GLM 5.3 vs GPT-6.1 Terra
GPT-6.1 Terra leads GLM 5.3 on coding, 77.4 to 68.5 on the Vibe Coding Index, at about the same output price ($5.00 vs $4.40 per million). Updated October 29, 2026; 26 benchmarks have a result for both.
GLM 5.3 or GPT-6.1 Terra: which to pick
Pick GPT-6.1 Terra for coding: it scores 77.4 on the Vibe Coding Index against 68.5 for GLM 5.3, though on 4 benchmarks against 5.
Pick GLM 5.3 for output tokens 1.1x cheaper ($4.40 vs $5.00 per million) and work measured by SciCode or Tax Agent Bench (Vals AI), where it leads.
- Shared results
- 26
- GLM 5.3 only
- 70
- GPT-6.1 Terra only
- 10
- 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-6.1 Terra
GPT-6.1 Terra scores 77.4 against 68.5.
5 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-6.1 Terra
GPT-6.1 Terra leads, 51.0 against 31.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 has a AA Agentic Index result, 53.1.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.3 has a GPQA Diamond result, 88.1%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 has a MMLU Pro result, 86.8%.
Directional only
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1291 against 1283.
Same board, both measured
General
Vals Index
GPT-6.1 Terra
GPT-6.1 Terra leads, 60.5% against 59.7%.
Same board, both measured
Long documents
Listed context window
GPT-6.1 Terra
GPT-6.1 Terra takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-6.1 Terra
GLM 5.3 $0.0036, GPT-6.1 Terra $0.0035 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 has the number, $0.36.
Directional only
At a glance
- Coding index
- GLM 5.3 68.5 from 5 benchmarks, GPT-6.1 Terra 77.4 from 4 benchmarks.
- Widest category
- Math, 31.5 points apart on average over 3 shared benchmarks.
- Widest single gap
- FrontierMath, tier 4: GLM 5.3 29.3% against GPT-6.1 Terra 92.7%, 63.4 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $4.40 against GPT-6.1 Terra $5.00 per million output tokens, 1.1x apart.
- Context window
- GLM 5.3 takes 1.05M tokens, GPT-6.1 Terra 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-6.1 Terra | Ties |
|---|---|---|---|---|
| Coding | 1 | 6 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 3 | 1 | ||
| Reasoning | 0 | 4 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 1 | ||
| Web design | 3 | 1 | ||
| General | 0 | 2 | ||
| All shared boards | 7 | 19 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index16
| Benchmark | GLM 5.3 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 94.3% | Only GPT-6.1 Terra measured |
| LMArena WebDev | 1623 | 1673 | GPT-6.1 Terra |
| AA Coding Index | 74.8 | Not measured | Only GLM 5.3 measured |
| LiveCodeBench (Vals AI) | 80.5% | Not measured | Only GLM 5.3 measured |
| SWE-bench Verified (Vals AI) | 95.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 4 (Vals AI) | 38.9% | 49.5% | GPT-6.1 Terra |
| Vibe Code Bench (Vals AI) | 78.1% | 84.0% | GPT-6.1 Terra |
| Code migration (Vals AI) | 44.2% | Not measured | Only GLM 5.3 measured |
| ProgramBench (Vals AI) | 1.5% | Not measured | Only GLM 5.3 measured |
| IOI (Vals AI) | 68.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 2.1 (Vals AI) | 71.5% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GLM 5.3 measured |
| SkillsBench (Vals AI) | 47.5% | Not measured | Only GLM 5.3 measured |
| CyberBench (Vals AI) | 72.1% | Not measured | Only GLM 5.3 measured |
| SciCode | 59.0% | 55.1% | GLM 5.3 |
| FrontierCode | 40.1% | Not measured | Only GLM 5.3 measured |
| FrontierSWE | 30.2% | Not measured | Only GLM 5.3 measured |
| WeirdML | 75.4% | Not measured | Only GLM 5.3 measured |
| ALE-Bench | 1317.4 | 2608.8 | GPT-6.1 Terra |
| LMArena Text: coding | 1497 | Not measured | Only GLM 5.3 measured |
| LMArena Text: software and IT | 1494 | Not measured | Only GLM 5.3 measured |
| LMArena WebDev: React | 1643 | 1657 | GPT-6.1 Terra |
| LMArena WebDev: HTML | 1597 | 1719 | GPT-6.1 Terra |
| SWE-Bench Pro V2 | 95.6% | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5.3 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| AA Intelligence Index | 31.7 | 51.0 | GPT-6.1 Terra |
| Epoch Capabilities Index | 156.0 | Not measured | Only GLM 5.3 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 5.3 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | Not measured | Only GLM 5.3 measured |
| Design Arena: agents building mobile apps | 1192 | Not measured | Only GLM 5.3 measured |
| Design Arena: HTML slides | 1189 | Not measured | Only GLM 5.3 measured |
| Design Arena: PowerPoint slides | 1214 | Not measured | Only GLM 5.3 measured |
| LiveBench research | Not measured | 73.9% | Only GPT-6.1 Terra measured |
| LiveBench browser | Not measured | 93.1% | Only GPT-6.1 Terra measured |
| LiveBench long agentic | Not measured | 48.6% | Only GPT-6.1 Terra measured |
| SREBench (Vals AI) | 9.2% | Not measured | Only GLM 5.3 measured |
| Finance Agent (Vals AI) | 55.8% | Not measured | Only GLM 5.3 measured |
| Tax Agent Bench (Vals AI) | 39.7% | 15.2% | GLM 5.3 |
| Legal Research Bench (Vals AI) | 49.0% | 26.0% | GLM 5.3 |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.6% | 1.7% | GLM 5.3 |
| Excel Modeling Benchmark (Vals AI) | 56.3% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only GLM 5.3 measured |
| APEX-Agents | 56.6% | 68.3% | GPT-6.1 Terra |
| Vending-Bench 2 | $8,164 | Not measured | Only GLM 5.3 measured |
| MCP Atlas | 84.2% | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 5.3 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | Not measured | Only GLM 5.3 measured |
| LiveBench reasoning | Not measured | 88.7% | Only GPT-6.1 Terra measured |
| MysteryMechanism (Vals AI) | 23.0% | Not measured | Only GLM 5.3 measured |
| GPQA Diamond (Artificial Analysis run) | 91.7% | Not measured | Only GLM 5.3 measured |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 46.2% | GPT-6.1 Terra |
| GPQA Diamond (Epoch AI run) | 90.9% | 94.3% | GPT-6.1 Terra |
| Chess puzzles | 37.0% | Not measured | Only GLM 5.3 measured |
| Mystery game puzzles | 33.0% | 74.0% | GPT-6.1 Terra |
| EBR-bench | Not measured | 78.1% | Only GPT-6.1 Terra measured |
| LMArena Text: hard prompts | 1490 | Not measured | Only GLM 5.3 measured |
| LMArena Text: expert | 1517 | Not measured | Only GLM 5.3 measured |
| LMArena Text: hard prompts in English | 1497 | Not measured | Only GLM 5.3 measured |
| Humanity's Last Exam (Scale run) | 16.4% | 35.7% | GPT-6.1 Terra |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GLM 5.3 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| OTIS Mock AIME | 91.1% | 100.0% | GPT-6.1 Terra |
| FrontierMath, tiers 1 to 3 | 68.8% | 90.9% | GPT-6.1 Terra |
| FrontierMath, tier 4 | 29.3% | 92.7% | GPT-6.1 Terra |
| ProofBench | 49.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: math | 1497 | Not measured | Only GLM 5.3 measured |
| LMArena Text: mathematical work | 1501 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5.3 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | Not measured | Only GLM 5.3 measured |
| LegalBench (Vals AI) | 84.8% | Not measured | Only GLM 5.3 measured |
| TaxEval (Vals AI) | 72.4% | Not measured | Only GLM 5.3 measured |
| MedCode (Vals AI) | 42.9% | Not measured | Only GLM 5.3 measured |
| SimpleQA Verified | 41.0% | 57.3% | GPT-6.1 Terra |
| LMArena Text: science | 1498 | Not measured | Only GLM 5.3 measured |
| LMArena Text: medicine and healthcare | 1483 | Not measured | Only GLM 5.3 measured |
| LMArena Text: legal and government | 1473 | Not measured | Only GLM 5.3 measured |
| LMArena Text: business and finance | 1461 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 11 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 5.3 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| Design Arena: websites | 1291 | 1283 | GLM 5.3 |
| Design Arena: UI components | 1302 | 1277 | GLM 5.3 |
| Design Arena: game dev | 1306 | 1348 | GPT-6.1 Terra |
| Design Arena: data viz | 1263 | Not measured | Only GLM 5.3 measured |
| Design Arena: 3D | 1328 | Not measured | Only GLM 5.3 measured |
| Design Arena: all code categories | 1299 | 1295 | GLM 5.3 |
| Design Arena: SVG | 1287 | Not measured | Only GLM 5.3 measured |
| Design Arena: ASCII art | 1208 | Not measured | Only GLM 5.3 measured |
| LiveBench design | Not measured | 82.3% | Only GPT-6.1 Terra measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5.3 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| Vals Index | 59.7% | 60.5% | GPT-6.1 Terra |
| LiveBench data analysis | Not measured | 82.3% | Only GPT-6.1 Terra measured |
| Public Benefits Bench (Vals AI) | 68.5% | Not measured | Only GLM 5.3 measured |
| MedScribe (Vals AI) | 88.8% | Not measured | Only GLM 5.3 measured |
| Long context reasoning (AA-LCR) | 79.7% | 82.7% | GPT-6.1 Terra |
| DTBench | 87.7% | Not measured | Only GLM 5.3 measured |
| LMCA | 55.5% | Not measured | Only GLM 5.3 measured |
| Furniture assembly | Not measured | 79.2% | Only GPT-6.1 Terra measured |
| LMArena Text | 1474 | Not measured | Only GLM 5.3 measured |
| LMArena Text: instruction following | 1481 | Not measured | Only GLM 5.3 measured |
| LMArena Text: creative writing | 1459 | Not measured | Only GLM 5.3 measured |
| LMArena Text: English | 1483 | Not measured | Only GLM 5.3 measured |
| LMArena Text: non-English | 1460 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Chinese | 1532 | Not measured | Only GLM 5.3 measured |
| LMArena Text: German | 1491 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Japanese | 1458 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Korean | 1457 | Not measured | Only GLM 5.3 measured |
| LMArena Text: French | 1496 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Spanish | 1466 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Russian | 1471 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Polish | 1462 | Not measured | Only GLM 5.3 measured |
| LMArena Text: multi-turn | 1470 | Not measured | Only GLM 5.3 measured |
| LMArena Text: longer queries | 1483 | Not measured | Only GLM 5.3 measured |
| LMArena Text: writing and language | 1470 | Not measured | Only GLM 5.3 measured |
| LMArena Text: entertainment and media | 1448 | Not measured | Only GLM 5.3 measured |
| MultiChallenge | Not measured | 74.2% | Only GPT-6.1 Terra measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 | GPT-6.1 Terra | 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
GPT-6.1 Terra costs less
Repository review
50K input + 3K output tokens
GPT-6.1 Terra costs less
Agent loop
200K input + 10K output tokens
GPT-6.1 Terra 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-6.1 Terra |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 18, 2026 | October 26, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $1.40 | $1.00 |
| Output $/Mtok | $4.40 | $5.00 |
| Coding index | 68.5 | 77.4 |
| Independent measurements behind it | 5 | 4 |
| Cost per solved task | $0.36 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than GPT-6.1 Terra for coding?
Pick GPT-6.1 Terra for coding: it scores 77.4 on the Vibe Coding Index against 68.5 for GLM 5.3, though on 4 benchmarks against 5. Pick GLM 5.3 for output tokens 1.1x cheaper ($4.40 vs $5.00 per million) and work measured by SciCode or Tax Agent Bench (Vals AI), where it leads.
Which is cheaper, GLM 5.3 or GPT-6.1 Terra?
GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; GPT-6.1 Terra costs $1.00 and $5.00. Cost per solved task is known for GLM 5.3 only.
Which has the bigger context window, GLM 5.3 or GPT-6.1 Terra?
GPT-6.1 Terra, with 1.05M tokens against 1.05M.
How many benchmarks is the GLM 5.3 vs GPT-6.1 Terra comparison based on?
26 benchmarks have a result for both models as of October 29, 2026. GLM 5.3's coding index rests on 5 independent benchmarks and GPT-6.1 Terra's on 4. Where only one model has a result, the page says so instead of naming a winner.