GLM 4.5 vs GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini leads GLM 4.5 on the Artificial Analysis Intelligence Index, 20.4 to 12.8, at about the same output price ($2.00 vs $2.20 per million). Updated October 22, 2026; 18 benchmarks have a result for both.
GLM 4.5 or GPT-5.1-Codex-Mini: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.5 for work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 18
- GLM 4.5 only
- 43
- GPT-5.1-Codex-Mini only
- 4
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini leads, 20.4 against 12.8.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 4.5 has a GPQA Diamond result, 72.2%.
Directional only
Math
AIME
No call
Only GLM 4.5 has a AIME result, 86.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.5 has a MMLU Pro result, 81.2%.
Directional only
Web design
Design Arena: websites
GLM 4.5
GLM 4.5 leads, 1169 against 1111.
Same board, both measured
Long documents
Listed context window
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.1-Codex-Mini
GLM 4.5 $0.0017, GPT-5.1-Codex-Mini $0.0013 per turn.
List prices
At a glance
- Coding index
- GLM 4.5 not benchmarked, GPT-5.1-Codex-Mini not benchmarked.
- Widest category
- Agentic work, 19.9 points apart on average over 1 shared benchmark.
- Widest single gap
- IFBench: GLM 4.5 44.1% against GPT-5.1-Codex-Mini 67.9%, 23.8 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 $2.20 against GPT-5.1-Codex-Mini $2.00 per million output tokens, 1.1x apart.
- Context window
- GLM 4.5 takes 131K tokens, GPT-5.1-Codex-Mini 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 4.5 | Bars | GPT-5.1-Codex-Mini | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 0 | ||
| Web design | 7 | 0 | ||
| General | 0 | 2 | ||
| All shared boards | 8 | 10 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GLM 4.5 | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| SWE-bench Verified | 64.2% | Not measured | Only GLM 4.5 measured |
| LMArena WebDev | Not measured | 1244 | Only GPT-5.1-Codex-Mini measured |
| LiveCodeBench (Vals AI) | 67.5% | Not measured | Only GLM 4.5 measured |
| LiveCodeBench (Artificial Analysis run) | 73.8% | 83.6% | GPT-5.1-Codex-Mini |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | 33.3% | GPT-5.1-Codex-Mini |
| Terminal-Bench | Not measured | 61.6% | Only GPT-5.1-Codex-Mini measured |
| WeirdML | 40.6% | Not measured | Only GLM 4.5 measured |
| ALE-Bench | 344.8 | Not measured | Only GLM 4.5 measured |
| LMArena Text: coding | 1434 | Not measured | Only GLM 4.5 measured |
| LMArena Text: software and IT | 1440 | Not measured | Only GLM 4.5 measured |
| LMArena WebDev: HTML | Not measured | 1253 | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.8 | 20.4 | GPT-5.1-Codex-Mini |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 43.0% | 62.9% | GPT-5.1-Codex-Mini |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.5 | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| GPQA Diamond | 72.2% | Not measured | Only GLM 4.5 measured |
| GPQA Diamond (Artificial Analysis run) | 78.2% | 81.3% | GPT-5.1-Codex-Mini |
| Humanity's Last Exam (Artificial Analysis run) | 13.0% | 18.5% | GPT-5.1-Codex-Mini |
| LMArena Text: hard prompts | 1429 | Not measured | Only GLM 4.5 measured |
| LMArena Text: expert | 1433 | Not measured | Only GLM 4.5 measured |
| LMArena Text: hard prompts in English | 1429 | Not measured | Only GLM 4.5 measured |
| Kagi LLM Benchmark | 57.9% | Not measured | Only GLM 4.5 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 4.5 | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| AIME | 86.7% | Not measured | Only GLM 4.5 measured |
| MATH 500 (Vals AI) | 94.0% | Not measured | Only GLM 4.5 measured |
| MGSM (Vals AI) | 90.8% | Not measured | Only GLM 4.5 measured |
| AA Math Index | 73.7 | 91.7 | GPT-5.1-Codex-Mini |
| AIME 2025 (Artificial Analysis run) | 73.7% | 91.7% | GPT-5.1-Codex-Mini |
| LMArena Text: math | 1427 | Not measured | Only GLM 4.5 measured |
| LMArena Text: mathematical work | 1430 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.5 | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| MMLU Pro | 81.2% | Not measured | Only GLM 4.5 measured |
| LegalBench (Vals AI) | 75.6% | Not measured | Only GLM 4.5 measured |
| CorpFin (Vals AI) | 61.0% | Not measured | Only GLM 4.5 measured |
| TaxEval (Vals AI) | 72.4% | Not measured | Only GLM 4.5 measured |
| MedQA (Vals AI) | 90.0% | Not measured | Only GLM 4.5 measured |
| MMLU Pro (Artificial Analysis run) | 83.5% | 82.0% | GLM 4.5 |
| LMArena Text: science | 1444 | Not measured | Only GLM 4.5 measured |
| LMArena Text: medicine and healthcare | 1455 | Not measured | Only GLM 4.5 measured |
| LMArena Text: legal and government | 1438 | Not measured | Only GLM 4.5 measured |
| LMArena Text: business and finance | 1420 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GLM 4.5 | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| Design Arena: websites | 1169 | 1111 | GLM 4.5 |
| Design Arena: UI components | 1141 | 1078 | GLM 4.5 |
| Design Arena: game dev | 1144 | 1092 | GLM 4.5 |
| Design Arena: data viz | 1159 | 1097 | GLM 4.5 |
| Design Arena: 3D | 1170 | 988 | GLM 4.5 |
| Design Arena: all code categories | 1165 | 1093 | GLM 4.5 |
| Design Arena: SVG | 1096 | 978 | GLM 4.5 |
| Design Arena: ASCII art | Not measured | 1105 | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.5 | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| IFBench | 44.1% | 67.9% | GPT-5.1-Codex-Mini |
| Long context reasoning (AA-LCR) | 52.7% | 66.7% | GPT-5.1-Codex-Mini |
| Creative writing (Lech Mazur) | 73.4% | Not measured | Only GLM 4.5 measured |
| LMArena Text | 1430 | Not measured | Only GLM 4.5 measured |
| LMArena Text: instruction following | 1404 | Not measured | Only GLM 4.5 measured |
| LMArena Text: creative writing | 1395 | Not measured | Only GLM 4.5 measured |
| LMArena Text: English | 1437 | Not measured | Only GLM 4.5 measured |
| LMArena Text: non-English | 1417 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Chinese | 1465 | Not measured | Only GLM 4.5 measured |
| LMArena Text: German | 1407 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Japanese | 1415 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Korean | 1380 | Not measured | Only GLM 4.5 measured |
| LMArena Text: French | 1418 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Spanish | 1454 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Russian | 1413 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Polish | 1422 | Not measured | Only GLM 4.5 measured |
| LMArena Text: multi-turn | 1415 | Not measured | Only GLM 4.5 measured |
| LMArena Text: longer queries | 1412 | Not measured | Only GLM 4.5 measured |
| LMArena Text: writing and language | 1403 | Not measured | Only GLM 4.5 measured |
| LMArena Text: entertainment and media | 1404 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5 | GPT-5.1-Codex-Mini | 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-5.1-Codex-Mini costs less
Repository review
50K input + 3K output tokens
GPT-5.1-Codex-Mini costs less
Agent loop
200K input + 10K output tokens
GPT-5.1-Codex-Mini 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 4.5 | OpenAI GPT-5.1-Codex-Mini |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | July 25, 2025 | November 13, 2025 |
| Context window | 131K | 400K |
| Input $/Mtok | $0.60 | $0.25 |
| Output $/Mtok | $2.20 | $2.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5 better than GPT-5.1-Codex-Mini for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5 for work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, GLM 4.5 or GPT-5.1-Codex-Mini?
GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; GPT-5.1-Codex-Mini costs $0.25 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.5 or GPT-5.1-Codex-Mini?
GPT-5.1-Codex-Mini, with 400K tokens against 131K.
How many benchmarks is the GLM 4.5 vs GPT-5.1-Codex-Mini comparison based on?
18 benchmarks have a result for both models as of October 22, 2026. GLM 4.5's coding index rests on 0 independent benchmarks and GPT-5.1-Codex-Mini's on 1. Where only one model has a result, the page says so instead of naming a winner.