GLM 4.5 vs Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads GLM 4.5 on the Artificial Analysis Intelligence Index, 39.9 to 12.8, and GLM 4.5 costs 2.7x less per output token ($2.20 vs $6.00 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
GLM 4.5 or Qwen3.8 2.4T A95B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.5 for output tokens 2.7x cheaper ($2.20 vs $6.00 per million) and work measured by AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 4
- GLM 4.5 only
- 57
- Qwen3.8 2.4T A95B only
- 3
- Like-for-like categories
- 3 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads, 39.9 against 12.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.8 2.4T A95B has a AA Agentic Index result, 50.1.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 4.5 has a GPQA Diamond result, 72.2%.
Directional only
Math
AIME
No call
Only GLM 4.5 has a AIME result, 86.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.5 has a MMLU Pro result, 81.2%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.5 has a Design Arena: websites result, 1169.
Directional only
Long documents
Listed context window
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5
GLM 4.5 $0.0017, Qwen3.8 2.4T A95B $0.0050 per turn.
List prices
At a glance
- Coding index
- GLM 4.5 not benchmarked, Qwen3.8 2.4T A95B not benchmarked.
- Widest category
- General, 27.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Humanity's Last Exam (Artificial Analysis run): GLM 4.5 13.0% against Qwen3.8 2.4T A95B 42.4%, 29.4 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 $2.20 against Qwen3.8 2.4T A95B $6.00 per million output tokens, 2.7x apart.
- Context window
- GLM 4.5 takes 131K 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 4.5 | Bars | Qwen3.8 2.4T A95B | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 4 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index10 boards, none shared
| Benchmark | GLM 4.5 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| SWE-bench Verified | 64.2% | Not measured | Only GLM 4.5 measured |
| AA Coding Index | Not measured | 71.9 | Only Qwen3.8 2.4T A95B measured |
| LiveCodeBench (Vals AI) | 67.5% | Not measured | Only GLM 4.5 measured |
| LiveCodeBench (Artificial Analysis run) | 73.8% | Not measured | Only GLM 4.5 measured |
| SciCode | Not measured | 54.1% | Only Qwen3.8 2.4T A95B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | Not measured | Only GLM 4.5 measured |
| WeirdML | 40.6% | Not measured | Only GLM 4.5 measured |
| ALE-Bench | 344.8 | Not measured | Only GLM 4.5 measured |
| LMArena Text: coding | 1434 | Not measured | Only GLM 4.5 measured |
| LMArena Text: software and IT | 1440 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.8 | 39.9 | Qwen3.8 2.4T A95B |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 4.5 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 50.1 | Only Qwen3.8 2.4T A95B measured |
| τ²-Bench Telecom | 43.0% | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.5 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| GPQA Diamond | 72.2% | Not measured | Only GLM 4.5 measured |
| GPQA Diamond (Artificial Analysis run) | 78.2% | 93.5% | Qwen3.8 2.4T A95B |
| Humanity's Last Exam (Artificial Analysis run) | 13.0% | 42.4% | Qwen3.8 2.4T A95B |
| LMArena Text: hard prompts | 1429 | Not measured | Only GLM 4.5 measured |
| LMArena Text: expert | 1433 | Not measured | Only GLM 4.5 measured |
| LMArena Text: hard prompts in English | 1429 | Not measured | Only GLM 4.5 measured |
| Kagi LLM Benchmark | 57.9% | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.5 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AIME | 86.7% | Not measured | Only GLM 4.5 measured |
| MATH 500 (Vals AI) | 94.0% | Not measured | Only GLM 4.5 measured |
| MGSM (Vals AI) | 90.8% | Not measured | Only GLM 4.5 measured |
| AA Math Index | 73.7 | Not measured | Only GLM 4.5 measured |
| AIME 2025 (Artificial Analysis run) | 73.7% | Not measured | Only GLM 4.5 measured |
| LMArena Text: math | 1427 | Not measured | Only GLM 4.5 measured |
| LMArena Text: mathematical work | 1430 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GLM 4.5 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| MMLU Pro | 81.2% | Not measured | Only GLM 4.5 measured |
| LegalBench (Vals AI) | 75.6% | Not measured | Only GLM 4.5 measured |
| CorpFin (Vals AI) | 61.0% | Not measured | Only GLM 4.5 measured |
| TaxEval (Vals AI) | 72.4% | Not measured | Only GLM 4.5 measured |
| MedQA (Vals AI) | 90.0% | Not measured | Only GLM 4.5 measured |
| MMLU Pro (Artificial Analysis run) | 83.5% | Not measured | Only GLM 4.5 measured |
| LMArena Text: science | 1444 | Not measured | Only GLM 4.5 measured |
| LMArena Text: medicine and healthcare | 1455 | Not measured | Only GLM 4.5 measured |
| LMArena Text: legal and government | 1438 | Not measured | Only GLM 4.5 measured |
| LMArena Text: business and finance | 1420 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.5 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| Design Arena: websites | 1169 | Not measured | Only GLM 4.5 measured |
| Design Arena: UI components | 1141 | Not measured | Only GLM 4.5 measured |
| Design Arena: game dev | 1144 | Not measured | Only GLM 4.5 measured |
| Design Arena: data viz | 1159 | Not measured | Only GLM 4.5 measured |
| Design Arena: 3D | 1170 | Not measured | Only GLM 4.5 measured |
| Design Arena: all code categories | 1165 | Not measured | Only GLM 4.5 measured |
| Design Arena: SVG | 1096 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| IFBench | 44.1% | Not measured | Only GLM 4.5 measured |
| Long context reasoning (AA-LCR) | 52.7% | 80.3% | Qwen3.8 2.4T A95B |
| Creative writing (Lech Mazur) | 73.4% | Not measured | Only GLM 4.5 measured |
| LMArena Text | 1430 | Not measured | Only GLM 4.5 measured |
| LMArena Text: instruction following | 1404 | Not measured | Only GLM 4.5 measured |
| LMArena Text: creative writing | 1395 | Not measured | Only GLM 4.5 measured |
| LMArena Text: English | 1437 | Not measured | Only GLM 4.5 measured |
| LMArena Text: non-English | 1417 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Chinese | 1465 | Not measured | Only GLM 4.5 measured |
| LMArena Text: German | 1407 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Japanese | 1415 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Korean | 1380 | Not measured | Only GLM 4.5 measured |
| LMArena Text: French | 1418 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Spanish | 1454 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Russian | 1413 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Polish | 1422 | Not measured | Only GLM 4.5 measured |
| LMArena Text: multi-turn | 1415 | Not measured | Only GLM 4.5 measured |
| LMArena Text: longer queries | 1412 | Not measured | Only GLM 4.5 measured |
| LMArena Text: writing and language | 1403 | Not measured | Only GLM 4.5 measured |
| LMArena Text: entertainment and media | 1404 | Not measured | Only GLM 4.5 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 4.5 | 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 4.5 costs less
Repository review
50K input + 3K output tokens
GLM 4.5 costs less
Agent loop
200K input + 10K output tokens
GLM 4.5 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 4.5 | Qwen3.8 2.4T A95B |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | July 25, 2025 | August 12, 2026 |
| Context window | 131K | 1.05M |
| Input $/Mtok | $0.60 | $2.00 |
| Output $/Mtok | $2.20 | $6.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5 better than Qwen3.8 2.4T A95B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5 for output tokens 2.7x cheaper ($2.20 vs $6.00 per million) and work measured by AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, GLM 4.5 or Qwen3.8 2.4T A95B?
GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.8 2.4T A95B costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.5 or Qwen3.8 2.4T A95B?
Qwen3.8 2.4T A95B, with 1.05M tokens against 131K.
How many benchmarks is the GLM 4.5 vs Qwen3.8 2.4T A95B comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. GLM 4.5's coding index rests on 0 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.