GLM 5V Turbo vs GPT-5.6 Sol
GPT-5.6 Sol leads GLM 5V Turbo on the Artificial Analysis Intelligence Index, 47.0 to 23.5, and GLM 5V Turbo costs 5.0x less per output token ($4.00 vs $20.00 per million). Updated October 22, 2026; 63 benchmarks have a result for both.
GLM 5V Turbo or GPT-5.6 Sol: which to pick
Only GPT-5.6 Sol has a coding score, 73.5 on the Vibe Coding Index; GLM 5V Turbo has none, so the two cannot be ranked on coding.
Pick GLM 5V Turbo for output tokens 5.0x cheaper ($4.00 vs $20.00 per million) and work measured by Design Arena: agents building Android apps or τ²-Bench Telecom, where it leads.
- Shared results
- 63
- GLM 5V Turbo only
- 2
- GPT-5.6 Sol only
- 76
- Like-for-like categories
- 8 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 Sol has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 47.0 against 23.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GPT-5.6 Sol has a AA Agentic Index result, 50.2.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.6 Sol has a GPQA Diamond result, 95.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.6 Sol has a MMLU Pro result, 89.1%.
Directional only
Web design
Design Arena: websites
GPT-5.6 Sol
GPT-5.6 Sol leads, 1321 against 1230.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Sol
GPT-5.6 Sol takes 1.05M tokens against 203K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5V Turbo
GLM 5V Turbo $0.0032, GPT-5.6 Sol $0.014 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.6 Sol has the number, $1.20.
Directional only
At a glance
- Coding index
- GLM 5V Turbo not benchmarked, GPT-5.6 Sol 73.5 from 6 benchmarks.
- Widest category
- Coding, 25.6 points apart on average over 7 shared benchmarks.
- Widest single gap
- LMArena WebDev: HTML: GLM 5V Turbo 1356 against GPT-5.6 Sol 1618, 40.6 points apart on a 0 to 100 scale.
- Output price
- GLM 5V Turbo $4.00 against GPT-5.6 Sol $20.00 per million output tokens, 5.0x apart.
- Context window
- GLM 5V Turbo takes 203K tokens, GPT-5.6 Sol 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 5V Turbo | Bars | GPT-5.6 Sol | Ties |
|---|---|---|---|---|
| Coding | 0 | 7 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 2 | 7 | ||
| Reasoning | 0 | 7 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 3 | ||
| Web design | 0 | 8 | ||
| General | 3 | 22 | ||
| All shared boards | 6 | 57 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index07
| Benchmark | GLM 5V Turbo | GPT-5.6 Sol | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 89.7% | Only GPT-5.6 Sol measured |
| LMArena WebDev | 1402 | 1616 | GPT-5.6 Sol |
| AA Coding Index | Not measured | 77.4 | Only GPT-5.6 Sol measured |
| LiveCodeBench (Vals AI) | Not measured | 82.6% | Only GPT-5.6 Sol measured |
| SWE-bench Verified (Vals AI) | Not measured | 96.2% | Only GPT-5.6 Sol measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 37.9% | Only GPT-5.6 Sol measured |
| Vibe Code Bench (Vals AI) | Not measured | 80.5% | Only GPT-5.6 Sol measured |
| Code migration (Vals AI) | Not measured | 52.9% | Only GPT-5.6 Sol measured |
| ProgramBench (Vals AI) | Not measured | 1.5% | Only GPT-5.6 Sol measured |
| IOI (Vals AI) | Not measured | 91.2% | Only GPT-5.6 Sol measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 85.8% | Only GPT-5.6 Sol measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 20.0% | Only GPT-5.6 Sol measured |
| SkillsBench (Vals AI) | Not measured | 54.1% | Only GPT-5.6 Sol measured |
| CyberBench (Vals AI) | Not measured | 76.3% | Only GPT-5.6 Sol measured |
| SciCode | Not measured | 57.8% | Only GPT-5.6 Sol measured |
| Terminal-Bench Hard (Artificial Analysis run) | 32.6% | 65.9% | GPT-5.6 Sol |
| FrontierCode | Not measured | 47.5% | Only GPT-5.6 Sol measured |
| FrontierSWE | Not measured | 32.2% | Only GPT-5.6 Sol measured |
| GSO-Bench | Not measured | 76.5% | Only GPT-5.6 Sol measured |
| WeirdML | Not measured | 88.8% | Only GPT-5.6 Sol measured |
| MirrorCode | Not measured | 20.0% | Only GPT-5.6 Sol measured |
| ALE-Bench | Not measured | 2176.9 | Only GPT-5.6 Sol measured |
| LMArena Text: coding | 1466 | 1496 | GPT-5.6 Sol |
| LMArena Text: software and IT | 1460 | 1489 | GPT-5.6 Sol |
| LMArena WebDev: React | 1386 | 1620 | GPT-5.6 Sol |
| LMArena WebDev: HTML | 1356 | 1618 | GPT-5.6 Sol |
| LMArena WebDev: from an image | 1425 | 1609 | GPT-5.6 Sol |
| SciCode | Not measured | 10.8% | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 11 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5V Turbo | GPT-5.6 Sol | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.5 | 47.0 | GPT-5.6 Sol |
| Epoch Capabilities Index | Not measured | 162.8 | Only GPT-5.6 Sol measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index27
| Benchmark | GLM 5V Turbo | GPT-5.6 Sol | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 50.2 | Only GPT-5.6 Sol measured |
| Design Arena: agents building web apps | 1124 | 1245 | GPT-5.6 Sol |
| Design Arena: agents building full-stack apps | 1149 | 1261 | GPT-5.6 Sol |
| Design Arena: agents building mobile apps | 1134 | 1248 | GPT-5.6 Sol |
| Design Arena: agents building Android apps | 1267 | 1240 | GLM 5V Turbo |
| Design Arena: agents building Godot games | 957 | 1260 | GPT-5.6 Sol |
| Design Arena: agents building games | 1102 | 1266 | GPT-5.6 Sol |
| Design Arena: HTML slides | 1070 | 1236 | GPT-5.6 Sol |
| Design Arena: PowerPoint slides | 1127 | 1215 | GPT-5.6 Sol |
| LiveBench research | Not measured | 54.5% | Only GPT-5.6 Sol measured |
| LiveBench browser | Not measured | 88.0% | Only GPT-5.6 Sol measured |
| LiveBench long agentic | Not measured | 48.5% | Only GPT-5.6 Sol measured |
| SREBench (Vals AI) | Not measured | 30.5% | Only GPT-5.6 Sol measured |
| Finance Agent (Vals AI) | Not measured | 53.8% | Only GPT-5.6 Sol measured |
| Tax Agent Bench (Vals AI) | Not measured | 31.1% | Only GPT-5.6 Sol measured |
| Legal Research Bench (Vals AI) | Not measured | 48.1% | Only GPT-5.6 Sol measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 72.3% | Only GPT-5.6 Sol measured |
| BioMysteryBench (Vals AI) | Not measured | 71.1% | Only GPT-5.6 Sol measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 20.0% | Only GPT-5.6 Sol measured |
| Vals Science Index | Not measured | 41.5% | Only GPT-5.6 Sol measured |
| Time Horizon Index (Vals AI) | Not measured | 23.8% | Only GPT-5.6 Sol measured |
| CUA-bench (Vals AI) | Not measured | 8.3% | Only GPT-5.6 Sol measured |
| τ²-Bench Telecom | 98.5% | 85.1% | GLM 5V Turbo |
| OSWorld 2.0 | Not measured | 27.3% | Only GPT-5.6 Sol measured |
| BALROG | Not measured | 60.0% | Only GPT-5.6 Sol measured |
| PostTrainBench | Not measured | 36.2% | Only GPT-5.6 Sol measured |
| Vending-Bench 2 | Not measured | $9,619 | Only GPT-5.6 Sol measured |
| LMArena Search | Not measured | 1261 | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 13 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GLM 5V Turbo | GPT-5.6 Sol | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 95.2% | Only GPT-5.6 Sol measured |
| LiveBench reasoning | Not measured | 91.7% | Only GPT-5.6 Sol measured |
| MysteryMechanism (Vals AI) | Not measured | 33.3% | Only GPT-5.6 Sol measured |
| MMMU Pro (Vals AI) | Not measured | 88.8% | Only GPT-5.6 Sol measured |
| GPQA Diamond (Artificial Analysis run) | 80.9% | 94.1% | GPT-5.6 Sol |
| Humanity's Last Exam (Artificial Analysis run) | 17.1% | 49.5% | GPT-5.6 Sol |
| GPQA Diamond (Epoch AI run) | Not measured | 93.5% | Only GPT-5.6 Sol measured |
| ARC-AGI | Not measured | 97.5% | Only GPT-5.6 Sol measured |
| ARC-AGI-2 | Not measured | 92.5% | Only GPT-5.6 Sol measured |
| SimpleBench | Not measured | 64.8% | Only GPT-5.6 Sol measured |
| Chess puzzles | Not measured | 59.0% | Only GPT-5.6 Sol measured |
| Mystery game puzzles | Not measured | 58.0% | Only GPT-5.6 Sol measured |
| EBR-bench | Not measured | 44.8% | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts | 1443 | 1483 | GPT-5.6 Sol |
| LMArena Text: expert | 1453 | 1517 | GPT-5.6 Sol |
| LMArena Text: hard prompts in English | 1456 | 1477 | GPT-5.6 Sol |
| LMArena Vision: diagrams | 1286 | 1305 | GPT-5.6 Sol |
| LMArena Vision: homework | 1273 | 1305 | GPT-5.6 Sol |
| Kagi LLM Benchmark | Not measured | 67.0% | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 3 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5V Turbo | GPT-5.6 Sol | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 100.0% | Only GPT-5.6 Sol measured |
| FrontierMath, tiers 1 to 3 | Not measured | 89.1% | Only GPT-5.6 Sol measured |
| FrontierMath, tier 4 | Not measured | 82.9% | Only GPT-5.6 Sol measured |
| ProofBench | Not measured | 83.0% | Only GPT-5.6 Sol measured |
| LMArena Text: math | 1442 | 1473 | GPT-5.6 Sol |
| LMArena Text: mathematical work | 1447 | 1496 | GPT-5.6 Sol |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
| Benchmark | GLM 5V Turbo | GPT-5.6 Sol | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 89.1% | Only GPT-5.6 Sol measured |
| LegalBench (Vals AI) | Not measured | 87.0% | Only GPT-5.6 Sol measured |
| CorpFin (Vals AI) | Not measured | 64.4% | Only GPT-5.6 Sol measured |
| TaxEval (Vals AI) | Not measured | 74.8% | Only GPT-5.6 Sol measured |
| MortgageTax (Vals AI) | Not measured | 67.3% | Only GPT-5.6 Sol measured |
| MedCode (Vals AI) | Not measured | 44.0% | Only GPT-5.6 Sol measured |
| SimpleQA Verified | Not measured | 69.7% | Only GPT-5.6 Sol measured |
| LMArena Text: science | 1448 | 1455 | GPT-5.6 Sol |
| LMArena Text: medicine and healthcare | 1433 | 1431 | GLM 5V Turbo |
| LMArena Text: legal and government | 1436 | 1476 | GPT-5.6 Sol |
| LMArena Text: business and finance | 1435 | 1457 | GPT-5.6 Sol |
| FACTS Benchmark Suite | Not measured | 52.4% | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | GLM 5V Turbo | GPT-5.6 Sol | Leader |
|---|---|---|---|
| Design Arena: websites | 1230 | 1321 | GPT-5.6 Sol |
| Design Arena: UI components | 1210 | 1335 | GPT-5.6 Sol |
| Design Arena: game dev | 1218 | 1367 | GPT-5.6 Sol |
| Design Arena: data viz | 1202 | 1322 | GPT-5.6 Sol |
| Design Arena: 3D | 1208 | 1384 | GPT-5.6 Sol |
| Design Arena: all code categories | 1228 | 1341 | GPT-5.6 Sol |
| Design Arena: SVG | 1151 | 1314 | GPT-5.6 Sol |
| Design Arena: ASCII art | 1117 | 1304 | GPT-5.6 Sol |
| LiveBench design | Not measured | 81.6% | Only GPT-5.6 Sol measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index322
| Benchmark | GLM 5V Turbo | GPT-5.6 Sol | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 79.8% | Only GPT-5.6 Sol measured |
| Public Benefits Bench (Vals AI) | Not measured | 66.5% | Only GPT-5.6 Sol measured |
| SAGE (Vals AI) | Not measured | 52.6% | Only GPT-5.6 Sol measured |
| MedScribe (Vals AI) | Not measured | 85.2% | Only GPT-5.6 Sol measured |
| Vals Multimodal Index | Not measured | 72.6% | Only GPT-5.6 Sol measured |
| IFBench | 61.1% | 72.7% | GPT-5.6 Sol |
| Long context reasoning (AA-LCR) | 70.3% | 84.0% | GPT-5.6 Sol |
| DTBench | Not measured | 95.7% | Only GPT-5.6 Sol measured |
| LMCA | Not measured | 58.5% | Only GPT-5.6 Sol measured |
| Surface Evolver Bench | Not measured | 93.1% | Only GPT-5.6 Sol measured |
| Furniture assembly | Not measured | 56.7% | Only GPT-5.6 Sol measured |
| LMArena Text | 1437 | 1457 | GPT-5.6 Sol |
| LMArena Text: instruction following | 1423 | 1483 | GPT-5.6 Sol |
| LMArena Text: creative writing | 1417 | 1449 | GPT-5.6 Sol |
| LMArena Vision | 1264 | 1281 | GPT-5.6 Sol |
| LMArena Text: English | 1454 | 1457 | GPT-5.6 Sol |
| LMArena Text: non-English | 1420 | 1451 | GPT-5.6 Sol |
| LMArena Text: Chinese | 1491 | 1525 | GPT-5.6 Sol |
| LMArena Text: German | 1422 | 1476 | GPT-5.6 Sol |
| LMArena Text: Japanese | Not measured | 1472 | Only GPT-5.6 Sol measured |
| LMArena Text: Korean | 1397 | 1442 | GPT-5.6 Sol |
| LMArena Text: French | 1443 | 1472 | GPT-5.6 Sol |
| LMArena Text: Spanish | 1452 | 1443 | GLM 5V Turbo |
| LMArena Text: Russian | 1432 | 1471 | GPT-5.6 Sol |
| LMArena Text: Polish | 1432 | 1459 | GPT-5.6 Sol |
| LMArena Text: multi-turn | 1431 | 1461 | GPT-5.6 Sol |
| LMArena Text: longer queries | 1438 | 1481 | GPT-5.6 Sol |
| LMArena Text: writing and language | 1420 | 1462 | GPT-5.6 Sol |
| LMArena Text: entertainment and media | 1407 | 1450 | GPT-5.6 Sol |
| LMArena Vision: English | 1274 | 1280 | GPT-5.6 Sol |
| LMArena Vision: Chinese | 1329 | 1313 | GLM 5V Turbo |
| LMArena Vision: reading text | 1270 | 1290 | GPT-5.6 Sol |
| LMArena Vision: humor | 1268 | 1246 | GLM 5V Turbo |
| LMArena Vision: captioning | 1264 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: recognizing things | 1241 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: creative writing | 1252 | 1283 | GPT-5.6 Sol |
| LMArena Document | 1416 | 1483 | GPT-5.6 Sol |
| Neither model has a result on the other 9 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5V Turbo | GPT-5.6 Sol | 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 5V Turbo costs less
Repository review
50K input + 3K output tokens
GLM 5V Turbo costs less
Agent loop
200K input + 10K output tokens
GLM 5V Turbo 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 5V Turbo | OpenAI GPT-5.6 Sol |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | April 1, 2026 | July 9, 2026 |
| Context window | 203K | 1.05M |
| Input $/Mtok | $1.20 | $4.00 |
| Output $/Mtok | $4.00 | $20.00 |
| Coding index | Not benchmarked | 73.5 |
| Independent measurements behind it | 1 | 6 |
| Cost per solved task | Not computable | $1.20 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5V Turbo better than GPT-5.6 Sol for coding?
Only GPT-5.6 Sol has a coding score, 73.5 on the Vibe Coding Index; GLM 5V Turbo has none, so the two cannot be ranked on coding. Pick GLM 5V Turbo for output tokens 5.0x cheaper ($4.00 vs $20.00 per million) and work measured by Design Arena: agents building Android apps or τ²-Bench Telecom, where it leads.
Which is cheaper, GLM 5V Turbo or GPT-5.6 Sol?
GLM 5V Turbo costs $1.20 per million input tokens and $4.00 per million output tokens; GPT-5.6 Sol costs $4.00 and $20.00. Cost per solved task is known for GPT-5.6 Sol only.
Which has the bigger context window, GLM 5V Turbo or GPT-5.6 Sol?
GPT-5.6 Sol, with 1.05M tokens against 203K.
How many benchmarks is the GLM 5V Turbo vs GPT-5.6 Sol comparison based on?
63 benchmarks have a result for both models as of October 22, 2026. GLM 5V Turbo's coding index rests on 1 independent benchmark and GPT-5.6 Sol's on 6. Where only one model has a result, the page says so instead of naming a winner.