-mini (2024-07-18)Reflection Beam 1.0 501B A23B
MakerOpenAIReflection
WeightsNo public weights listedOpen weights
ReleasedJuly 18, 2024October 16, 2026
Context window128K1.05M
Input $/Mtok$0.15$0.30
Output $/Mtok$0.60$1.00
Coding index3.633.8
Independent measurements behind it11
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4o-mini (2024-07-18) better than Beam 1.0 501B A23B for coding?

Pick Beam 1.0 501B A23B for coding: it scores 33.8 on the Vibe Coding Index against 3.6 for GPT-4o-mini (2024-07-18). Pick GPT-4o-mini (2024-07-18) when output tokens cost 1.7x less.

Which is cheaper, GPT-4o-mini (2024-07-18) or Beam 1.0 501B A23B?

GPT-4o-mini (2024-07-18) costs $0.15 per million input tokens and $0.60 per million output tokens; Beam 1.0 501B A23B costs $0.30 and $1.00. Cost per solved task is known for neither of them.

Which has the bigger context window, GPT-4o-mini (2024-07-18) or Beam 1.0 501B A23B?

Beam 1.0 501B A23B, with 1.05M tokens against 128K.

How many benchmarks is the GPT-4o-mini (2024-07-18) vs Beam 1.0 501B A23B comparison based on?

23 benchmarks have a result for both models as of October 21, 2026. GPT-4o-mini (2024-07-18)'s coding index rests on 1 independent benchmark 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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