GPT-5.2 vs Beam 1.0 501B A23B
Beam 1.0 501B A23B costs 11.7x less per output token than GPT-5.2 ($1.20 vs $14.00 per million). Updated October 22, 2026; 27 benchmarks have a result for both.
GPT-5.2 or Beam 1.0 501B A23B: which to pick
Only GPT-5.2 has a coding score, 55.9 on the Vibe Coding Index; Beam 1.0 501B A23B has none, so the two cannot be ranked on coding.
Pick Beam 1.0 501B A23B for output tokens 11.7x cheaper ($1.20 vs $14.00 per million) and a larger context window (1.05M against 400K tokens).
- Shared results
- 27
- GPT-5.2 only
- 87
- Beam 1.0 501B A23B only
- 1
- Like-for-like categories
- 6 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only GPT-5.2 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.2 has a AA Intelligence Index result, 30.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.2 has a GPQA Diamond result, 91.7%.
Directional only
Math
AIME
No call
Only GPT-5.2 has a AIME result, 96.9%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.2 has a MMLU Pro result, 86.2%.
Directional only
Web design
Design Arena: websites
Beam 1.0 501B A23B
Beam 1.0 501B A23B leads, 1245 against 1195.
Same board, both measured
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 $0.0088, Beam 1.0 501B A23B $0.0010 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2 has the number, $1.91.
Directional only
At a glance
- Coding index
- GPT-5.2 55.9 from 3 benchmarks, Beam 1.0 501B A23B not benchmarked.
- Widest category
- Web design, 4.7 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: medicine and healthcare: GPT-5.2 1425 against Beam 1.0 501B A23B 1376, 8.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2 $14.00 against Beam 1.0 501B A23B $1.20 per million output tokens, 11.7x apart.
- Context window
- GPT-5.2 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 | Bars | Beam 1.0 501B A23B | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 3 | ||
| Web design | 0 | 2 | ||
| General | 3 | 9 | ||
| All shared boards | 4 | 23 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | GPT-5.2 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GPT-5.2 measured |
| SWE-bench Multilingual | 66.7% | Not measured | Only GPT-5.2 measured |
| LMArena WebDev | 1417 | 1437 | Beam 1.0 501B A23B |
| LiveCodeBench (Vals AI) | 85.4% | Not measured | Only GPT-5.2 measured |
| SWE-bench Verified (Vals AI) | 75.8% | Not measured | Only GPT-5.2 measured |
| Vibe Code Bench (Vals AI) | 53.5% | Not measured | Only GPT-5.2 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GPT-5.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 47.0% | Not measured | Only GPT-5.2 measured |
| Terminal-Bench | 64.9% | Not measured | Only GPT-5.2 measured |
| GSO-Bench | 27.5% | Not measured | Only GPT-5.2 measured |
| WeirdML | 72.2% | Not measured | Only GPT-5.2 measured |
| ALE-Bench | 1293.5 | Not measured | Only GPT-5.2 measured |
| LMArena Text: coding | 1442 | 1473 | Beam 1.0 501B A23B |
| LMArena Text: software and IT | 1434 | 1461 | Beam 1.0 501B A23B |
| LMArena WebDev: React | Not measured | 1443 | Only Beam 1.0 501B A23B measured |
| LMArena WebDev: HTML | 1427 | 1436 | Beam 1.0 501B A23B |
| Neither model has a result on the other 23 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.2 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | Not measured | Only GPT-5.2 measured |
| Epoch Capabilities Index | 153.7 | Not measured | Only GPT-5.2 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | GPT-5.2 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1086 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building full-stack apps | 1043 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building mobile apps | 1093 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Android apps | 1060 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2 measured |
| τ²-Bench Telecom | 84.8% | Not measured | Only GPT-5.2 measured |
| METR time horizons | 75.3% | Not measured | Only GPT-5.2 measured |
| Remote Labor Index | 2.5% | Not measured | Only GPT-5.2 measured |
| GDPval | 49.7% | Not measured | Only GPT-5.2 measured |
| DeepResearch Bench | 41.1% | Not measured | Only GPT-5.2 measured |
| Vending-Bench 2 | $3,591 | Not measured | Only GPT-5.2 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 55.9% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 29 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-5.2 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | Not measured | Only GPT-5.2 measured |
| MMMU Pro (Vals AI) | 86.7% | Not measured | Only GPT-5.2 measured |
| GPQA Diamond (Artificial Analysis run) | 90.3% | Not measured | Only GPT-5.2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 37.7% | Not measured | Only GPT-5.2 measured |
| GPQA Diamond (Epoch AI run) | 91.4% | Not measured | Only GPT-5.2 measured |
| ARC-AGI | 86.2% | Not measured | Only GPT-5.2 measured |
| ARC-AGI-2 | 52.9% | Not measured | Only GPT-5.2 measured |
| SimpleBench | 45.8% | Not measured | Only GPT-5.2 measured |
| Chess puzzles | 48.0% | Not measured | Only GPT-5.2 measured |
| Mystery game puzzles | 23.0% | Not measured | Only GPT-5.2 measured |
| EBR-bench | 23.0% | Not measured | Only GPT-5.2 measured |
| VPCT | 84.0% | Not measured | Only GPT-5.2 measured |
| LMArena Text: hard prompts | 1428 | 1442 | Beam 1.0 501B A23B |
| LMArena Text: expert | 1445 | 1447 | Beam 1.0 501B A23B |
| LMArena Text: hard prompts in English | 1431 | 1451 | Beam 1.0 501B A23B |
| LMArena Vision: diagrams | 1265 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: homework | 1276 | Not measured | Only GPT-5.2 measured |
| Kagi LLM Benchmark | 73.3% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5.2 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AIME | 96.9% | Not measured | Only GPT-5.2 measured |
| MGSM (Vals AI) | 94.0% | Not measured | Only GPT-5.2 measured |
| AA Math Index | 99.0 | Not measured | Only GPT-5.2 measured |
| AIME 2025 (Artificial Analysis run) | 99.0% | Not measured | Only GPT-5.2 measured |
| OTIS Mock AIME | 96.1% | Not measured | Only GPT-5.2 measured |
| FrontierMath, tiers 1 to 3 | 67.4% | Not measured | Only GPT-5.2 measured |
| FrontierMath, tier 4 | 31.7% | Not measured | Only GPT-5.2 measured |
| ProofBench | 15.0% | Not measured | Only GPT-5.2 measured |
| LMArena Text: math | 1440 | 1459 | Beam 1.0 501B A23B |
| LMArena Text: mathematical work | 1442 | 1475 | Beam 1.0 501B A23B |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
| Benchmark | GPT-5.2 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| MMLU Pro | 86.2% | Not measured | Only GPT-5.2 measured |
| LegalBench (Vals AI) | 82.8% | Not measured | Only GPT-5.2 measured |
| CaseLaw (Vals AI) | 66.0% | Not measured | Only GPT-5.2 measured |
| CorpFin (Vals AI) | 65.9% | Not measured | Only GPT-5.2 measured |
| TaxEval (Vals AI) | 75.8% | Not measured | Only GPT-5.2 measured |
| MortgageTax (Vals AI) | 67.1% | Not measured | Only GPT-5.2 measured |
| MedQA (Vals AI) | 94.1% | Not measured | Only GPT-5.2 measured |
| MedCode (Vals AI) | 49.8% | Not measured | Only GPT-5.2 measured |
| MMLU Pro (Artificial Analysis run) | 87.4% | Not measured | Only GPT-5.2 measured |
| SimpleQA Verified | 37.1% | Not measured | Only GPT-5.2 measured |
| LMArena Text: science | 1425 | 1430 | Beam 1.0 501B A23B |
| LMArena Text: medicine and healthcare | 1425 | 1376 | GPT-5.2 |
| LMArena Text: legal and government | 1427 | 1433 | Beam 1.0 501B A23B |
| LMArena Text: business and finance | 1414 | 1430 | Beam 1.0 501B A23B |
| FACTS Benchmark Suite | 48.5% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5.2 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| Design Arena: websites | 1195 | 1245 | Beam 1.0 501B A23B |
| Design Arena: UI components | 1188 | Not measured | Only GPT-5.2 measured |
| Design Arena: game dev | 1195 | 1220 | Beam 1.0 501B A23B |
| Design Arena: data viz | 1202 | Not measured | Only GPT-5.2 measured |
| Design Arena: 3D | 1079 | Not measured | Only GPT-5.2 measured |
| Design Arena: all code categories | 1170 | Not measured | Only GPT-5.2 measured |
| Design Arena: SVG | 1139 | Not measured | Only GPT-5.2 measured |
| Design Arena: ASCII art | 1152 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index39
| Benchmark | GPT-5.2 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 49.3% | Not measured | Only GPT-5.2 measured |
| MedScribe (Vals AI) | 84.4% | Not measured | Only GPT-5.2 measured |
| IFBench | 75.4% | Not measured | Only GPT-5.2 measured |
| Long context reasoning (AA-LCR) | 82.7% | Not measured | Only GPT-5.2 measured |
| DTBench | 90.9% | Not measured | Only GPT-5.2 measured |
| LMCA | 43.9% | Not measured | Only GPT-5.2 measured |
| CL-bench | 18.2% | Not measured | Only GPT-5.2 measured |
| Furniture assembly | 38.3% | Not measured | Only GPT-5.2 measured |
| LMArena Text | 1416 | 1425 | Beam 1.0 501B A23B |
| LMArena Text: instruction following | 1409 | 1430 | Beam 1.0 501B A23B |
| LMArena Text: creative writing | 1376 | 1390 | Beam 1.0 501B A23B |
| LMArena Vision | 1246 | Not measured | Only GPT-5.2 measured |
| LMArena Text: English | 1422 | 1436 | Beam 1.0 501B A23B |
| LMArena Text: non-English | 1405 | 1413 | Beam 1.0 501B A23B |
| LMArena Text: Chinese | 1447 | 1424 | GPT-5.2 |
| LMArena Text: German | 1444 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Japanese | 1420 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Korean | 1374 | Not measured | Only GPT-5.2 measured |
| LMArena Text: French | 1454 | 1461 | Beam 1.0 501B A23B |
| LMArena Text: Spanish | 1413 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Russian | 1415 | 1412 | GPT-5.2 |
| LMArena Text: Polish | 1426 | Not measured | Only GPT-5.2 measured |
| LMArena Text: multi-turn | 1419 | 1414 | GPT-5.2 |
| LMArena Text: longer queries | 1413 | 1429 | Beam 1.0 501B A23B |
| LMArena Text: writing and language | 1392 | 1400 | Beam 1.0 501B A23B |
| LMArena Text: entertainment and media | 1378 | 1399 | Beam 1.0 501B A23B |
| LMArena Vision: English | 1243 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: Chinese | 1305 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: reading text | 1257 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: humor | 1227 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: captioning | 1267 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: recognizing things | 1232 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: creative writing | 1225 | Not measured | Only GPT-5.2 measured |
| LMArena Document | 1405 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2 | 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 | Reflection Beam 1.0 501B A23B |
|---|---|---|
| Maker | OpenAI | Reflection |
| Weights | No public weights listed | Open weights |
| Released | December 10, 2025 | October 16, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.75 | $0.40 |
| Output $/Mtok | $14.00 | $1.20 |
| Coding index | 55.9 | Not benchmarked |
| Independent measurements behind it | 3 | 1 |
| Cost per solved task | $1.91 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2 better than Beam 1.0 501B A23B for coding?
Only GPT-5.2 has a coding score, 55.9 on the Vibe Coding Index; Beam 1.0 501B A23B has none, so the two cannot be ranked on coding. Pick Beam 1.0 501B A23B for output tokens 11.7x cheaper ($1.20 vs $14.00 per million) and a larger context window (1.05M against 400K tokens).
Which is cheaper, GPT-5.2 or Beam 1.0 501B A23B?
GPT-5.2 costs $1.75 per million input tokens and $14.00 per million output tokens; Beam 1.0 501B A23B costs $0.40 and $1.20. Cost per solved task is known for GPT-5.2 only.
Which has the bigger context window, GPT-5.2 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 vs Beam 1.0 501B A23B comparison based on?
27 benchmarks have a result for both models as of October 22, 2026. GPT-5.2's coding index rests on 3 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.