GLM 4.6 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 GLM 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 33.8 for Beam 1.0 501B A23B.
Pick Beam 1.0 501B A23B when output tokens cost 2.2x less, or when the context window is larger, 1.05M against 205K tokens.
- Shared results
- 27
- GLM 4.6 only
- 39
- 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
GLM 4.6
GLM 4.6 scores 52.2 against 33.8.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only GLM 4.6 has a AA Intelligence Index result, 18.5.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 4.6 has a GPQA Diamond result, 74.5%.
Directional only
Math
AIME
No call
Only GLM 4.6 has a AIME result, 92.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.6 has a MMLU Pro result, 82.2%.
Directional only
Web design
Design Arena: websites
Beam 1.0 501B A23B
Beam 1.0 501B A23B leads, 1253 against 1174.
Same board, both measured
Long documents
Listed context window
Beam 1.0 501B A23B
Beam 1.0 501B A23B takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Beam 1.0 501B A23B
GLM 4.6 $0.0016, Beam 1.0 501B A23B $0.0008 per turn.
List prices
At a glance
- Coding index
- GLM 4.6 52.2 from 2 benchmarks, Beam 1.0 501B A23B 33.8 from 1 benchmark.
- Widest category
- Web design, 9.4 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena WebDev: HTML: GLM 4.6 1350 against Beam 1.0 501B A23B 1436, 14.3 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6 $2.20 against Beam 1.0 501B A23B $1.00 per million output tokens, 2.2x apart.
- Context window
- GLM 4.6 takes 205K 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 | GLM 4.6 | Bars | Beam 1.0 501B A23B | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 3 | 1 | ||
| Web design | 0 | 2 | ||
| General | 9 | 3 | ||
| All shared boards | 12 | 15 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index04
| Benchmark | GLM 4.6 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| SWE-bench Verified | 68.2% | Not measured | Only GLM 4.6 measured |
| LMArena WebDev | 1341 | 1437 | Beam 1.0 501B A23B |
| AA Coding Index | 45.8 | Not measured | Only GLM 4.6 measured |
| LiveCodeBench (Vals AI) | 81.0% | Not measured | Only GLM 4.6 measured |
| Vibe Code Bench (Vals AI) | 3.1% | Not measured | Only GLM 4.6 measured |
| LiveCodeBench (Artificial Analysis run) | 69.5% | Not measured | Only GLM 4.6 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | Not measured | Only GLM 4.6 measured |
| Terminal-Bench | 24.5% | Not measured | Only GLM 4.6 measured |
| ALE-Bench | 340.8 | Not measured | Only GLM 4.6 measured |
| LMArena Text: coding | 1449 | 1473 | Beam 1.0 501B A23B |
| LMArena Text: software and IT | 1455 | 1461 | Beam 1.0 501B A23B |
| LMArena WebDev: React | Not measured | 1443 | Only Beam 1.0 501B A23B measured |
| LMArena WebDev: HTML | 1350 | 1436 | Beam 1.0 501B A23B |
| Neither model has a result on the other 25 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.6 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.5 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GLM 4.6 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1029 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building mobile apps | 1096 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Android apps | 1090 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Godot games | 1180 | Not measured | Only GLM 4.6 measured |
| τ²-Bench Telecom | 76.9% | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GLM 4.6 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| GPQA Diamond | 74.5% | Not measured | Only GLM 4.6 measured |
| GPQA Diamond (Artificial Analysis run) | 78.0% | Not measured | Only GLM 4.6 measured |
| Humanity's Last Exam (Artificial Analysis run) | 14.5% | Not measured | Only GLM 4.6 measured |
| LMArena Text: hard prompts | 1440 | 1442 | Beam 1.0 501B A23B |
| LMArena Text: expert | 1432 | 1447 | Beam 1.0 501B A23B |
| LMArena Text: hard prompts in English | 1445 | 1451 | Beam 1.0 501B A23B |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.6 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AIME | 92.7% | Not measured | Only GLM 4.6 measured |
| MGSM (Vals AI) | 89.8% | Not measured | Only GLM 4.6 measured |
| AA Math Index | 86.0 | Not measured | Only GLM 4.6 measured |
| AIME 2025 (Artificial Analysis run) | 86.0% | Not measured | Only GLM 4.6 measured |
| LMArena Text: math | 1432 | 1459 | Beam 1.0 501B A23B |
| LMArena Text: mathematical work | 1447 | 1475 | Beam 1.0 501B A23B |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 4.6 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| MMLU Pro | 82.2% | Not measured | Only GLM 4.6 measured |
| LegalBench (Vals AI) | 79.6% | Not measured | Only GLM 4.6 measured |
| CorpFin (Vals AI) | 56.8% | Not measured | Only GLM 4.6 measured |
| TaxEval (Vals AI) | 66.2% | Not measured | Only GLM 4.6 measured |
| MedQA (Vals AI) | 92.2% | Not measured | Only GLM 4.6 measured |
| MMLU Pro (Artificial Analysis run) | 82.9% | Not measured | Only GLM 4.6 measured |
| LMArena Text: science | 1456 | 1430 | GLM 4.6 |
| LMArena Text: medicine and healthcare | 1455 | 1376 | GLM 4.6 |
| LMArena Text: legal and government | 1448 | 1433 | GLM 4.6 |
| LMArena Text: business and finance | 1416 | 1430 | Beam 1.0 501B A23B |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.6 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| Design Arena: websites | 1174 | 1253 | Beam 1.0 501B A23B |
| Design Arena: UI components | 1147 | Not measured | Only GLM 4.6 measured |
| Design Arena: game dev | 1151 | 1222 | Beam 1.0 501B A23B |
| Design Arena: data viz | 1161 | Not measured | Only GLM 4.6 measured |
| Design Arena: 3D | 1129 | Not measured | Only GLM 4.6 measured |
| Design Arena: all code categories | 1166 | Not measured | Only GLM 4.6 measured |
| Design Arena: SVG | 1108 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index93
| Benchmark | GLM 4.6 | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| IFBench | 43.4% | Not measured | Only GLM 4.6 measured |
| Long context reasoning (AA-LCR) | 54.0% | Not measured | Only GLM 4.6 measured |
| LMArena Text | 1440 | 1425 | GLM 4.6 |
| LMArena Text: instruction following | 1410 | 1430 | Beam 1.0 501B A23B |
| LMArena Text: creative writing | 1412 | 1390 | GLM 4.6 |
| LMArena Text: English | 1449 | 1436 | GLM 4.6 |
| LMArena Text: non-English | 1426 | 1413 | GLM 4.6 |
| LMArena Text: Chinese | 1499 | 1424 | GLM 4.6 |
| LMArena Text: German | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Japanese | 1393 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Korean | 1400 | Not measured | Only GLM 4.6 measured |
| LMArena Text: French | 1459 | 1461 | Beam 1.0 501B A23B |
| LMArena Text: Spanish | 1436 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Russian | 1418 | 1412 | GLM 4.6 |
| LMArena Text: Polish | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: multi-turn | 1427 | 1414 | GLM 4.6 |
| LMArena Text: longer queries | 1422 | 1429 | Beam 1.0 501B A23B |
| LMArena Text: writing and language | 1415 | 1400 | GLM 4.6 |
| LMArena Text: entertainment and media | 1409 | 1399 | GLM 4.6 |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6 | 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 | Z.ai GLM 4.6 | Reflection Beam 1.0 501B A23B |
|---|---|---|
| Maker | Z.ai | Reflection |
| Weights | Open weights | Open weights |
| Released | September 30, 2025 | October 16, 2026 |
| Context window | 205K | 1.05M |
| Input $/Mtok | $0.55 | $0.30 |
| Output $/Mtok | $2.20 | $1.00 |
| Coding index | 52.2 | 33.8 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6 better than Beam 1.0 501B A23B for coding?
Pick GLM 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 33.8 for Beam 1.0 501B A23B. Pick Beam 1.0 501B A23B when output tokens cost 2.2x less, or when the context window is larger, 1.05M against 205K tokens.
Which is cheaper, GLM 4.6 or Beam 1.0 501B A23B?
GLM 4.6 costs $0.55 per million input tokens and $2.20 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, GLM 4.6 or Beam 1.0 501B A23B?
Beam 1.0 501B A23B, with 1.05M tokens against 205K.
How many benchmarks is the GLM 4.6 vs Beam 1.0 501B A23B comparison based on?
27 benchmarks have a result for both models as of October 21, 2026. GLM 4.6'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.