GLM 5.3 vs GPT-5.6 Sol
GPT-5.6 Sol leads GLM 5.3 on coding, 73.5 to 70.6 on the Vibe Coding Index, and GLM 5.3 costs 4.5x less per output token ($4.40 vs $20.00 per million). Updated October 22, 2026; 97 benchmarks have a result for both.
GLM 5.3 or GPT-5.6 Sol: which to pick
Pick GPT-5.6 Sol for coding: it scores 73.5 on the Vibe Coding Index against 70.6 for GLM 5.3.
Pick GLM 5.3 for output tokens 4.5x cheaper ($4.40 vs $20.00 per million) and a lower cost per solved task ($0.36 against $1.20).
- Shared results
- 97
- GLM 5.3 only
- 3
- GPT-5.6 Sol only
- 42
- 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
GPT-5.6 Sol
GPT-5.6 Sol scores 73.5 against 70.6.
6 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 47.0 against 44.8.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3
GLM 5.3 leads, 53.1 against 50.2.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.6 Sol
GPT-5.6 Sol leads, 95.2% against 88.1%.
Same board, both measured
Knowledge
MMLU Pro
GPT-5.6 Sol
GPT-5.6 Sol leads, 89.1% against 86.8%.
Same board, both measured
Web design
Design Arena: websites
GPT-5.6 Sol
GPT-5.6 Sol leads, 1321 against 1295.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 has a Vals Index result, 59.7%.
Directional only
Long documents
Listed context window
GPT-5.6 Sol
GPT-5.6 Sol takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3
GLM 5.3 $0.0036, GPT-5.6 Sol $0.014 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3
GLM 5.3 $0.36, GPT-5.6 Sol $1.20.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.3 70.6 from 6 benchmarks, GPT-5.6 Sol 73.5 from 6 benchmarks.
- Widest category
- Math, 20.1 points apart on average over 6 shared benchmarks.
- Widest single gap
- LiveBench browser: GLM 5.3 0.0% against GPT-5.6 Sol 88.0%, 88.0 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $4.40 against GPT-5.6 Sol $20.00 per million output tokens, 4.5x apart.
- Context window
- GLM 5.3 takes 1.05M 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 5.3 | Bars | GPT-5.6 Sol | Ties |
|---|---|---|---|---|
| Coding | 6 | 16 | 1 tie | |
| Intelligence | 0 | 2 | ||
| Agentic work | 6 | 8 | ||
| Reasoning | 2 | 9 | ||
| Math | 2 | 4 | ||
| Knowledge | 3 | 6 | ||
| Web design | 0 | 9 | ||
| General | 15 | 8 | ||
| All shared boards | 34 | 62 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index616
| Benchmark | GLM 5.3 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| LiveBench coding | 82.8% | 89.7% | GPT-5.6 Sol |
| LMArena WebDev | 1621 | 1616 | GLM 5.3 |
| AA Coding Index | 74.8 | 77.4 | GPT-5.6 Sol |
| LiveCodeBench (Vals AI) | 80.5% | 82.6% | GPT-5.6 Sol |
| SWE-bench Verified (Vals AI) | 95.4% | 96.2% | GPT-5.6 Sol |
| Terminal-Bench 4 (Vals AI) | 38.9% | 37.9% | GLM 5.3 |
| Vibe Code Bench (Vals AI) | 78.1% | 80.5% | GPT-5.6 Sol |
| Code migration (Vals AI) | 44.2% | 52.9% | GPT-5.6 Sol |
| ProgramBench (Vals AI) | 1.5% | 1.5% | Tie |
| IOI (Vals AI) | 68.4% | 91.2% | GPT-5.6 Sol |
| Terminal-Bench 2.1 (Vals AI) | 71.5% | 85.8% | GPT-5.6 Sol |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | 20.0% | GPT-5.6 Sol |
| SkillsBench (Vals AI) | 47.5% | 54.1% | GPT-5.6 Sol |
| CyberBench (Vals AI) | 72.1% | 76.3% | GPT-5.6 Sol |
| SciCode | 59.0% | 57.8% | GLM 5.3 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 65.9% | Only GPT-5.6 Sol measured |
| FrontierCode | 40.1% | 47.5% | GPT-5.6 Sol |
| FrontierSWE | 30.2% | 32.2% | GPT-5.6 Sol |
| GSO-Bench | Not measured | 76.5% | Only GPT-5.6 Sol measured |
| WeirdML | 75.4% | 88.8% | GPT-5.6 Sol |
| MirrorCode | Not measured | 20.0% | Only GPT-5.6 Sol measured |
| ALE-Bench | 1317.4 | 2176.9 | GPT-5.6 Sol |
| LMArena Text: coding | 1497 | 1496 | GLM 5.3 |
| LMArena Text: software and IT | 1493 | 1489 | GLM 5.3 |
| LMArena WebDev: React | 1641 | 1620 | GLM 5.3 |
| LMArena WebDev: HTML | 1595 | 1618 | GPT-5.6 Sol |
| LMArena WebDev: from an image | Not measured | 1609 | Only GPT-5.6 Sol measured |
| 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 index02
| Benchmark | GLM 5.3 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | 47.0 | GPT-5.6 Sol |
| Epoch Capabilities Index | 156.0 | 162.8 | GPT-5.6 Sol |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index68
| Benchmark | GLM 5.3 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | 50.2 | GLM 5.3 |
| Design Arena: agents building web apps | Not measured | 1245 | Only GPT-5.6 Sol measured |
| Design Arena: agents building full-stack apps | Not measured | 1261 | Only GPT-5.6 Sol measured |
| Design Arena: agents building mobile apps | 1194 | 1248 | GPT-5.6 Sol |
| Design Arena: agents building Android apps | Not measured | 1240 | Only GPT-5.6 Sol measured |
| Design Arena: agents building Godot games | Not measured | 1260 | Only GPT-5.6 Sol measured |
| Design Arena: agents building games | Not measured | 1266 | Only GPT-5.6 Sol measured |
| Design Arena: HTML slides | 1189 | 1236 | GPT-5.6 Sol |
| Design Arena: PowerPoint slides | 1230 | 1215 | GLM 5.3 |
| LiveBench research | 73.2% | 54.5% | GLM 5.3 |
| LiveBench browser | 0.0% | 88.0% | GPT-5.6 Sol |
| LiveBench long agentic | 35.9% | 48.5% | GPT-5.6 Sol |
| SREBench (Vals AI) | 9.2% | 30.5% | GPT-5.6 Sol |
| Finance Agent (Vals AI) | 55.8% | 53.8% | GLM 5.3 |
| Tax Agent Bench (Vals AI) | 39.7% | 31.1% | GLM 5.3 |
| Legal Research Bench (Vals AI) | 49.0% | 48.1% | GLM 5.3 |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.6% | Not measured | Only GLM 5.3 measured |
| Excel Modeling Benchmark (Vals AI) | 56.3% | 72.3% | GPT-5.6 Sol |
| BioMysteryBench (Vals AI) | Not measured | 71.1% | Only GPT-5.6 Sol measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | 20.0% | GPT-5.6 Sol |
| 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 | Not measured | 85.1% | Only GPT-5.6 Sol measured |
| APEX-Agents | 56.6% | Not measured | Only GLM 5.3 measured |
| 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 | $8,164 | $9,619 | GPT-5.6 Sol |
| LMArena Search | Not measured | 1261 | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 11 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index29
| Benchmark | GLM 5.3 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | 95.2% | GPT-5.6 Sol |
| LiveBench reasoning | 85.8% | 91.7% | GPT-5.6 Sol |
| MysteryMechanism (Vals AI) | 23.0% | 33.3% | GPT-5.6 Sol |
| MMMU Pro (Vals AI) | Not measured | 88.8% | Only GPT-5.6 Sol measured |
| GPQA Diamond (Artificial Analysis run) | 91.7% | 94.1% | GPT-5.6 Sol |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 49.5% | GPT-5.6 Sol |
| GPQA Diamond (Epoch AI run) | 90.9% | 93.5% | GPT-5.6 Sol |
| 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 | 37.0% | 59.0% | GPT-5.6 Sol |
| Mystery game puzzles | 33.0% | 58.0% | GPT-5.6 Sol |
| EBR-bench | Not measured | 44.8% | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts | 1490 | 1483 | GLM 5.3 |
| LMArena Text: expert | 1514 | 1517 | GPT-5.6 Sol |
| LMArena Text: hard prompts in English | 1496 | 1477 | GLM 5.3 |
| LMArena Vision: diagrams | Not measured | 1305 | Only GPT-5.6 Sol measured |
| LMArena Vision: homework | Not measured | 1305 | Only GPT-5.6 Sol measured |
| 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 index24
| Benchmark | GLM 5.3 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| OTIS Mock AIME | 91.1% | 100.0% | GPT-5.6 Sol |
| FrontierMath, tiers 1 to 3 | 68.8% | 89.1% | GPT-5.6 Sol |
| FrontierMath, tier 4 | 29.3% | 82.9% | GPT-5.6 Sol |
| ProofBench | 49.0% | 83.0% | GPT-5.6 Sol |
| LMArena Text: math | 1495 | 1473 | GLM 5.3 |
| LMArena Text: mathematical work | 1497 | 1496 | GLM 5.3 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index36
| Benchmark | GLM 5.3 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | 89.1% | GPT-5.6 Sol |
| LegalBench (Vals AI) | 84.8% | 87.0% | GPT-5.6 Sol |
| CorpFin (Vals AI) | Not measured | 64.4% | Only GPT-5.6 Sol measured |
| TaxEval (Vals AI) | 72.4% | 74.8% | GPT-5.6 Sol |
| MortgageTax (Vals AI) | Not measured | 67.3% | Only GPT-5.6 Sol measured |
| MedCode (Vals AI) | 42.9% | 44.0% | GPT-5.6 Sol |
| SimpleQA Verified | 41.0% | 69.7% | GPT-5.6 Sol |
| LMArena Text: science | 1498 | 1455 | GLM 5.3 |
| LMArena Text: medicine and healthcare | 1481 | 1431 | GLM 5.3 |
| LMArena Text: legal and government | 1473 | 1476 | GPT-5.6 Sol |
| LMArena Text: business and finance | 1459 | 1457 | GLM 5.3 |
| 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 index09
| Benchmark | GLM 5.3 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | 1321 | GPT-5.6 Sol |
| Design Arena: UI components | 1314 | 1335 | GPT-5.6 Sol |
| Design Arena: game dev | 1311 | 1367 | GPT-5.6 Sol |
| Design Arena: data viz | 1260 | 1322 | GPT-5.6 Sol |
| Design Arena: 3D | 1332 | 1384 | GPT-5.6 Sol |
| Design Arena: all code categories | 1304 | 1341 | GPT-5.6 Sol |
| Design Arena: SVG | 1294 | 1314 | GPT-5.6 Sol |
| Design Arena: ASCII art | 1208 | 1304 | GPT-5.6 Sol |
| LiveBench design | 77.3% | 81.6% | GPT-5.6 Sol |
GeneralLike-for-likePublic boards, shown as published, kept out of the index158
| Benchmark | GLM 5.3 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| Vals Index | 59.7% | Not measured | Only GLM 5.3 measured |
| LiveBench data analysis | 70.2% | 79.8% | GPT-5.6 Sol |
| Public Benefits Bench (Vals AI) | 68.5% | 66.5% | GLM 5.3 |
| SAGE (Vals AI) | Not measured | 52.6% | Only GPT-5.6 Sol measured |
| MedScribe (Vals AI) | 88.8% | 85.2% | GLM 5.3 |
| Vals Multimodal Index | Not measured | 72.6% | Only GPT-5.6 Sol measured |
| IFBench | Not measured | 72.7% | Only GPT-5.6 Sol measured |
| Long context reasoning (AA-LCR) | 79.7% | 84.0% | GPT-5.6 Sol |
| DTBench | 87.7% | 95.7% | GPT-5.6 Sol |
| LMCA | 55.5% | 58.5% | GPT-5.6 Sol |
| 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 | 1474 | 1457 | GLM 5.3 |
| LMArena Text: instruction following | 1479 | 1483 | GPT-5.6 Sol |
| LMArena Text: creative writing | 1458 | 1449 | GLM 5.3 |
| LMArena Vision | Not measured | 1281 | Only GPT-5.6 Sol measured |
| LMArena Text: English | 1482 | 1457 | GLM 5.3 |
| LMArena Text: non-English | 1459 | 1451 | GLM 5.3 |
| LMArena Text: Chinese | 1532 | 1525 | GLM 5.3 |
| LMArena Text: German | 1488 | 1476 | GLM 5.3 |
| LMArena Text: Japanese | 1460 | 1472 | GPT-5.6 Sol |
| LMArena Text: Korean | 1460 | 1442 | GLM 5.3 |
| LMArena Text: French | 1503 | 1472 | GLM 5.3 |
| LMArena Text: Spanish | 1462 | 1443 | GLM 5.3 |
| LMArena Text: Russian | 1469 | 1471 | GPT-5.6 Sol |
| LMArena Text: Polish | 1466 | 1459 | GLM 5.3 |
| LMArena Text: multi-turn | 1472 | 1461 | GLM 5.3 |
| LMArena Text: longer queries | 1483 | 1481 | GLM 5.3 |
| LMArena Text: writing and language | 1469 | 1462 | GLM 5.3 |
| LMArena Text: entertainment and media | 1447 | 1450 | GPT-5.6 Sol |
| LMArena Vision: English | Not measured | 1280 | Only GPT-5.6 Sol measured |
| LMArena Vision: Chinese | Not measured | 1313 | Only GPT-5.6 Sol measured |
| LMArena Vision: reading text | Not measured | 1290 | Only GPT-5.6 Sol measured |
| LMArena Vision: humor | Not measured | 1246 | Only GPT-5.6 Sol measured |
| LMArena Vision: creative writing | Not measured | 1283 | Only GPT-5.6 Sol measured |
| LMArena Document | Not measured | 1483 | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 10 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 | 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 5.3 costs less
Repository review
50K input + 3K output tokens
GLM 5.3 costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 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 5.3 | OpenAI GPT-5.6 Sol |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 18, 2026 | July 9, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $1.40 | $4.00 |
| Output $/Mtok | $4.40 | $20.00 |
| Coding index | 70.6 | 73.5 |
| Independent measurements behind it | 6 | 6 |
| Cost per solved task | $0.36 | $1.20 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than GPT-5.6 Sol for coding?
Pick GPT-5.6 Sol for coding: it scores 73.5 on the Vibe Coding Index against 70.6 for GLM 5.3. Pick GLM 5.3 for output tokens 4.5x cheaper ($4.40 vs $20.00 per million) and a lower cost per solved task ($0.36 against $1.20).
Which is cheaper, GLM 5.3 or GPT-5.6 Sol?
GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; GPT-5.6 Sol costs $4.00 and $20.00. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and GPT-5.6 Sol $1.20.
Which has the bigger context window, GLM 5.3 or GPT-5.6 Sol?
GPT-5.6 Sol, with 1.05M tokens against 1.05M.
How many benchmarks is the GLM 5.3 vs GPT-5.6 Sol comparison based on?
97 benchmarks have a result for both models as of October 22, 2026. GLM 5.3's coding index rests on 6 independent benchmarks and GPT-5.6 Sol's on 6. Where only one model has a result, the page says so instead of naming a winner.