GPT-5.3-Codex vs Beam 1.0 501B A23B
Beam 1.0 501B A23B costs 12.7x less per output token than GPT-5.3-Codex ($1.10 vs $14.00 per million). Updated October 21, 2026; 5 benchmarks have a result for both.
GPT-5.3-Codex or Beam 1.0 501B A23B: which to pick
Only GPT-5.3-Codex has a coding score, 60.0 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 12.7x cheaper ($1.10 vs $14.00 per million) and a larger context window (1.05M against 400K tokens).
- Shared results
- 5
- GPT-5.3-Codex only
- 29
- Beam 1.0 501B A23B only
- 23
- Like-for-like categories
- 2 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-5.3-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.3-Codex has a AA Intelligence Index result, 32.5.
Directional only
Web design
Design Arena: websites
Beam 1.0 501B A23B
Beam 1.0 501B A23B leads, 1253 against 1163.
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-5.3-Codex $0.0088, Beam 1.0 501B A23B $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.3-Codex has the number, $0.59.
Directional only
At a glance
- Coding index
- GPT-5.3-Codex 60.0 from 3 benchmarks, Beam 1.0 501B A23B not benchmarked.
- Widest category
- Web design, 10.1 points apart on average over 2 shared benchmarks.
- Widest single gap
- Design Arena: websites: GPT-5.3-Codex 1163 against Beam 1.0 501B A23B 1253, 11.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5.3-Codex $14.00 against Beam 1.0 501B A23B $1.10 per million output tokens, 12.7x apart.
- Context window
- GPT-5.3-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-5.3-Codex | Bars | Beam 1.0 501B A23B | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Web design | 0 | 2 | ||
| All shared boards | 0 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | GPT-5.3-Codex | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| LMArena WebDev | 1407 | 1437 | Beam 1.0 501B A23B |
| LiveCodeBench (Vals AI) | 87.3% | Not measured | Only GPT-5.3-Codex measured |
| SWE-bench Verified (Vals AI) | 78.0% | Not measured | Only GPT-5.3-Codex measured |
| Vibe Code Bench (Vals AI) | 61.8% | Not measured | Only GPT-5.3-Codex measured |
| IOI (Vals AI) | 53.8% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | 53.0% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench | 78.4% | Not measured | Only GPT-5.3-Codex measured |
| WeirdML | 79.3% | Not measured | Only GPT-5.3-Codex measured |
| ALE-Bench | 1655.2 | Not measured | Only GPT-5.3-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 | 1395 | 1443 | Beam 1.0 501B A23B |
| LMArena WebDev: HTML | 1422 | 1436 | Beam 1.0 501B A23B |
| LMArena WebDev: from an image | 1443 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.3-Codex | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AA Intelligence Index | 32.5 | Not measured | Only GPT-5.3-Codex measured |
| Epoch Capabilities Index | 157.1 | Not measured | Only GPT-5.3-Codex measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.3-Codex | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1032 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | 983 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | 1054 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | 1124 | Not measured | Only GPT-5.3-Codex measured |
| τ²-Bench Telecom | 86.0% | Not measured | Only GPT-5.3-Codex measured |
| METR time horizons | 74.5% | Not measured | Only GPT-5.3-Codex measured |
| Vending-Bench 2 | $5,940 | Not measured | Only GPT-5.3-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-5.3-Codex | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 91.5% | Not measured | Only GPT-5.3-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | 42.5% | Not measured | Only GPT-5.3-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 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.3-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-5.3-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 14 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5.3-Codex | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| Design Arena: websites | 1163 | 1253 | Beam 1.0 501B A23B |
| Design Arena: UI components | 1137 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: game dev | 1161 | 1233 | Beam 1.0 501B A23B |
| Design Arena: data viz | 1164 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: 3D | 1008 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: all code categories | 1145 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: SVG | 1129 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: ASCII art | 1152 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | GPT-5.3-Codex | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| IFBench | 75.4% | Not measured | Only GPT-5.3-Codex measured |
| Long context reasoning (AA-LCR) | 83.3% | Not measured | Only GPT-5.3-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-5.3-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-5.3-Codex | Reflection Beam 1.0 501B A23B |
|---|---|---|
| Maker | OpenAI | Reflection |
| Weights | No public weights listed | Open weights |
| Released | February 24, 2026 | October 16, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.75 | $0.35 |
| Output $/Mtok | $14.00 | $1.10 |
| Coding index | 60.0 | Not benchmarked |
| Independent measurements behind it | 3 | 1 |
| Cost per solved task | $0.59 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.3-Codex better than Beam 1.0 501B A23B for coding?
Only GPT-5.3-Codex has a coding score, 60.0 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 12.7x cheaper ($1.10 vs $14.00 per million) and a larger context window (1.05M against 400K tokens).
Which is cheaper, GPT-5.3-Codex or Beam 1.0 501B A23B?
GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Beam 1.0 501B A23B costs $0.35 and $1.10. Cost per solved task is known for GPT-5.3-Codex only.
Which has the bigger context window, GPT-5.3-Codex or Beam 1.0 501B A23B?
Beam 1.0 501B A23B, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.3-Codex vs Beam 1.0 501B A23B comparison based on?
5 benchmarks have a result for both models as of October 21, 2026. GPT-5.3-Codex'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.