DeepSeek V3.2 Exp vs Beam 1.0 501B A23B
Updated October 21, 2026. 27 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick DeepSeek V3.2 Exp for coding: it scores 55.0 on the Vibe Coding Index against 33.8 for Beam 1.0 501B A23B.
Pick Beam 1.0 501B A23B when the context window is larger, 1.05M against 164K tokens, or when what matters is LMArena WebDev or LMArena Text: coding, where it leads.
- Shared results
- 27
- DeepSeek V3.2 Exp only
- 30
- Beam 1.0 501B A23B only
- 1
- 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
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp scores 55.0 against 33.8.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only DeepSeek V3.2 Exp has a AA Intelligence Index result, 16.6.
Directional only
Web design
Design Arena: websites
Beam 1.0 501B A23B
Beam 1.0 501B A23B leads, 1253 against 1179.
Same board, both measured
Long documents
Listed context window
Beam 1.0 501B A23B
Beam 1.0 501B A23B takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp $0.0005, Beam 1.0 501B A23B $0.0008 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.2 Exp 55.0 from 2 benchmarks, Beam 1.0 501B A23B 33.8 from 1 benchmark.
- Widest category
- Coding, 14.7 points apart on average over 4 shared benchmarks.
- Widest single gap
- LMArena WebDev: HTML: DeepSeek V3.2 Exp 1281 against Beam 1.0 501B A23B 1436, 25.9 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against Beam 1.0 501B A23B $1.00 per million output tokens, 2.4x apart.
- Context window
- DeepSeek V3.2 Exp takes 164K 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 | DeepSeek V3.2 Exp | Bars | Beam 1.0 501B A23B | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 2 | 2 | ||
| Web design | 0 | 2 | ||
| General | 6 | 6 | ||
| All shared boards | 8 | 19 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index04
| Benchmark | DeepSeek V3.2 Exp | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena WebDev | 1272 | 1437 | Beam 1.0 501B A23B |
| LiveCodeBench (Artificial Analysis run) | 78.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| WeirdML | 39.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: coding | 1438 | 1473 | Beam 1.0 501B A23B |
| LMArena Text: software and IT | 1438 | 1461 | Beam 1.0 501B A23B |
| LMArena WebDev: React | Not measured | 1443 | Only Beam 1.0 501B A23B measured |
| LMArena WebDev: HTML | 1281 | 1436 | Beam 1.0 501B A23B |
| Neither model has a result on the other 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | Not measured | Only DeepSeek V3.2 Exp measured |
| Epoch Capabilities Index | 144.9 | Not measured | Only DeepSeek V3.2 Exp measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 33.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | DeepSeek V3.2 Exp | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: hard prompts | 1429 | 1442 | Beam 1.0 501B A23B |
| LMArena Text: expert | 1422 | 1447 | Beam 1.0 501B A23B |
| LMArena Text: hard prompts in English | 1444 | 1451 | Beam 1.0 501B A23B |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 Exp | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | Not measured | Only DeepSeek V3.2 Exp measured |
| AIME 2025 (Artificial Analysis run) | 87.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: math | 1422 | 1459 | Beam 1.0 501B A23B |
| LMArena Text: mathematical work | 1433 | 1475 | Beam 1.0 501B A23B |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | DeepSeek V3.2 Exp | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 85.0% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: science | 1443 | 1430 | DeepSeek V3.2 Exp |
| LMArena Text: medicine and healthcare | 1468 | 1376 | DeepSeek V3.2 Exp |
| LMArena Text: legal and government | 1419 | 1433 | Beam 1.0 501B A23B |
| LMArena Text: business and finance | 1412 | 1430 | Beam 1.0 501B A23B |
| Neither model has a result on the other 12 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 Exp | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| Design Arena: websites | 1179 | 1253 | Beam 1.0 501B A23B |
| Design Arena: UI components | 1166 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: game dev | 1145 | 1222 | Beam 1.0 501B A23B |
| Design Arena: data viz | 1152 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: 3D | 1148 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: all code categories | 1171 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: SVG | 1031 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index66
| Benchmark | DeepSeek V3.2 Exp | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| IFBench | 54.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Long context reasoning (AA-LCR) | 72.3% | Not measured | Only DeepSeek V3.2 Exp measured |
| DTBench | 87.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMCA | 29.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Fiction.LiveBench | 83.3% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench | 13.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench Life | 9.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text | 1425 | 1425 | Beam 1.0 501B A23B |
| LMArena Text: instruction following | 1401 | 1430 | Beam 1.0 501B A23B |
| LMArena Text: creative writing | 1404 | 1390 | DeepSeek V3.2 Exp |
| LMArena Text: English | 1441 | 1436 | DeepSeek V3.2 Exp |
| LMArena Text: non-English | 1404 | 1413 | Beam 1.0 501B A23B |
| LMArena Text: Chinese | 1461 | 1424 | DeepSeek V3.2 Exp |
| LMArena Text: German | 1440 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Japanese | 1331 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Korean | 1372 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: French | 1434 | 1461 | Beam 1.0 501B A23B |
| LMArena Text: Spanish | 1441 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Russian | 1412 | 1412 | Beam 1.0 501B A23B |
| LMArena Text: Polish | 1435 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: multi-turn | 1422 | 1414 | DeepSeek V3.2 Exp |
| LMArena Text: longer queries | 1418 | 1429 | Beam 1.0 501B A23B |
| LMArena Text: writing and language | 1407 | 1400 | DeepSeek V3.2 Exp |
| LMArena Text: entertainment and media | 1407 | 1399 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | 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
DeepSeek V3.2 Exp costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.2 Exp costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.2 Exp 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 | DeepSeek V3.2 Exp | Reflection Beam 1.0 501B A23B |
|---|---|---|
| Maker | DeepSeek | Reflection |
| Weights | Open weights | Open weights |
| Released | September 29, 2025 | October 16, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.27 | $0.30 |
| Output $/Mtok | $0.41 | $1.00 |
| Coding index | 55.0 | 33.8 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than Beam 1.0 501B A23B for coding?
Pick DeepSeek V3.2 Exp for coding: it scores 55.0 on the Vibe Coding Index against 33.8 for Beam 1.0 501B A23B. Pick Beam 1.0 501B A23B when the context window is larger, 1.05M against 164K tokens, or when what matters is LMArena WebDev or LMArena Text: coding, where it leads.
Which is cheaper, DeepSeek V3.2 Exp or Beam 1.0 501B A23B?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; Beam 1.0 501B A23B costs $0.30 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.2 Exp or Beam 1.0 501B A23B?
Beam 1.0 501B A23B, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3.2 Exp vs Beam 1.0 501B A23B comparison based on?
27 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2 Exp'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.