GLM 4.7 vs GPT-6.1 Sol
Updated October 21, 2026. 46 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-6.1 Sol for coding: it scores 88.7 on the Vibe Coding Index against 33.6 for GLM 4.7.
Pick GLM 4.7 when output tokens cost 5.1x less, or when what matters is LMArena Text: science or LMArena Text: medicine and healthcare, where it leads.
- Shared results
- 46
- GLM 4.7 only
- 34
- GPT-6.1 Sol only
- 49
- Like-for-like categories
- 8 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
GPT-6.1 Sol
GPT-6.1 Sol scores 88.7 against 33.6.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-6.1 Sol
GPT-6.1 Sol leads, 51.8 against 22.2.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 4.7 has a GPQA Diamond result, 80.0%.
Directional only
Math
AIME
No call
Only GLM 4.7 has a AIME result, 93.3%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.7 has a MMLU Pro result, 82.7%.
Directional only
Web design
Design Arena: websites
GPT-6.1 Sol
GPT-6.1 Sol leads, 1321 against 1226.
Same board, both measured
General
Vals Index
No call
Only GPT-6.1 Sol has a Vals Index result, 63.9%.
Directional only
Long documents
Listed context window
GPT-6.1 Sol
GPT-6.1 Sol takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7
GLM 4.7 $0.0015, GPT-6.1 Sol $0.0070 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 4.7 has the number, $0.29.
Directional only
At a glance
- Coding index
- GLM 4.7 33.6 from 1 benchmark, GPT-6.1 Sol 88.7 from 2 benchmarks.
- Widest category
- Math, 34.4 points apart on average over 4 shared benchmarks.
- Widest single gap
- ProofBench: GLM 4.7 6.0% against GPT-6.1 Sol 99.0%, 93.0 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $1.98 against GPT-6.1 Sol $10.00 per million output tokens, 5.1x apart.
- Context window
- GLM 4.7 takes 205K tokens, GPT-6.1 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 4.7 | Bars | GPT-6.1 Sol | Ties |
|---|---|---|---|---|
| Coding | 0 | 6 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 4 | ||
| Knowledge | 2 | 4 | ||
| Web design | 0 | 6 | ||
| General | 3 | 12 | ||
| All shared boards | 5 | 41 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index06
| Benchmark | GLM 4.7 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 96.0% | Only GPT-6.1 Sol measured |
| LMArena WebDev | 1435 | 1753 | GPT-6.1 Sol |
| AA Coding Index | 45.3 | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Vals AI) | 82.2% | Not measured | Only GLM 4.7 measured |
| SWE-bench Verified (Vals AI) | 69.4% | Not measured | Only GLM 4.7 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 55.0% | Only GPT-6.1 Sol measured |
| Vibe Code Bench (Vals AI) | Not measured | 88.9% | Only GPT-6.1 Sol measured |
| Code migration (Vals AI) | Not measured | 65.1% | Only GPT-6.1 Sol measured |
| ProgramBench (Vals AI) | Not measured | 3.0% | Only GPT-6.1 Sol measured |
| IOI (Vals AI) | Not measured | 96.9% | Only GPT-6.1 Sol measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 43.5% | Only GPT-6.1 Sol measured |
| CyberBench (Vals AI) | Not measured | 39.3% | Only GPT-6.1 Sol measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GLM 4.7 measured |
| SciCode | Not measured | 55.8% | Only GPT-6.1 Sol measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | Not measured | Only GLM 4.7 measured |
| Terminal-Bench | 33.4% | Not measured | Only GLM 4.7 measured |
| FrontierCode | Not measured | 50.2% | Only GPT-6.1 Sol measured |
| ALE-Bench | 399.5 | 2743.8 | GPT-6.1 Sol |
| LMArena Text: coding | 1454 | 1478 | GPT-6.1 Sol |
| LMArena Text: software and IT | 1453 | 1476 | GPT-6.1 Sol |
| LMArena WebDev: React | 1420 | 1754 | GPT-6.1 Sol |
| LMArena WebDev: HTML | 1447 | 1800 | GPT-6.1 Sol |
| LMArena WebDev: from an image | Not measured | 1702 | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.7 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | 51.8 | GPT-6.1 Sol |
| Epoch Capabilities Index | 143.6 | 168.4 | GPT-6.1 Sol |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.7 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1050 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building mobile apps | 1105 | 1190 | GPT-6.1 Sol |
| Design Arena: agents building Android apps | 1118 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Godot games | 1058 | Not measured | Only GLM 4.7 measured |
| Design Arena: spreadsheets | Not measured | 1250 | Only GPT-6.1 Sol measured |
| LiveBench research | Not measured | 83.5% | Only GPT-6.1 Sol measured |
| LiveBench browser | Not measured | 88.0% | Only GPT-6.1 Sol measured |
| LiveBench long agentic | Not measured | 73.0% | Only GPT-6.1 Sol measured |
| SREBench (Vals AI) | Not measured | 50.8% | Only GPT-6.1 Sol measured |
| Finance Agent (Vals AI) | Not measured | 52.0% | Only GPT-6.1 Sol measured |
| Tax Agent Bench (Vals AI) | Not measured | 21.8% | Only GPT-6.1 Sol measured |
| Legal Research Bench (Vals AI) | Not measured | 38.5% | Only GPT-6.1 Sol measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 6.3% | Only GPT-6.1 Sol measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 70.8% | Only GPT-6.1 Sol measured |
| BioMysteryBench (Vals AI) | Not measured | 79.6% | Only GPT-6.1 Sol measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 52.9% | Only GPT-6.1 Sol measured |
| Vals Science Index | Not measured | 59.6% | Only GPT-6.1 Sol measured |
| Robo-bench (Vals AI) | Not measured | 28.3% | Only GPT-6.1 Sol measured |
| τ²-Bench Telecom | 95.9% | Not measured | Only GLM 4.7 measured |
| APEX-Agents | Not measured | 60.0% | Only GPT-6.1 Sol measured |
| SpireClimb | Not measured | 24.7% | Only GPT-6.1 Sol measured |
| Vending-Bench 2 | $2,377 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 17 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GLM 4.7 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | Not measured | Only GLM 4.7 measured |
| LiveBench reasoning | Not measured | 92.6% | Only GPT-6.1 Sol measured |
| MysteryMechanism (Vals AI) | Not measured | 46.4% | Only GPT-6.1 Sol measured |
| GPQA Diamond (Artificial Analysis run) | 85.9% | Not measured | Only GLM 4.7 measured |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | 52.9% | GPT-6.1 Sol |
| GPQA Diamond (Epoch AI run) | 83.3% | 95.4% | GPT-6.1 Sol |
| ARC-AGI | Not measured | 98.5% | Only GPT-6.1 Sol measured |
| ARC-AGI-2 | Not measured | 94.2% | Only GPT-6.1 Sol measured |
| SimpleBench | 47.7% | Not measured | Only GLM 4.7 measured |
| Chess puzzles | 21.0% | 68.0% | GPT-6.1 Sol |
| Mystery game puzzles | Not measured | 80.0% | Only GPT-6.1 Sol measured |
| EBR-bench | Not measured | 54.3% | Only GPT-6.1 Sol measured |
| LMArena Text: hard prompts | 1443 | 1463 | GPT-6.1 Sol |
| LMArena Text: expert | 1424 | 1489 | GPT-6.1 Sol |
| LMArena Text: hard prompts in English | 1452 | 1465 | GPT-6.1 Sol |
| LMArena Vision: diagrams | Not measured | 1306 | Only GPT-6.1 Sol measured |
| LMArena Vision: homework | Not measured | 1356 | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 4.7 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| AIME | 93.3% | Not measured | Only GLM 4.7 measured |
| MGSM (Vals AI) | 88.2% | Not measured | Only GLM 4.7 measured |
| AA Math Index | 95.0 | Not measured | Only GLM 4.7 measured |
| AIME 2025 (Artificial Analysis run) | 95.0% | Not measured | Only GLM 4.7 measured |
| OTIS Mock AIME | 83.3% | 100.0% | GPT-6.1 Sol |
| FrontierMath, tiers 1 to 3 | Not measured | 93.7% | Only GPT-6.1 Sol measured |
| FrontierMath, tier 4 | Not measured | 100.0% | Only GPT-6.1 Sol measured |
| ProofBench | 6.0% | 99.0% | GPT-6.1 Sol |
| LMArena Text: math | 1424 | 1489 | GPT-6.1 Sol |
| LMArena Text: mathematical work | 1411 | 1513 | GPT-6.1 Sol |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | GLM 4.7 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | Not measured | Only GLM 4.7 measured |
| LegalBench (Vals AI) | 83.4% | Not measured | Only GLM 4.7 measured |
| CaseLaw (Vals AI) | 54.9% | Not measured | Only GLM 4.7 measured |
| CorpFin (Vals AI) | 46.4% | Not measured | Only GLM 4.7 measured |
| TaxEval (Vals AI) | 68.8% | Not measured | Only GLM 4.7 measured |
| MedQA (Vals AI) | 93.7% | Not measured | Only GLM 4.7 measured |
| MedCode (Vals AI) | 32.8% | 48.8% | GPT-6.1 Sol |
| MMLU Pro (Artificial Analysis run) | 85.6% | Not measured | Only GLM 4.7 measured |
| SimpleQA Verified | 32.2% | 73.9% | GPT-6.1 Sol |
| LMArena Text: science | 1457 | 1444 | GLM 4.7 |
| LMArena Text: medicine and healthcare | 1469 | 1433 | GLM 4.7 |
| LMArena Text: legal and government | 1444 | 1471 | GPT-6.1 Sol |
| LMArena Text: business and finance | 1429 | 1445 | GPT-6.1 Sol |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GLM 4.7 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| Design Arena: websites | 1226 | 1321 | GPT-6.1 Sol |
| Design Arena: UI components | 1199 | 1321 | GPT-6.1 Sol |
| Design Arena: game dev | 1189 | 1389 | GPT-6.1 Sol |
| Design Arena: data viz | 1195 | 1278 | GPT-6.1 Sol |
| Design Arena: 3D | 1191 | 1461 | GPT-6.1 Sol |
| Design Arena: all code categories | 1217 | 1345 | GPT-6.1 Sol |
| Design Arena: SVG | 1142 | Not measured | Only GLM 4.7 measured |
| Design Arena: ASCII art | 1165 | Not measured | Only GLM 4.7 measured |
| LiveBench design | Not measured | 82.1% | Only GPT-6.1 Sol measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index312
| Benchmark | GLM 4.7 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| Vals Index | Not measured | 63.9% | Only GPT-6.1 Sol measured |
| LiveBench data analysis | Not measured | 82.7% | Only GPT-6.1 Sol measured |
| Public Benefits Bench (Vals AI) | Not measured | 59.3% | Only GPT-6.1 Sol measured |
| SAGE (Vals AI) | Not measured | 46.5% | Only GPT-6.1 Sol measured |
| MedScribe (Vals AI) | 68.6% | 86.5% | GPT-6.1 Sol |
| IFBench | 67.9% | Not measured | Only GLM 4.7 measured |
| Long context reasoning (AA-LCR) | 71.0% | 84.0% | GPT-6.1 Sol |
| DTBench | Not measured | 96.5% | Only GPT-6.1 Sol measured |
| LMCA | Not measured | 62.8% | Only GPT-6.1 Sol measured |
| CL-bench | 15.9% | Not measured | Only GLM 4.7 measured |
| Furniture assembly | Not measured | 80.0% | Only GPT-6.1 Sol measured |
| CL-bench Life | 10.9% | Not measured | Only GLM 4.7 measured |
| LMArena Text | 1435 | 1443 | GPT-6.1 Sol |
| LMArena Text: instruction following | 1412 | 1466 | GPT-6.1 Sol |
| LMArena Text: creative writing | 1401 | 1425 | GPT-6.1 Sol |
| LMArena Vision | Not measured | 1288 | Only GPT-6.1 Sol measured |
| LMArena Text: English | 1453 | 1450 | GLM 4.7 |
| LMArena Text: non-English | 1417 | 1433 | GPT-6.1 Sol |
| LMArena Text: Chinese | 1495 | 1487 | GLM 4.7 |
| LMArena Text: German | 1426 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Japanese | 1439 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Korean | 1399 | Not measured | Only GLM 4.7 measured |
| LMArena Text: French | 1432 | 1460 | GPT-6.1 Sol |
| LMArena Text: Spanish | 1434 | 1439 | GPT-6.1 Sol |
| LMArena Text: Russian | 1423 | 1457 | GPT-6.1 Sol |
| LMArena Text: Polish | 1435 | Not measured | Only GLM 4.7 measured |
| LMArena Text: multi-turn | 1446 | 1442 | GLM 4.7 |
| LMArena Text: longer queries | 1432 | 1458 | GPT-6.1 Sol |
| LMArena Text: writing and language | 1411 | 1444 | GPT-6.1 Sol |
| LMArena Text: entertainment and media | 1400 | 1426 | GPT-6.1 Sol |
| LMArena Vision: English | Not measured | 1274 | Only GPT-6.1 Sol measured |
| LMArena Vision: reading text | Not measured | 1289 | Only GPT-6.1 Sol measured |
| LMArena Vision: creative writing | Not measured | 1301 | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 | GPT-6.1 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 4.7 costs less
Repository review
50K input + 3K output tokens
GLM 4.7 costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 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 4.7 | OpenAI GPT-6.1 Sol |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | December 22, 2025 | September 29, 2026 |
| Context window | 205K | 1.05M |
| Input $/Mtok | $0.54 | $2.00 |
| Output $/Mtok | $1.98 | $10.00 |
| Coding index | 33.6 | 88.7 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $0.29 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than GPT-6.1 Sol for coding?
Pick GPT-6.1 Sol for coding: it scores 88.7 on the Vibe Coding Index against 33.6 for GLM 4.7. Pick GLM 4.7 when output tokens cost 5.1x less, or when what matters is LMArena Text: science or LMArena Text: medicine and healthcare, where it leads.
Which is cheaper, GLM 4.7 or GPT-6.1 Sol?
GLM 4.7 costs $0.54 per million input tokens and $1.98 per million output tokens; GPT-6.1 Sol costs $2.00 and $10.00. Cost per solved task is known for GLM 4.7 only.
Which has the bigger context window, GLM 4.7 or GPT-6.1 Sol?
GPT-6.1 Sol, with 1.05M tokens against 205K.
How many benchmarks is the GLM 4.7 vs GPT-6.1 Sol comparison based on?
46 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 1 independent benchmark and GPT-6.1 Sol's on 2. Where only one model has a result, the page says so instead of naming a winner.