GLM 4.6 vs Qwen3 Next 80B A3B Instruct
GLM 4.6 leads Qwen3 Next 80B A3B Instruct on the Artificial Analysis Intelligence Index, 18.5 to 9.6, and Qwen3 Next 80B A3B Instruct costs 2.0x less per output token ($1.10 vs $2.20 per million). Updated October 22, 2026; 40 benchmarks have a result for both.
GLM 4.6 or Qwen3 Next 80B A3B Instruct: which to pick
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Qwen3 Next 80B A3B Instruct has none, so the two cannot be ranked on coding.
Pick Qwen3 Next 80B A3B Instruct for output tokens 2.0x cheaper ($1.10 vs $2.20 per million) and a larger context window (262K against 205K tokens).
- Shared results
- 40
- GLM 4.6 only
- 29
- Qwen3 Next 80B A3B Instruct only
- 1
- Like-for-like categories
- 7 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 4.6 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.6
GLM 4.6 leads, 18.5 against 9.6.
Same board, both measured
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
No call
Only GLM 4.6 has a Design Arena: websites result, 1174.
Directional only
Long documents
Listed context window
Qwen3 Next 80B A3B Instruct
Qwen3 Next 80B A3B Instruct takes 262K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 Next 80B A3B Instruct
GLM 4.6 $0.0017, Qwen3 Next 80B A3B Instruct $0.0006 per turn.
List prices
At a glance
- Coding index
- GLM 4.6 8.8 from 2 benchmarks, Qwen3 Next 80B A3B Instruct not benchmarked.
- Widest category
- Agentic work, 35.4 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GLM 4.6 76.9% against Qwen3 Next 80B A3B Instruct 41.5%, 35.4 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6 $2.20 against Qwen3 Next 80B A3B Instruct $1.10 per million output tokens, 2.0x apart.
- Context window
- GLM 4.6 takes 205K tokens, Qwen3 Next 80B A3B Instruct 262K.
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 | Qwen3 Next 80B A3B Instruct | Ties |
|---|---|---|---|---|
| Coding | 3 | 1 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 1 | ||
| Math | 3 | 1 | ||
| Knowledge | 3 | 2 | ||
| General | 16 | 3 | ||
| All shared boards | 32 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index31
| Benchmark | GLM 4.6 | Qwen3 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| SWE-bench Verified | 68.2% | Not measured | Only GLM 4.6 measured |
| LMArena WebDev | 1341 | Not measured | Only GLM 4.6 measured |
| 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% | 78.4% | Qwen3 Next 80B A3B Instruct |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | 9.8% | GLM 4.6 |
| 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 | 1440 | GLM 4.6 |
| LMArena Text: software and IT | 1455 | 1447 | GLM 4.6 |
| LMArena WebDev: HTML | 1350 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6 | Qwen3 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.5 | 9.6 | GLM 4.6 |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6 | Qwen3 Next 80B A3B Instruct | 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 | 1084 | 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% | 41.5% | GLM 4.6 |
| Berkeley Function Calling Leaderboard (BFCL) | 72.4% | Not measured | Only GLM 4.6 measured |
| BrowseComp-Plus | 48.2% | 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 index51
| Benchmark | GLM 4.6 | Qwen3 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 74.5% | Not measured | Only GLM 4.6 measured |
| GPQA Diamond (Artificial Analysis run) | 78.0% | 75.9% | GLM 4.6 |
| Humanity's Last Exam (Artificial Analysis run) | 14.5% | 12.6% | GLM 4.6 |
| LMArena Text: hard prompts | 1440 | 1428 | GLM 4.6 |
| LMArena Text: expert | 1432 | 1417 | GLM 4.6 |
| LMArena Text: hard prompts in English | 1445 | 1431 | GLM 4.6 |
| Kagi LLM Benchmark | 45.7% | 66.7% | Qwen3 Next 80B A3B Instruct |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 4.6 | Qwen3 Next 80B A3B Instruct | 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 | 84.3 | GLM 4.6 |
| AIME 2025 (Artificial Analysis run) | 86.0% | 84.3% | GLM 4.6 |
| LMArena Text: math | 1432 | 1440 | Qwen3 Next 80B A3B Instruct |
| LMArena Text: mathematical work | 1447 | 1434 | GLM 4.6 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index32
| Benchmark | GLM 4.6 | Qwen3 Next 80B A3B Instruct | 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% | 82.4% | GLM 4.6 |
| LMArena Text: science | 1456 | 1434 | GLM 4.6 |
| LMArena Text: medicine and healthcare | 1455 | 1455 | Qwen3 Next 80B A3B Instruct |
| LMArena Text: legal and government | 1448 | 1431 | GLM 4.6 |
| LMArena Text: business and finance | 1416 | 1422 | Qwen3 Next 80B A3B Instruct |
| 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 4.6 | Qwen3 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1174 | Not measured | Only GLM 4.6 measured |
| Design Arena: UI components | 1145 | Not measured | Only GLM 4.6 measured |
| Design Arena: game dev | 1150 | Not measured | Only GLM 4.6 measured |
| Design Arena: data viz | 1161 | Not measured | Only GLM 4.6 measured |
| Design Arena: 3D | 1128 | Not measured | Only GLM 4.6 measured |
| Design Arena: all code categories | 1165 | 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 index163
| Benchmark | GLM 4.6 | Qwen3 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| IFBench | 43.4% | 60.7% | Qwen3 Next 80B A3B Instruct |
| Long context reasoning (AA-LCR) | 54.0% | 63.7% | Qwen3 Next 80B A3B Instruct |
| Fiction.LiveBench | Not measured | 55.6% | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text | 1440 | 1417 | GLM 4.6 |
| LMArena Text: instruction following | 1410 | 1388 | GLM 4.6 |
| LMArena Text: creative writing | 1412 | 1333 | GLM 4.6 |
| LMArena Text: English | 1449 | 1421 | GLM 4.6 |
| LMArena Text: non-English | 1426 | 1407 | GLM 4.6 |
| LMArena Text: Chinese | 1499 | 1460 | GLM 4.6 |
| LMArena Text: German | 1447 | 1416 | GLM 4.6 |
| LMArena Text: Japanese | 1393 | 1395 | Qwen3 Next 80B A3B Instruct |
| LMArena Text: Korean | 1400 | 1363 | GLM 4.6 |
| LMArena Text: French | 1459 | 1414 | GLM 4.6 |
| LMArena Text: Spanish | 1436 | 1435 | GLM 4.6 |
| LMArena Text: Russian | 1418 | 1404 | GLM 4.6 |
| LMArena Text: Polish | 1447 | 1398 | GLM 4.6 |
| LMArena Text: multi-turn | 1427 | 1416 | GLM 4.6 |
| LMArena Text: longer queries | 1422 | 1402 | GLM 4.6 |
| LMArena Text: writing and language | 1415 | 1355 | GLM 4.6 |
| LMArena Text: entertainment and media | 1409 | 1349 | GLM 4.6 |
| 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 4.6 | Qwen3 Next 80B A3B Instruct | 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
Qwen3 Next 80B A3B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen3 Next 80B A3B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen3 Next 80B A3B Instruct 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 | Qwen3 Next 80B A3B Instruct |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | September 30, 2025 | September 11, 2025 |
| Context window | 205K | 262K |
| Input $/Mtok | $0.60 | $0.10 |
| Output $/Mtok | $2.20 | $1.10 |
| Coding index | 8.8 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6 better than Qwen3 Next 80B A3B Instruct for coding?
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Qwen3 Next 80B A3B Instruct has none, so the two cannot be ranked on coding. Pick Qwen3 Next 80B A3B Instruct for output tokens 2.0x cheaper ($1.10 vs $2.20 per million) and a larger context window (262K against 205K tokens).
Which is cheaper, GLM 4.6 or Qwen3 Next 80B A3B Instruct?
GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3 Next 80B A3B Instruct costs $0.10 and $1.10. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.6 or Qwen3 Next 80B A3B Instruct?
Qwen3 Next 80B A3B Instruct, with 262K tokens against 205K.
How many benchmarks is the GLM 4.6 vs Qwen3 Next 80B A3B Instruct comparison based on?
40 benchmarks have a result for both models as of October 22, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and Qwen3 Next 80B A3B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.