GPT-5.2-Codex vs Beam 1.0 501B A23B
Updated October 21, 2026. 3 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-5.2-Codex for coding: it scores 55.1 on the Vibe Coding Index against 33.8 for Beam 1.0 501B A23B.
Pick Beam 1.0 501B A23B when output tokens cost 14.0x less, or when the context window is larger, 1.05M against 400K tokens.
- Shared results
- 3
- GPT-5.2-Codex only
- 19
- Beam 1.0 501B A23B only
- 25
- Like-for-like categories
- 1 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.
Coding
Vibe Coding Index, four public coding benchmarks
GPT-5.2-Codex
GPT-5.2-Codex scores 55.1 against 33.8.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only GPT-5.2-Codex has a AA Intelligence Index result, 28.5.
Directional only
Web design
Design Arena: websites
No call
Only Beam 1.0 501B A23B has a Design Arena: websites result, 1253.
Directional only
Long documents
Listed context window
Beam 1.0 501B A23B
Beam 1.0 501B A23B takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Beam 1.0 501B A23B
GPT-5.2-Codex $0.0088, Beam 1.0 501B A23B $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2-Codex has the number, $0.43.
Directional only
At a glance
- Coding index
- GPT-5.2-Codex 55.1 from 2 benchmarks, Beam 1.0 501B A23B 33.8 from 1 benchmark.
- Widest category
- Coding, 15.8 points apart on average over 3 shared benchmarks.
- Widest single gap
- LMArena WebDev: React: GPT-5.2-Codex 1329 against Beam 1.0 501B A23B 1443, 18.9 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2-Codex $14.00 against Beam 1.0 501B A23B $1.00 per million output tokens, 14.0x apart.
- Context window
- GPT-5.2-Codex takes 400K 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.
| Category | GPT-5.2-Codex | Bars | Beam 1.0 501B A23B | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| All shared boards | 0 | 3 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index03
| Benchmark | GPT-5.2-Codex | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Multilingual | 66.3% | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev | 1339 | 1437 | Beam 1.0 501B A23B |
| LiveCodeBench (Vals AI) | 88.0% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Verified (Vals AI) | 72.4% | Not measured | Only GPT-5.2-Codex measured |
| Vibe Code Bench (Vals AI) | 37.9% | Not measured | Only GPT-5.2-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | Not measured | Only GPT-5.2-Codex measured |
| Terminal-Bench | 66.5% | Not measured | Only GPT-5.2-Codex measured |
| ALE-Bench | 1299.9 | Not measured | Only GPT-5.2-Codex measured |
| LMArena Text: coding | Not measured | 1473 | Only Beam 1.0 501B A23B measured |
| LMArena Text: software and IT | Not measured | 1461 | Only Beam 1.0 501B A23B measured |
| LMArena WebDev: React | 1329 | 1443 | Beam 1.0 501B A23B |
| LMArena WebDev: HTML | 1348 | 1436 | Beam 1.0 501B A23B |
| Neither model has a result on the other 25 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.2-Codex | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AA Intelligence Index | 28.5 | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.2-Codex | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1051 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building full-stack apps | 993 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building mobile apps | 1090 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Android apps | 1176 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2-Codex measured |
| τ²-Bench Telecom | 92.1% | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.2-Codex | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 89.9% | Not measured | Only GPT-5.2-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | 35.7% | Not measured | Only GPT-5.2-Codex measured |
| LMArena Text: hard prompts | Not measured | 1442 | Only Beam 1.0 501B A23B measured |
| LMArena Text: expert | Not measured | 1447 | Only Beam 1.0 501B A23B measured |
| LMArena Text: hard prompts in English | Not measured | 1451 | Only Beam 1.0 501B A23B measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.2-Codex | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| LMArena Text: math | Not measured | 1459 | Only Beam 1.0 501B A23B measured |
| LMArena Text: mathematical work | Not measured | 1475 | Only Beam 1.0 501B A23B measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.2-Codex | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| LMArena Text: science | Not measured | 1430 | Only Beam 1.0 501B A23B measured |
| LMArena Text: medicine and healthcare | Not measured | 1376 | Only Beam 1.0 501B A23B measured |
| LMArena Text: legal and government | Not measured | 1433 | Only Beam 1.0 501B A23B measured |
| LMArena Text: business and finance | Not measured | 1430 | Only Beam 1.0 501B A23B measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.2-Codex | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1253 | Only Beam 1.0 501B A23B measured |
| Design Arena: game dev | Not measured | 1222 | Only Beam 1.0 501B A23B measured |
| Neither model has a result on the other 7 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | GPT-5.2-Codex | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| IFBench | 77.6% | Not measured | Only GPT-5.2-Codex measured |
| Long context reasoning (AA-LCR) | 82.3% | Not measured | Only GPT-5.2-Codex measured |
| LMArena Text | Not measured | 1425 | Only Beam 1.0 501B A23B measured |
| LMArena Text: instruction following | Not measured | 1430 | Only Beam 1.0 501B A23B measured |
| LMArena Text: creative writing | Not measured | 1390 | Only Beam 1.0 501B A23B measured |
| LMArena Text: English | Not measured | 1436 | Only Beam 1.0 501B A23B measured |
| LMArena Text: non-English | Not measured | 1413 | Only Beam 1.0 501B A23B measured |
| LMArena Text: Chinese | Not measured | 1424 | Only Beam 1.0 501B A23B measured |
| LMArena Text: French | Not measured | 1461 | Only Beam 1.0 501B A23B measured |
| LMArena Text: Russian | Not measured | 1412 | Only Beam 1.0 501B A23B measured |
| LMArena Text: multi-turn | Not measured | 1414 | Only Beam 1.0 501B A23B measured |
| LMArena Text: longer queries | Not measured | 1429 | Only Beam 1.0 501B A23B measured |
| LMArena Text: writing and language | Not measured | 1400 | Only Beam 1.0 501B A23B measured |
| LMArena Text: entertainment and media | Not measured | 1399 | Only Beam 1.0 501B A23B measured |
| Neither model has a result on the other 32 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| 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
Beam 1.0 501B A23B costs less
Repository review
50K input + 3K output tokens
Beam 1.0 501B A23B costs less
Agent loop
200K input + 10K output tokens
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.
| Specification | OpenAI GPT-5.2-Codex | Reflection Beam 1.0 501B A23B |
|---|---|---|
| Maker | OpenAI | Reflection |
| Weights | No public weights listed | Open weights |
| Released | January 14, 2026 | October 16, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.75 | $0.30 |
| Output $/Mtok | $14.00 | $1.00 |
| Coding index | 55.1 | 33.8 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.43 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2-Codex better than Beam 1.0 501B A23B for coding?
Pick GPT-5.2-Codex for coding: it scores 55.1 on the Vibe Coding Index against 33.8 for Beam 1.0 501B A23B. Pick Beam 1.0 501B A23B when output tokens cost 14.0x less, or when the context window is larger, 1.05M against 400K tokens.
Which is cheaper, GPT-5.2-Codex or Beam 1.0 501B A23B?
GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Beam 1.0 501B A23B costs $0.30 and $1.00. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, GPT-5.2-Codex or Beam 1.0 501B A23B?
Beam 1.0 501B A23B, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.2-Codex vs Beam 1.0 501B A23B comparison based on?
3 benchmarks have a result for both models as of October 21, 2026. GPT-5.2-Codex'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.