GLM 4.6 vs GPT-5.1-Codex
GPT-5.1-Codex leads GLM 4.6 on coding, 15.3 to 8.8 on the Vibe Coding Index, and GLM 4.6 costs 4.5x less per output token ($2.20 vs $10.00 per million). Updated October 22, 2026; 25 benchmarks have a result for both.
GLM 4.6 or GPT-5.1-Codex: which to pick
Pick GPT-5.1-Codex for coding: it scores 15.3 on the Vibe Coding Index against 8.8 for GLM 4.6.
Pick GLM 4.6 for output tokens 4.5x cheaper ($2.20 vs $10.00 per million) and work measured by SWE-bench Verified or LMArena WebDev, where it leads.
- Shared results
- 25
- GLM 4.6 only
- 44
- GPT-5.1-Codex only
- 1
- 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-5.1-Codex
GPT-5.1-Codex scores 15.3 against 8.8.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-5.1-Codex
GPT-5.1-Codex leads, 23.7 against 18.5.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 4.6 has a GPQA Diamond result, 74.5%.
Directional only
Math
AIME
No call
Only GLM 4.6 has a AIME result, 92.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.6 has a MMLU Pro result, 82.2%.
Directional only
Web design
Design Arena: websites
GLM 4.6
GLM 4.6 leads, 1174 against 1161.
Same board, both measured
Long documents
Listed context window
GPT-5.1-Codex
GPT-5.1-Codex takes 400K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.6
GLM 4.6 $0.0017, GPT-5.1-Codex $0.0063 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.1-Codex has the number, $0.892.
Directional only
At a glance
- Coding index
- GLM 4.6 8.8 from 2 benchmarks, GPT-5.1-Codex 15.3 from 2 benchmarks.
- Widest category
- General, 21.0 points apart on average over 2 shared benchmarks.
- Widest single gap
- Terminal-Bench: GLM 4.6 24.5% against GPT-5.1-Codex 57.8%, 33.3 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6 $2.20 against GPT-5.1-Codex $10.00 per million output tokens, 4.5x apart.
- Context window
- GLM 4.6 takes 205K tokens, GPT-5.1-Codex 400K.
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 4.6 | Bars | GPT-5.1-Codex | Ties |
|---|---|---|---|---|
| Coding | 3 | 6 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 3 | ||
| Reasoning | 0 | 2 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 1 | ||
| Web design | 3 | 2 | ||
| General | 0 | 2 | ||
| All shared boards | 6 | 19 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index36
| Benchmark | GLM 4.6 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| SWE-bench Verified | 68.2% | 66.0% | GLM 4.6 |
| LMArena WebDev | 1341 | 1337 | GLM 4.6 |
| AA Coding Index | 45.8 | Not measured | Only GLM 4.6 measured |
| LiveCodeBench (Vals AI) | 81.0% | 85.5% | GPT-5.1-Codex |
| Vibe Code Bench (Vals AI) | 3.1% | 13.1% | GPT-5.1-Codex |
| LiveCodeBench (Artificial Analysis run) | 69.5% | 84.9% | GPT-5.1-Codex |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | 34.8% | GPT-5.1-Codex |
| Terminal-Bench | 24.5% | 57.8% | GPT-5.1-Codex |
| ALE-Bench | 340.8 | 1244.9 | GPT-5.1-Codex |
| LMArena Text: coding | 1449 | Not measured | Only GLM 4.6 measured |
| LMArena Text: software and IT | 1455 | Not measured | Only GLM 4.6 measured |
| LMArena WebDev: HTML | 1350 | 1346 | GLM 4.6 |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.6 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.5 | 23.7 | GPT-5.1-Codex |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GLM 4.6 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1018 | Only GPT-5.1-Codex measured |
| Design Arena: agents building full-stack apps | 1029 | 1086 | GPT-5.1-Codex |
| Design Arena: agents building mobile apps | 1096 | 1191 | GPT-5.1-Codex |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Godot games | 1180 | Not measured | Only GLM 4.6 measured |
| τ²-Bench Telecom | 76.9% | 83.0% | GPT-5.1-Codex |
| Berkeley Function Calling Leaderboard (BFCL) | 72.4% | Not measured | Only GLM 4.6 measured |
| BrowseComp-Plus | 48.2% | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.6 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| GPQA Diamond | 74.5% | Not measured | Only GLM 4.6 measured |
| GPQA Diamond (Artificial Analysis run) | 78.0% | 86.0% | GPT-5.1-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 14.5% | 25.7% | GPT-5.1-Codex |
| LMArena Text: hard prompts | 1440 | Not measured | Only GLM 4.6 measured |
| LMArena Text: expert | 1432 | Not measured | Only GLM 4.6 measured |
| LMArena Text: hard prompts in English | 1445 | Not measured | Only GLM 4.6 measured |
| Kagi LLM Benchmark | 45.7% | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.6 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| AIME | 92.7% | Not measured | Only GLM 4.6 measured |
| MGSM (Vals AI) | 89.8% | Not measured | Only GLM 4.6 measured |
| AA Math Index | 86.0 | 95.7 | GPT-5.1-Codex |
| AIME 2025 (Artificial Analysis run) | 86.0% | 95.7% | GPT-5.1-Codex |
| LMArena Text: math | 1432 | Not measured | Only GLM 4.6 measured |
| LMArena Text: mathematical work | 1447 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.6 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| MMLU Pro | 82.2% | Not measured | Only GLM 4.6 measured |
| LegalBench (Vals AI) | 79.6% | Not measured | Only GLM 4.6 measured |
| CorpFin (Vals AI) | 56.8% | Not measured | Only GLM 4.6 measured |
| TaxEval (Vals AI) | 66.2% | Not measured | Only GLM 4.6 measured |
| MedQA (Vals AI) | 92.2% | Not measured | Only GLM 4.6 measured |
| MMLU Pro (Artificial Analysis run) | 82.9% | 86.0% | GPT-5.1-Codex |
| LMArena Text: science | 1456 | Not measured | Only GLM 4.6 measured |
| LMArena Text: medicine and healthcare | 1455 | Not measured | Only GLM 4.6 measured |
| LMArena Text: legal and government | 1448 | Not measured | Only GLM 4.6 measured |
| LMArena Text: business and finance | 1416 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index32
| Benchmark | GLM 4.6 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | 1174 | 1161 | GLM 4.6 |
| Design Arena: UI components | 1145 | 1234 | GPT-5.1-Codex |
| Design Arena: game dev | 1150 | 1137 | GLM 4.6 |
| Design Arena: data viz | 1161 | 1213 | GPT-5.1-Codex |
| Design Arena: 3D | 1128 | Not measured | Only GLM 4.6 measured |
| Design Arena: all code categories | 1165 | 1152 | GLM 4.6 |
| Design Arena: SVG | 1108 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.6 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| IFBench | 43.4% | 70.0% | GPT-5.1-Codex |
| Long context reasoning (AA-LCR) | 54.0% | 69.3% | GPT-5.1-Codex |
| LMArena Text | 1440 | Not measured | Only GLM 4.6 measured |
| LMArena Text: instruction following | 1410 | Not measured | Only GLM 4.6 measured |
| LMArena Text: creative writing | 1412 | Not measured | Only GLM 4.6 measured |
| LMArena Text: English | 1449 | Not measured | Only GLM 4.6 measured |
| LMArena Text: non-English | 1426 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Chinese | 1499 | Not measured | Only GLM 4.6 measured |
| LMArena Text: German | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Japanese | 1393 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Korean | 1400 | Not measured | Only GLM 4.6 measured |
| LMArena Text: French | 1459 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Spanish | 1436 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Russian | 1418 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Polish | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: multi-turn | 1427 | Not measured | Only GLM 4.6 measured |
| LMArena Text: longer queries | 1422 | Not measured | Only GLM 4.6 measured |
| LMArena Text: writing and language | 1415 | Not measured | Only GLM 4.6 measured |
| LMArena Text: entertainment and media | 1409 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6 | GPT-5.1-Codex | 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 4.6 costs less
Repository review
50K input + 3K output tokens
GLM 4.6 costs less
Agent loop
200K input + 10K output tokens
GLM 4.6 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 4.6 | OpenAI GPT-5.1-Codex |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | September 30, 2025 | November 13, 2025 |
| Context window | 205K | 400K |
| Input $/Mtok | $0.60 | $1.25 |
| Output $/Mtok | $2.20 | $10.00 |
| Coding index | 8.8 | 15.3 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | $0.892 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6 better than GPT-5.1-Codex for coding?
Pick GPT-5.1-Codex for coding: it scores 15.3 on the Vibe Coding Index against 8.8 for GLM 4.6. Pick GLM 4.6 for output tokens 4.5x cheaper ($2.20 vs $10.00 per million) and work measured by SWE-bench Verified or LMArena WebDev, where it leads.
Which is cheaper, GLM 4.6 or GPT-5.1-Codex?
GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; GPT-5.1-Codex costs $1.25 and $10.00. Cost per solved task is known for GPT-5.1-Codex only.
Which has the bigger context window, GLM 4.6 or GPT-5.1-Codex?
GPT-5.1-Codex, with 400K tokens against 205K.
How many benchmarks is the GLM 4.6 vs GPT-5.1-Codex comparison based on?
25 benchmarks have a result for both models as of October 22, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and GPT-5.1-Codex's on 2. Where only one model has a result, the page says so instead of naming a winner.