GLM 5.1 vs Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads GLM 5.1 on the Artificial Analysis Intelligence Index, 39.9 to 26.1, and GLM 5.1 costs 1.4x less per output token ($4.40 vs $6.00 per million). Updated October 22, 2026; 9 benchmarks have a result for both.
GLM 5.1 or Qwen3.8 2.4T A95B: which to pick
Only GLM 5.1 has a coding score, 49.2 on the Vibe Coding Index; Qwen3.8 2.4T A95B has none, so the two cannot be ranked on coding.
Pick Qwen3.8 2.4T A95B for a larger context window (1.05M against 205K tokens) and work measured by AA Coding Index or SciCode, where it leads.
- Shared results
- 9
- GLM 5.1 only
- 82
- Qwen3.8 2.4T A95B only
- 0
- Like-for-like categories
- 6 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 GLM 5.1 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads, 39.9 against 26.1.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads, 50.1 against 23.9.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 5.1 has a GPQA Diamond result, 84.5%.
Directional only
Math
AIME
No call
Only GLM 5.1 has a AIME result, 91.9%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.1 has a MMLU Pro result, 86.9%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 5.1 has a Design Arena: websites result, 1290.
Directional only
Long documents
Listed context window
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.1
GLM 5.1 $0.0036, Qwen3.8 2.4T A95B $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.1 has the number, $0.60.
Directional only
At a glance
- Coding index
- GLM 5.1 49.2 from 3 benchmarks, Qwen3.8 2.4T A95B not benchmarked.
- Widest category
- Agentic work, 17.6 points apart on average over 2 shared benchmarks.
- Widest single gap
- AA Agentic Index: GLM 5.1 23.9 against Qwen3.8 2.4T A95B 50.1, 26.2 points apart on a 0 to 100 scale.
- Output price
- GLM 5.1 $4.40 against Qwen3.8 2.4T A95B $6.00 per million output tokens, 1.4x apart.
- Context window
- GLM 5.1 takes 205K tokens, Qwen3.8 2.4T A95B 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 5.1 | Bars | Qwen3.8 2.4T A95B | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 0 | 2 | ||
| Math | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 9 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GLM 5.1 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| LMArena WebDev | 1508 | Not measured | Only GLM 5.1 measured |
| AA Coding Index | 55.8 | 71.9 | Qwen3.8 2.4T A95B |
| LiveCodeBench (Vals AI) | 81.4% | Not measured | Only GLM 5.1 measured |
| SWE-bench Verified (Vals AI) | 76.4% | Not measured | Only GLM 5.1 measured |
| Vibe Code Bench (Vals AI) | 31.5% | Not measured | Only GLM 5.1 measured |
| Code migration (Vals AI) | 25.8% | Not measured | Only GLM 5.1 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only GLM 5.1 measured |
| Terminal-Bench 2.1 (Vals AI) | 56.9% | Not measured | Only GLM 5.1 measured |
| SciCode | 44.8% | 54.1% | Qwen3.8 2.4T A95B |
| Terminal-Bench Hard (Artificial Analysis run) | 43.2% | Not measured | Only GLM 5.1 measured |
| WeirdML | 57.1% | Not measured | Only GLM 5.1 measured |
| ALE-Bench | 887.1 | Not measured | Only GLM 5.1 measured |
| LMArena Text: coding | 1485 | Not measured | Only GLM 5.1 measured |
| LMArena Text: software and IT | 1484 | Not measured | Only GLM 5.1 measured |
| LMArena WebDev: React | 1505 | Not measured | Only GLM 5.1 measured |
| LMArena WebDev: HTML | 1508 | Not measured | Only GLM 5.1 measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5.1 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Intelligence Index | 26.1 | 39.9 | Qwen3.8 2.4T A95B |
| Epoch Capabilities Index | 149.8 | Not measured | Only GLM 5.1 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5.1 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Agentic Index | 23.9 | 50.1 | Qwen3.8 2.4T A95B |
| Design Arena: agents building web apps | 1175 | Not measured | Only GLM 5.1 measured |
| Design Arena: agents building full-stack apps | 1167 | Not measured | Only GLM 5.1 measured |
| Design Arena: agents building mobile apps | 1160 | Not measured | Only GLM 5.1 measured |
| Design Arena: agents building Android apps | 1181 | Not measured | Only GLM 5.1 measured |
| Design Arena: agents building Godot games | 1053 | Not measured | Only GLM 5.1 measured |
| Design Arena: agents building games | 1164 | Not measured | Only GLM 5.1 measured |
| Design Arena: HTML slides | 1109 | Not measured | Only GLM 5.1 measured |
| Design Arena: PowerPoint slides | 1162 | Not measured | Only GLM 5.1 measured |
| Design Arena: spreadsheets | 1315 | Not measured | Only GLM 5.1 measured |
| Finance Agent (Vals AI) | 44.8% | Not measured | Only GLM 5.1 measured |
| Legal Research Bench (Vals AI) | 27.9% | Not measured | Only GLM 5.1 measured |
| τ²-Bench Telecom | 97.7% | Not measured | Only GLM 5.1 measured |
| APEX-Agents | 40.9% | Not measured | Only GLM 5.1 measured |
| Vending-Bench 2 | $5,634 | Not measured | Only GLM 5.1 measured |
| MCP Atlas | 75.6% | 84.5% | Qwen3.8 2.4T A95B |
| Neither model has a result on the other 26 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5.1 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| GPQA Diamond | 84.5% | Not measured | Only GLM 5.1 measured |
| GPQA Diamond (Artificial Analysis run) | 86.8% | 93.5% | Qwen3.8 2.4T A95B |
| Humanity's Last Exam (Artificial Analysis run) | 30.1% | 42.4% | Qwen3.8 2.4T A95B |
| GPQA Diamond (Epoch AI run) | 89.9% | Not measured | Only GLM 5.1 measured |
| SimpleBench | 55.1% | Not measured | Only GLM 5.1 measured |
| Chess puzzles | 20.0% | Not measured | Only GLM 5.1 measured |
| LMArena Text: hard prompts | 1473 | Not measured | Only GLM 5.1 measured |
| LMArena Text: expert | 1478 | Not measured | Only GLM 5.1 measured |
| LMArena Text: hard prompts in English | 1482 | Not measured | Only GLM 5.1 measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5.1 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AIME | 91.9% | Not measured | Only GLM 5.1 measured |
| OTIS Mock AIME | 93.3% | Not measured | Only GLM 5.1 measured |
| FrontierMath, tiers 1 to 3 | 36.8% | Not measured | Only GLM 5.1 measured |
| ProofBench | 22.2% | Not measured | Only GLM 5.1 measured |
| LMArena Text: math | 1473 | Not measured | Only GLM 5.1 measured |
| LMArena Text: mathematical work | 1470 | Not measured | Only GLM 5.1 measured |
| MathArena | 20.1% | 31.6% | Qwen3.8 2.4T A95B |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | GLM 5.1 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| MMLU Pro | 86.9% | Not measured | Only GLM 5.1 measured |
| LegalBench (Vals AI) | 84.4% | Not measured | Only GLM 5.1 measured |
| CaseLaw (Vals AI) | 51.5% | Not measured | Only GLM 5.1 measured |
| CorpFin (Vals AI) | 64.5% | Not measured | Only GLM 5.1 measured |
| TaxEval (Vals AI) | 71.2% | Not measured | Only GLM 5.1 measured |
| MedCode (Vals AI) | 41.6% | Not measured | Only GLM 5.1 measured |
| SimpleQA Verified | 34.0% | Not measured | Only GLM 5.1 measured |
| LMArena Text: science | 1478 | Not measured | Only GLM 5.1 measured |
| LMArena Text: medicine and healthcare | 1474 | Not measured | Only GLM 5.1 measured |
| LMArena Text: legal and government | 1465 | Not measured | Only GLM 5.1 measured |
| LMArena Text: business and finance | 1452 | Not measured | Only GLM 5.1 measured |
| Neither model has a result on the other 9 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 5.1 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| Design Arena: websites | 1290 | Not measured | Only GLM 5.1 measured |
| Design Arena: UI components | 1292 | Not measured | Only GLM 5.1 measured |
| Design Arena: game dev | 1264 | Not measured | Only GLM 5.1 measured |
| Design Arena: data viz | 1366 | Not measured | Only GLM 5.1 measured |
| Design Arena: 3D | 1336 | Not measured | Only GLM 5.1 measured |
| Design Arena: all code categories | 1268 | Not measured | Only GLM 5.1 measured |
| Design Arena: SVG | 1217 | Not measured | Only GLM 5.1 measured |
| Design Arena: ASCII art | 1141 | Not measured | Only GLM 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 index01
| Benchmark | GLM 5.1 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 61.8% | Not measured | Only GLM 5.1 measured |
| MedScribe (Vals AI) | 72.3% | Not measured | Only GLM 5.1 measured |
| IFBench | 76.3% | Not measured | Only GLM 5.1 measured |
| Long context reasoning (AA-LCR) | 73.7% | 80.3% | Qwen3.8 2.4T A95B |
| ExploitBench | 18.1% | Not measured | Only GLM 5.1 measured |
| LMArena Text | 1463 | Not measured | Only GLM 5.1 measured |
| LMArena Text: instruction following | 1453 | Not measured | Only GLM 5.1 measured |
| LMArena Text: creative writing | 1453 | Not measured | Only GLM 5.1 measured |
| LMArena Text: English | 1476 | Not measured | Only GLM 5.1 measured |
| LMArena Text: non-English | 1448 | Not measured | Only GLM 5.1 measured |
| LMArena Text: Chinese | 1515 | Not measured | Only GLM 5.1 measured |
| LMArena Text: German | 1467 | Not measured | Only GLM 5.1 measured |
| LMArena Text: Japanese | 1435 | Not measured | Only GLM 5.1 measured |
| LMArena Text: Korean | 1424 | Not measured | Only GLM 5.1 measured |
| LMArena Text: French | 1470 | Not measured | Only GLM 5.1 measured |
| LMArena Text: Spanish | 1468 | Not measured | Only GLM 5.1 measured |
| LMArena Text: Russian | 1454 | Not measured | Only GLM 5.1 measured |
| LMArena Text: Polish | 1449 | Not measured | Only GLM 5.1 measured |
| LMArena Text: multi-turn | 1473 | Not measured | Only GLM 5.1 measured |
| LMArena Text: longer queries | 1467 | Not measured | Only GLM 5.1 measured |
| LMArena Text: writing and language | 1454 | Not measured | Only GLM 5.1 measured |
| LMArena Text: entertainment and media | 1442 | Not measured | Only GLM 5.1 measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.1 | Qwen3.8 2.4T A95B | 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
GLM 5.1 costs less
Repository review
50K input + 3K output tokens
GLM 5.1 costs less
Agent loop
200K input + 10K output tokens
GLM 5.1 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 5.1 | Qwen3.8 2.4T A95B |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | April 7, 2026 | August 12, 2026 |
| Context window | 205K | 1.05M |
| Input $/Mtok | $1.40 | $2.00 |
| Output $/Mtok | $4.40 | $6.00 |
| Coding index | 49.2 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.60 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.1 better than Qwen3.8 2.4T A95B for coding?
Only GLM 5.1 has a coding score, 49.2 on the Vibe Coding Index; Qwen3.8 2.4T A95B has none, so the two cannot be ranked on coding. Pick Qwen3.8 2.4T A95B for a larger context window (1.05M against 205K tokens) and work measured by AA Coding Index or SciCode, where it leads.
Which is cheaper, GLM 5.1 or Qwen3.8 2.4T A95B?
GLM 5.1 costs $1.40 per million input tokens and $4.40 per million output tokens; Qwen3.8 2.4T A95B costs $2.00 and $6.00. Cost per solved task is known for GLM 5.1 only.
Which has the bigger context window, GLM 5.1 or Qwen3.8 2.4T A95B?
Qwen3.8 2.4T A95B, with 1.05M tokens against 205K.
How many benchmarks is the GLM 5.1 vs Qwen3.8 2.4T A95B comparison based on?
9 benchmarks have a result for both models as of October 22, 2026. GLM 5.1's coding index rests on 3 independent benchmarks and Qwen3.8 2.4T A95B's on 0. Where only one model has a result, the page says so instead of naming a winner.