GPT-5 Nano vs Beam 1.0 501B A23B
Updated October 21, 2026. 24 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
GPT-5 Nano and Beam 1.0 501B A23B are level for coding, 34.8 against 33.8 on the Vibe Coding Index, so price and context decide.
Pick Beam 1.0 501B A23B when the context window is larger, 1.05M against 400K tokens, or when what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads. Pick GPT-5 Nano when output tokens cost 2.5x less.
- Shared results
- 24
- GPT-5 Nano only
- 61
- Beam 1.0 501B A23B only
- 4
- 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
No call
Level: 34.8 against 33.8, inside 2 points.
One benchmark behind each score
Intelligence
AA Intelligence Index
No call
Only GPT-5 Nano has a AA Intelligence Index result, 13.0.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5 Nano has a GPQA Diamond result, 63.4%.
Directional only
Math
AIME
No call
Only GPT-5 Nano has a AIME result, 81.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5 Nano has a MMLU Pro result, 76.1%.
Directional only
Web design
Design Arena: websites
Beam 1.0 501B A23B
Beam 1.0 501B A23B leads, 1253 against 1101.
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
GPT-5 Nano
GPT-5 Nano $0.0003, 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 Nano has the number, $0.109.
Directional only
At a glance
- Coding index
- GPT-5 Nano 34.8 from 1 benchmark, Beam 1.0 501B A23B 33.8 from 1 benchmark.
- Widest category
- Math, 24.2 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: mathematical work: GPT-5 Nano 1326 against Beam 1.0 501B A23B 1475, 24.9 points apart on a 0 to 100 scale.
- Output price
- GPT-5 Nano $0.40 against Beam 1.0 501B A23B $1.00 per million output tokens, 2.5x apart.
- Context window
- GPT-5 Nano 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 Nano | Bars | Beam 1.0 501B A23B | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 2 | ||
| General | 0 | 11 | ||
| All shared boards | 0 | 24 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index02
| Benchmark | GPT-5 Nano | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| SWE-bench Verified | 34.8% | Not measured | Only GPT-5 Nano measured |
| LMArena WebDev | Not measured | 1437 | Only Beam 1.0 501B A23B measured |
| LiveCodeBench (Vals AI) | 70.2% | Not measured | Only GPT-5 Nano measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | Not measured | Only GPT-5 Nano measured |
| Terminal-Bench Hard (Artificial Analysis run) | 17.4% | Not measured | Only GPT-5 Nano measured |
| Terminal-Bench | 21.8% | Not measured | Only GPT-5 Nano measured |
| WeirdML | 38.1% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: coding | 1351 | 1473 | Beam 1.0 501B A23B |
| LMArena Text: software and IT | 1345 | 1461 | Beam 1.0 501B A23B |
| LMArena WebDev: React | Not measured | 1443 | Only Beam 1.0 501B A23B measured |
| LMArena WebDev: HTML | Not measured | 1436 | Only Beam 1.0 501B A23B measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5 Nano | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.0 | Not measured | Only GPT-5 Nano measured |
| Epoch Capabilities Index | 139.2 | Not measured | Only GPT-5 Nano measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5 Nano | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 36.5% | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-5 Nano | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| GPQA Diamond | 63.4% | Not measured | Only GPT-5 Nano measured |
| MMMU Pro (Vals AI) | 70.9% | Not measured | Only GPT-5 Nano measured |
| GPQA Diamond (Artificial Analysis run) | 67.6% | Not measured | Only GPT-5 Nano measured |
| Humanity's Last Exam (Artificial Analysis run) | 9.5% | Not measured | Only GPT-5 Nano measured |
| GPQA Diamond (Epoch AI run) | 69.4% | Not measured | Only GPT-5 Nano measured |
| ARC-AGI | 20.7% | Not measured | Only GPT-5 Nano measured |
| ARC-AGI-2 | 2.6% | Not measured | Only GPT-5 Nano measured |
| Chess puzzles | 22.0% | Not measured | Only GPT-5 Nano measured |
| Mystery game puzzles | 9.0% | Not measured | Only GPT-5 Nano measured |
| VPCT | 37.2% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: hard prompts | 1328 | 1442 | Beam 1.0 501B A23B |
| LMArena Text: expert | 1321 | 1447 | Beam 1.0 501B A23B |
| LMArena Text: hard prompts in English | 1338 | 1451 | Beam 1.0 501B A23B |
| LMArena Vision: diagrams | 1141 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: homework | 1157 | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5 Nano | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AIME | 81.2% | Not measured | Only GPT-5 Nano measured |
| MATH 500 (Vals AI) | 93.8% | Not measured | Only GPT-5 Nano measured |
| MGSM (Vals AI) | 89.3% | Not measured | Only GPT-5 Nano measured |
| AA Math Index | 83.7 | Not measured | Only GPT-5 Nano measured |
| AIME 2025 (Artificial Analysis run) | 83.7% | Not measured | Only GPT-5 Nano measured |
| OTIS Mock AIME | 81.1% | Not measured | Only GPT-5 Nano measured |
| FrontierMath, tiers 1 to 3 | 20.0% | Not measured | Only GPT-5 Nano measured |
| FrontierMath, tier 4 | 2.4% | Not measured | Only GPT-5 Nano measured |
| ProofBench | 12.0% | Not measured | Only GPT-5 Nano measured |
| MATH level 5 | 95.2% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: math | 1317 | 1459 | Beam 1.0 501B A23B |
| LMArena Text: mathematical work | 1326 | 1475 | Beam 1.0 501B A23B |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-5 Nano | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| MMLU Pro | 76.1% | Not measured | Only GPT-5 Nano measured |
| LegalBench (Vals AI) | 50.1% | Not measured | Only GPT-5 Nano measured |
| CaseLaw (Vals AI) | 52.6% | Not measured | Only GPT-5 Nano measured |
| TaxEval (Vals AI) | 67.4% | Not measured | Only GPT-5 Nano measured |
| MortgageTax (Vals AI) | 53.6% | Not measured | Only GPT-5 Nano measured |
| MedQA (Vals AI) | 93.3% | Not measured | Only GPT-5 Nano measured |
| MedCode (Vals AI) | 30.4% | Not measured | Only GPT-5 Nano measured |
| MMLU Pro (Artificial Analysis run) | 78.0% | Not measured | Only GPT-5 Nano measured |
| SimpleQA Verified | 11.7% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: science | 1318 | 1430 | Beam 1.0 501B A23B |
| LMArena Text: medicine and healthcare | 1335 | 1376 | Beam 1.0 501B A23B |
| LMArena Text: legal and government | 1298 | 1433 | Beam 1.0 501B A23B |
| LMArena Text: business and finance | 1311 | 1430 | Beam 1.0 501B A23B |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5 Nano | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| Design Arena: websites | 1101 | 1253 | Beam 1.0 501B A23B |
| Design Arena: UI components | 1066 | Not measured | Only GPT-5 Nano measured |
| Design Arena: game dev | 1045 | 1222 | Beam 1.0 501B A23B |
| Design Arena: data viz | 1056 | Not measured | Only GPT-5 Nano measured |
| Design Arena: 3D | 966 | Not measured | Only GPT-5 Nano measured |
| Design Arena: all code categories | 1084 | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index011
| Benchmark | GPT-5 Nano | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 30.4% | Not measured | Only GPT-5 Nano measured |
| MedScribe (Vals AI) | 72.9% | Not measured | Only GPT-5 Nano measured |
| IFBench | 67.6% | Not measured | Only GPT-5 Nano measured |
| Long context reasoning (AA-LCR) | 45.0% | Not measured | Only GPT-5 Nano measured |
| DTBench | 62.7% | Not measured | Only GPT-5 Nano measured |
| LMCA | 7.9% | Not measured | Only GPT-5 Nano measured |
| Fiction.LiveBench | 44.4% | Not measured | Only GPT-5 Nano measured |
| LMArena Text | 1320 | 1425 | Beam 1.0 501B A23B |
| LMArena Text: instruction following | 1306 | 1430 | Beam 1.0 501B A23B |
| LMArena Text: creative writing | 1249 | 1390 | Beam 1.0 501B A23B |
| LMArena Vision | 1159 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: English | 1321 | 1436 | Beam 1.0 501B A23B |
| LMArena Text: non-English | 1313 | 1413 | Beam 1.0 501B A23B |
| LMArena Text: Chinese | 1356 | 1424 | Beam 1.0 501B A23B |
| LMArena Text: German | 1327 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: Japanese | 1227 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: Korean | 1269 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: French | Not measured | 1461 | Only Beam 1.0 501B A23B measured |
| LMArena Text: Spanish | 1360 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: Russian | 1296 | 1412 | Beam 1.0 501B A23B |
| LMArena Text: Polish | 1314 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: multi-turn | 1311 | 1414 | Beam 1.0 501B A23B |
| LMArena Text: longer queries | 1312 | 1429 | Beam 1.0 501B A23B |
| LMArena Text: writing and language | 1283 | 1400 | Beam 1.0 501B A23B |
| LMArena Text: entertainment and media | 1268 | 1399 | Beam 1.0 501B A23B |
| LMArena Vision: English | 1170 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: Chinese | 1176 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: reading text | 1152 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: humor | 1110 | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5 Nano | 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
GPT-5 Nano costs less
Repository review
50K input + 3K output tokens
GPT-5 Nano costs less
Agent loop
200K input + 10K output tokens
GPT-5 Nano 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 Nano | Reflection Beam 1.0 501B A23B |
|---|---|---|
| Maker | OpenAI | Reflection |
| Weights | No public weights listed | Open weights |
| Released | August 7, 2025 | October 16, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $0.05 | $0.30 |
| Output $/Mtok | $0.40 | $1.00 |
| Coding index | 34.8 | 33.8 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $0.109 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5 Nano better than Beam 1.0 501B A23B for coding?
GPT-5 Nano and Beam 1.0 501B A23B are level for coding, 34.8 against 33.8 on the Vibe Coding Index, so price and context decide. Pick Beam 1.0 501B A23B when the context window is larger, 1.05M against 400K tokens, or when what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads. Pick GPT-5 Nano when output tokens cost 2.5x less.
Which is cheaper, GPT-5 Nano or Beam 1.0 501B A23B?
GPT-5 Nano costs $0.05 per million input tokens and $0.40 per million output tokens; Beam 1.0 501B A23B costs $0.30 and $1.00. Cost per solved task is known for GPT-5 Nano only.
Which has the bigger context window, GPT-5 Nano or Beam 1.0 501B A23B?
Beam 1.0 501B A23B, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5 Nano vs Beam 1.0 501B A23B comparison based on?
24 benchmarks have a result for both models as of October 21, 2026. GPT-5 Nano'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.