GPT-5.6 Terra vs Qwen3 Next 80B A3B Instruct
GPT-5.6 Terra leads Qwen3 Next 80B A3B Instruct on the Artificial Analysis Intelligence Index, 42.1 to 9.6, and Qwen3 Next 80B A3B Instruct costs 10.9x less per output token ($1.10 vs $12.00 per million). Updated October 22, 2026; 36 benchmarks have a result for both.
GPT-5.6 Terra or Qwen3 Next 80B A3B Instruct: which to pick
Only GPT-5.6 Terra has a coding score, 63.9 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 10.9x cheaper ($1.10 vs $12.00 per million) and work measured by Kagi LLM Benchmark or LMArena Text: medicine and healthcare, where it leads.
- Shared results
- 36
- GPT-5.6 Terra only
- 93
- Qwen3 Next 80B A3B Instruct only
- 5
- 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 GPT-5.6 Terra has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.6 Terra
GPT-5.6 Terra leads, 42.1 against 9.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GPT-5.6 Terra has a AA Agentic Index result, 43.2.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.6 Terra has a GPQA Diamond result, 90.9%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.6 Terra has a MMLU Pro result, 86.7%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.6 Terra has a Design Arena: websites result, 1268.
Directional only
Long documents
Listed context window
GPT-5.6 Terra
GPT-5.6 Terra takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 Next 80B A3B Instruct
GPT-5.6 Terra $0.0080, Qwen3 Next 80B A3B Instruct $0.0006 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.6 Terra has the number, $0.42.
Directional only
At a glance
- Coding index
- GPT-5.6 Terra 63.9 from 6 benchmarks, Qwen3 Next 80B A3B Instruct not benchmarked.
- Widest category
- Agentic work, 44.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Terminal-Bench Hard (Artificial Analysis run): GPT-5.6 Terra 62.9% against Qwen3 Next 80B A3B Instruct 9.8%, 53.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Terra $12.00 against Qwen3 Next 80B A3B Instruct $1.10 per million output tokens, 10.9x apart.
- Context window
- GPT-5.6 Terra takes 1.05M 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 | GPT-5.6 Terra | Bars | Qwen3 Next 80B A3B Instruct | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 1 | ||
| Math | 2 | 0 | ||
| Knowledge | 3 | 1 | ||
| General | 19 | 0 | ||
| All shared boards | 34 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | GPT-5.6 Terra | Qwen3 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| LiveBench coding | 86.2% | Not measured | Only GPT-5.6 Terra measured |
| LMArena WebDev | 1522 | Not measured | Only GPT-5.6 Terra measured |
| AA Coding Index | 76.7 | Not measured | Only GPT-5.6 Terra measured |
| LiveCodeBench (Vals AI) | 85.9% | Not measured | Only GPT-5.6 Terra measured |
| SWE-bench Verified (Vals AI) | 95.4% | Not measured | Only GPT-5.6 Terra measured |
| Terminal-Bench 4 (Vals AI) | 22.7% | Not measured | Only GPT-5.6 Terra measured |
| Vibe Code Bench (Vals AI) | 74.6% | Not measured | Only GPT-5.6 Terra measured |
| Code migration (Vals AI) | 47.8% | Not measured | Only GPT-5.6 Terra measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GPT-5.6 Terra measured |
| IOI (Vals AI) | 87.6% | Not measured | Only GPT-5.6 Terra measured |
| Terminal-Bench 2.1 (Vals AI) | 77.5% | Not measured | Only GPT-5.6 Terra measured |
| Vibe Code Bench 1-100 (Vals AI) | 14.8% | Not measured | Only GPT-5.6 Terra measured |
| SkillsBench (Vals AI) | 58.9% | Not measured | Only GPT-5.6 Terra measured |
| CyberBench (Vals AI) | 72.1% | Not measured | Only GPT-5.6 Terra measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 78.4% | Only Qwen3 Next 80B A3B Instruct measured |
| SciCode | 55.0% | Not measured | Only GPT-5.6 Terra measured |
| Terminal-Bench Hard (Artificial Analysis run) | 62.9% | 9.8% | GPT-5.6 Terra |
| FrontierCode | 41.3% | Not measured | Only GPT-5.6 Terra measured |
| WeirdML | 78.3% | Not measured | Only GPT-5.6 Terra measured |
| ALE-Bench | 1951.4 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: coding | 1485 | 1440 | GPT-5.6 Terra |
| LMArena Text: software and IT | 1482 | 1447 | GPT-5.6 Terra |
| LMArena WebDev: React | 1513 | Not measured | Only GPT-5.6 Terra measured |
| LMArena WebDev: HTML | 1526 | Not measured | Only GPT-5.6 Terra measured |
| LMArena WebDev: from an image | 1541 | Not measured | Only GPT-5.6 Terra measured |
| SciCode | 10.8% | Not measured | Only GPT-5.6 Terra measured |
| SWE-Bench Pro V2 | 92.4% | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Terra | Qwen3 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 42.1 | 9.6 | GPT-5.6 Terra |
| Epoch Capabilities Index | 160.1 | Not measured | Only GPT-5.6 Terra measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Terra | Qwen3 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 43.2 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: agents building web apps | 1120 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: agents building full-stack apps | 1061 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: agents building mobile apps | 1110 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: agents building Android apps | 1060 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: agents building games | 1083 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: HTML slides | 1047 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: PowerPoint slides | 1104 | Not measured | Only GPT-5.6 Terra measured |
| LiveBench research | 60.4% | Not measured | Only GPT-5.6 Terra measured |
| LiveBench browser | 84.0% | Not measured | Only GPT-5.6 Terra measured |
| LiveBench long agentic | 48.4% | Not measured | Only GPT-5.6 Terra measured |
| SREBench (Vals AI) | 9.5% | Not measured | Only GPT-5.6 Terra measured |
| Finance Agent (Vals AI) | 54.4% | Not measured | Only GPT-5.6 Terra measured |
| Tax Agent Bench (Vals AI) | 30.0% | Not measured | Only GPT-5.6 Terra measured |
| Legal Research Bench (Vals AI) | 41.4% | Not measured | Only GPT-5.6 Terra measured |
| Excel Modeling Benchmark (Vals AI) | 66.2% | Not measured | Only GPT-5.6 Terra measured |
| Terminal-Bench-Science (Vals AI) | 10.0% | Not measured | Only GPT-5.6 Terra measured |
| τ²-Bench Telecom | 86.3% | 41.5% | GPT-5.6 Terra |
| APEX-Agents | 58.2% | Not measured | Only GPT-5.6 Terra measured |
| BALROG | 53.2% | Not measured | Only GPT-5.6 Terra measured |
| Vending-Bench 2 | $7,343 | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 21 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index51
| Benchmark | GPT-5.6 Terra | Qwen3 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 90.9% | Not measured | Only GPT-5.6 Terra measured |
| LiveBench reasoning | 90.6% | Not measured | Only GPT-5.6 Terra measured |
| MMMU Pro (Vals AI) | 86.5% | Not measured | Only GPT-5.6 Terra measured |
| GPQA Diamond (Artificial Analysis run) | 92.5% | 75.9% | GPT-5.6 Terra |
| Humanity's Last Exam (Artificial Analysis run) | 42.9% | 12.6% | GPT-5.6 Terra |
| GPQA Diamond (Epoch AI run) | 93.3% | Not measured | Only GPT-5.6 Terra measured |
| ARC-AGI | 96.5% | Not measured | Only GPT-5.6 Terra measured |
| ARC-AGI-2 | 83.9% | Not measured | Only GPT-5.6 Terra measured |
| SimpleBench | 48.9% | Not measured | Only GPT-5.6 Terra measured |
| Chess puzzles | 47.0% | Not measured | Only GPT-5.6 Terra measured |
| Mystery game puzzles | 35.0% | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: hard prompts | 1469 | 1428 | GPT-5.6 Terra |
| LMArena Text: expert | 1496 | 1417 | GPT-5.6 Terra |
| LMArena Text: hard prompts in English | 1465 | 1431 | GPT-5.6 Terra |
| LMArena Vision: diagrams | 1295 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: homework | 1312 | Not measured | Only GPT-5.6 Terra measured |
| Kagi LLM Benchmark | 51.3% | 66.7% | Qwen3 Next 80B A3B Instruct |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Terra | Qwen3 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 84.3 | Only Qwen3 Next 80B A3B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 84.3% | Only Qwen3 Next 80B A3B Instruct measured |
| OTIS Mock AIME | 99.7% | Not measured | Only GPT-5.6 Terra measured |
| FrontierMath, tiers 1 to 3 | 86.0% | Not measured | Only GPT-5.6 Terra measured |
| FrontierMath, tier 4 | 70.7% | Not measured | Only GPT-5.6 Terra measured |
| ProofBench | 74.0% | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: math | 1465 | 1440 | GPT-5.6 Terra |
| LMArena Text: mathematical work | 1478 | 1434 | GPT-5.6 Terra |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GPT-5.6 Terra | Qwen3 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | Not measured | Only GPT-5.6 Terra measured |
| LegalBench (Vals AI) | 85.1% | Not measured | Only GPT-5.6 Terra measured |
| CorpFin (Vals AI) | 65.3% | Not measured | Only GPT-5.6 Terra measured |
| TaxEval (Vals AI) | 76.2% | Not measured | Only GPT-5.6 Terra measured |
| MortgageTax (Vals AI) | 67.3% | Not measured | Only GPT-5.6 Terra measured |
| MedCode (Vals AI) | 43.4% | Not measured | Only GPT-5.6 Terra measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 82.4% | Only Qwen3 Next 80B A3B Instruct measured |
| SimpleQA Verified | 43.2% | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: science | 1452 | 1434 | GPT-5.6 Terra |
| LMArena Text: medicine and healthcare | 1430 | 1455 | Qwen3 Next 80B A3B Instruct |
| LMArena Text: legal and government | 1461 | 1431 | GPT-5.6 Terra |
| LMArena Text: business and finance | 1448 | 1422 | GPT-5.6 Terra |
| FACTS Benchmark Suite | 52.0% | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.6 Terra | Qwen3 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1268 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: UI components | 1256 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: game dev | 1229 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: data viz | 1240 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: 3D | 1134 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: all code categories | 1240 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: SVG | 1219 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: ASCII art | 1178 | Not measured | Only GPT-5.6 Terra measured |
| LiveBench design | 76.0% | Not measured | Only GPT-5.6 Terra measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
| Benchmark | GPT-5.6 Terra | Qwen3 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.3% | Not measured | Only GPT-5.6 Terra measured |
| Public Benefits Bench (Vals AI) | 62.4% | Not measured | Only GPT-5.6 Terra measured |
| SAGE (Vals AI) | 47.0% | Not measured | Only GPT-5.6 Terra measured |
| MedScribe (Vals AI) | 82.9% | Not measured | Only GPT-5.6 Terra measured |
| Vals Multimodal Index | 65.1% | Not measured | Only GPT-5.6 Terra measured |
| IFBench | 71.2% | 60.7% | GPT-5.6 Terra |
| Long context reasoning (AA-LCR) | 83.0% | 63.7% | GPT-5.6 Terra |
| DTBench | 93.3% | Not measured | Only GPT-5.6 Terra measured |
| LMCA | 54.9% | Not measured | Only GPT-5.6 Terra measured |
| Fiction.LiveBench | Not measured | 55.6% | Only Qwen3 Next 80B A3B Instruct measured |
| Surface Evolver Bench | 83.8% | Not measured | Only GPT-5.6 Terra measured |
| Furniture assembly | 54.2% | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text | 1448 | 1417 | GPT-5.6 Terra |
| LMArena Text: instruction following | 1456 | 1388 | GPT-5.6 Terra |
| LMArena Text: creative writing | 1413 | 1333 | GPT-5.6 Terra |
| LMArena Vision | 1271 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: English | 1449 | 1421 | GPT-5.6 Terra |
| LMArena Text: non-English | 1441 | 1407 | GPT-5.6 Terra |
| LMArena Text: Chinese | 1514 | 1460 | GPT-5.6 Terra |
| LMArena Text: German | 1460 | 1416 | GPT-5.6 Terra |
| LMArena Text: Japanese | 1454 | 1395 | GPT-5.6 Terra |
| LMArena Text: Korean | 1427 | 1363 | GPT-5.6 Terra |
| LMArena Text: French | 1466 | 1414 | GPT-5.6 Terra |
| LMArena Text: Spanish | 1451 | 1435 | GPT-5.6 Terra |
| LMArena Text: Russian | 1452 | 1404 | GPT-5.6 Terra |
| LMArena Text: Polish | 1446 | 1398 | GPT-5.6 Terra |
| LMArena Text: multi-turn | 1453 | 1416 | GPT-5.6 Terra |
| LMArena Text: longer queries | 1454 | 1402 | GPT-5.6 Terra |
| LMArena Text: writing and language | 1431 | 1355 | GPT-5.6 Terra |
| LMArena Text: entertainment and media | 1418 | 1349 | GPT-5.6 Terra |
| LMArena Vision: English | 1272 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: Chinese | 1337 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: reading text | 1278 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: humor | 1236 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: creative writing | 1248 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Document | 1472 | Not measured | Only GPT-5.6 Terra measured |
| EQ-Bench 4 | 1234 | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Terra | 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 | OpenAI GPT-5.6 Terra | Qwen3 Next 80B A3B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | July 9, 2026 | September 11, 2025 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $2.00 | $0.10 |
| Output $/Mtok | $12.00 | $1.10 |
| Coding index | 63.9 | Not benchmarked |
| Independent measurements behind it | 6 | 0 |
| Cost per solved task | $0.42 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Terra better than Qwen3 Next 80B A3B Instruct for coding?
Only GPT-5.6 Terra has a coding score, 63.9 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 10.9x cheaper ($1.10 vs $12.00 per million) and work measured by Kagi LLM Benchmark or LMArena Text: medicine and healthcare, where it leads.
Which is cheaper, GPT-5.6 Terra or Qwen3 Next 80B A3B Instruct?
GPT-5.6 Terra costs $2.00 per million input tokens and $12.00 per million output tokens; Qwen3 Next 80B A3B Instruct costs $0.10 and $1.10. Cost per solved task is known for GPT-5.6 Terra only.
Which has the bigger context window, GPT-5.6 Terra or Qwen3 Next 80B A3B Instruct?
GPT-5.6 Terra, with 1.05M tokens against 262K.
How many benchmarks is the GPT-5.6 Terra vs Qwen3 Next 80B A3B Instruct comparison based on?
36 benchmarks have a result for both models as of October 22, 2026. GPT-5.6 Terra's coding index rests on 6 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.