GPT-5.1 vs Beam 1.0 501B A23B
Updated October 21, 2026. 28 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-5.1 for coding: it scores 53.3 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 the context window is larger, 1.05M against 400K tokens.
- Shared results
- 28
- GPT-5.1 only
- 72
- Beam 1.0 501B A23B only
- 0
- 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.
Coding
Vibe Coding Index, four public coding benchmarks
GPT-5.1
GPT-5.1 scores 53.3 against 33.8.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only GPT-5.1 has a AA Intelligence Index result, 24.7.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.1 has a GPQA Diamond result, 86.6%.
Directional only
Math
AIME
No call
Only GPT-5.1 has a AIME result, 93.3%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.1 has a MMLU Pro result, 86.4%.
Directional only
Web design
Design Arena: websites
Beam 1.0 501B A23B
Beam 1.0 501B A23B leads, 1253 against 1187.
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-5.1 $0.0063, 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-5.1 has the number, $1.02.
Directional only
At a glance
- Coding index
- GPT-5.1 53.3 from 2 benchmarks, Beam 1.0 501B A23B 33.8 from 1 benchmark.
- Widest category
- Coding, 7.6 points apart on average over 5 shared benchmarks.
- Widest single gap
- LMArena WebDev: React: GPT-5.1 1307 against Beam 1.0 501B A23B 1443, 22.7 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1 $10.00 against Beam 1.0 501B A23B $1.00 per million output tokens, 10.0x apart.
- Context window
- GPT-5.1 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-5.1 | Bars | Beam 1.0 501B A23B | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Reasoning | 3 | 0 | ||
| Math | 0 | 2 | ||
| Knowledge | 4 | 0 | ||
| Web design | 0 | 2 | ||
| General | 11 | 1 | ||
| All shared boards | 18 | 10 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index05
| Benchmark | GPT-5.1 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| SWE-bench Verified | 66.0% | Not measured | Only GPT-5.1 measured |
| LMArena WebDev | 1396 | 1437 | Beam 1.0 501B A23B |
| AA Coding Index | 49.4 | Not measured | Only GPT-5.1 measured |
| LiveCodeBench (Vals AI) | 86.5% | Not measured | Only GPT-5.1 measured |
| SWE-bench Verified (Vals AI) | 69.8% | Not measured | Only GPT-5.1 measured |
| Vibe Code Bench (Vals AI) | 24.6% | Not measured | Only GPT-5.1 measured |
| LiveCodeBench (Artificial Analysis run) | 86.8% | Not measured | Only GPT-5.1 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 45.5% | Not measured | Only GPT-5.1 measured |
| Terminal-Bench | 47.6% | Not measured | Only GPT-5.1 measured |
| GSO-Bench | 13.7% | Not measured | Only GPT-5.1 measured |
| WeirdML | 60.8% | Not measured | Only GPT-5.1 measured |
| LMArena Text: coding | 1453 | 1473 | Beam 1.0 501B A23B |
| LMArena Text: software and IT | 1454 | 1461 | Beam 1.0 501B A23B |
| LMArena WebDev: React | 1307 | 1443 | Beam 1.0 501B A23B |
| LMArena WebDev: HTML | 1405 | 1436 | Beam 1.0 501B A23B |
| LMArena WebDev: from an image | 1421 | Not measured | Only GPT-5.1 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
| Benchmark | GPT-5.1 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.7 | Not measured | Only GPT-5.1 measured |
| Epoch Capabilities Index | 149.8 | Not measured | Only GPT-5.1 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.1 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 81.9% | Not measured | Only GPT-5.1 measured |
| DeepResearch Bench | 42.8% | Not measured | Only GPT-5.1 measured |
| Vending-Bench 2 | $1,473 | Not measured | Only GPT-5.1 measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.1 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| GPQA Diamond | 86.6% | Not measured | Only GPT-5.1 measured |
| MMMU Pro (Vals AI) | 83.2% | Not measured | Only GPT-5.1 measured |
| GPQA Diamond (Artificial Analysis run) | 87.3% | Not measured | Only GPT-5.1 measured |
| Humanity's Last Exam (Artificial Analysis run) | 28.5% | Not measured | Only GPT-5.1 measured |
| GPQA Diamond (Epoch AI run) | 87.6% | Not measured | Only GPT-5.1 measured |
| ARC-AGI | 72.8% | Not measured | Only GPT-5.1 measured |
| ARC-AGI-2 | 17.6% | Not measured | Only GPT-5.1 measured |
| SimpleBench | 53.2% | Not measured | Only GPT-5.1 measured |
| Chess puzzles | 42.0% | Not measured | Only GPT-5.1 measured |
| Mystery game puzzles | 19.0% | Not measured | Only GPT-5.1 measured |
| VPCT | 58.7% | Not measured | Only GPT-5.1 measured |
| LMArena Text: hard prompts | 1457 | 1442 | GPT-5.1 |
| LMArena Text: expert | 1470 | 1447 | GPT-5.1 |
| LMArena Text: hard prompts in English | 1462 | 1451 | GPT-5.1 |
| LMArena Vision: diagrams | 1264 | Not measured | Only GPT-5.1 measured |
| LMArena Vision: homework | 1272 | Not measured | Only GPT-5.1 measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5.1 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AIME | 93.3% | Not measured | Only GPT-5.1 measured |
| MGSM (Vals AI) | 93.0% | Not measured | Only GPT-5.1 measured |
| AA Math Index | 94.0 | Not measured | Only GPT-5.1 measured |
| AIME 2025 (Artificial Analysis run) | 94.0% | Not measured | Only GPT-5.1 measured |
| OTIS Mock AIME | 88.6% | Not measured | Only GPT-5.1 measured |
| LMArena Text: math | 1446 | 1459 | Beam 1.0 501B A23B |
| LMArena Text: mathematical work | 1449 | 1475 | Beam 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
| Benchmark | GPT-5.1 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| MMLU Pro | 86.4% | Not measured | Only GPT-5.1 measured |
| LegalBench (Vals AI) | 85.7% | Not measured | Only GPT-5.1 measured |
| CaseLaw (Vals AI) | 73.4% | Not measured | Only GPT-5.1 measured |
| CorpFin (Vals AI) | 63.8% | Not measured | Only GPT-5.1 measured |
| TaxEval (Vals AI) | 74.9% | Not measured | Only GPT-5.1 measured |
| MortgageTax (Vals AI) | 61.4% | Not measured | Only GPT-5.1 measured |
| MedQA (Vals AI) | 96.4% | Not measured | Only GPT-5.1 measured |
| MedCode (Vals AI) | 52.7% | Not measured | Only GPT-5.1 measured |
| MMLU Pro (Artificial Analysis run) | 87.0% | Not measured | Only GPT-5.1 measured |
| SimpleQA Verified | 48.0% | Not measured | Only GPT-5.1 measured |
| LMArena Text: science | 1459 | 1430 | GPT-5.1 |
| LMArena Text: medicine and healthcare | 1456 | 1376 | GPT-5.1 |
| LMArena Text: legal and government | 1459 | 1433 | GPT-5.1 |
| LMArena Text: business and finance | 1431 | 1430 | GPT-5.1 |
| 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-5.1 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| Design Arena: websites | 1187 | 1253 | Beam 1.0 501B A23B |
| Design Arena: UI components | 1166 | Not measured | Only GPT-5.1 measured |
| Design Arena: game dev | 1179 | 1222 | Beam 1.0 501B A23B |
| Design Arena: data viz | 1204 | Not measured | Only GPT-5.1 measured |
| Design Arena: 3D | 1061 | Not measured | Only GPT-5.1 measured |
| Design Arena: all code categories | 1171 | Not measured | Only GPT-5.1 measured |
| Design Arena: SVG | 1149 | Not measured | Only GPT-5.1 measured |
| Design Arena: ASCII art | 1118 | Not measured | Only GPT-5.1 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index111
| Benchmark | GPT-5.1 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 43.2% | Not measured | Only GPT-5.1 measured |
| MedScribe (Vals AI) | 88.1% | Not measured | Only GPT-5.1 measured |
| IFBench | 72.9% | Not measured | Only GPT-5.1 measured |
| Long context reasoning (AA-LCR) | 80.0% | Not measured | Only GPT-5.1 measured |
| DTBench | 90.1% | Not measured | Only GPT-5.1 measured |
| LMCA | 43.9% | Not measured | Only GPT-5.1 measured |
| CL-bench | 23.7% | Not measured | Only GPT-5.1 measured |
| CL-bench Life | 17.3% | Not measured | Only GPT-5.1 measured |
| LMArena Text | 1443 | 1425 | GPT-5.1 |
| LMArena Text: instruction following | 1443 | 1430 | GPT-5.1 |
| LMArena Text: creative writing | 1427 | 1390 | GPT-5.1 |
| LMArena Vision | 1250 | Not measured | Only GPT-5.1 measured |
| LMArena Text: English | 1451 | 1436 | GPT-5.1 |
| LMArena Text: non-English | 1431 | 1413 | GPT-5.1 |
| LMArena Text: Chinese | 1495 | 1424 | GPT-5.1 |
| LMArena Text: German | 1438 | Not measured | Only GPT-5.1 measured |
| LMArena Text: Japanese | 1453 | Not measured | Only GPT-5.1 measured |
| LMArena Text: Korean | 1401 | Not measured | Only GPT-5.1 measured |
| LMArena Text: French | 1450 | 1461 | Beam 1.0 501B A23B |
| LMArena Text: Spanish | 1433 | Not measured | Only GPT-5.1 measured |
| LMArena Text: Russian | 1435 | 1412 | GPT-5.1 |
| LMArena Text: Polish | 1440 | Not measured | Only GPT-5.1 measured |
| LMArena Text: multi-turn | 1450 | 1414 | GPT-5.1 |
| LMArena Text: longer queries | 1448 | 1429 | GPT-5.1 |
| LMArena Text: writing and language | 1428 | 1400 | GPT-5.1 |
| LMArena Text: entertainment and media | 1415 | 1399 | GPT-5.1 |
| LMArena Vision: English | 1252 | Not measured | Only GPT-5.1 measured |
| LMArena Vision: Chinese | 1270 | Not measured | Only GPT-5.1 measured |
| LMArena Vision: reading text | 1255 | Not measured | Only GPT-5.1 measured |
| LMArena Vision: humor | 1263 | Not measured | Only GPT-5.1 measured |
| LMArena Vision: captioning | 1252 | Not measured | Only GPT-5.1 measured |
| LMArena Vision: recognizing things | 1232 | Not measured | Only GPT-5.1 measured |
| LMArena Vision: creative writing | 1266 | Not measured | Only GPT-5.1 measured |
| LMArena Document | 1403 | Not measured | Only GPT-5.1 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-5.1 | 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-5.1 | Reflection Beam 1.0 501B A23B |
|---|---|---|
| Maker | OpenAI | Reflection |
| Weights | No public weights listed | Open weights |
| Released | November 13, 2025 | October 16, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.25 | $0.30 |
| Output $/Mtok | $10.00 | $1.00 |
| Coding index | 53.3 | 33.8 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $1.02 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1 better than Beam 1.0 501B A23B for coding?
Pick GPT-5.1 for coding: it scores 53.3 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 the context window is larger, 1.05M against 400K tokens.
Which is cheaper, GPT-5.1 or Beam 1.0 501B A23B?
GPT-5.1 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 GPT-5.1 only.
Which has the bigger context window, GPT-5.1 or Beam 1.0 501B A23B?
Beam 1.0 501B A23B, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.1 vs Beam 1.0 501B A23B comparison based on?
28 benchmarks have a result for both models as of October 21, 2026. GPT-5.1'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.